Capability and robustness of novel hybridized models used for drought hazard modeling in southeast Queensland, Australia

Journal Pre-proofs Capability and robustness of novel hybridized models used for drought hazard modeling in southeast Queensland, Australia Omid Rahmati, Mahdi Panahi, Zahra Kalantari, Elinaz Soltani, Fatemeh Falah, Kavina S. Dayal, Farnoush Mohammadi, Ravinesh C. Deo, John Tiefenbacher, Dieu Tien Bui PII: DOI: Reference:

S0048-9697(19)34647-9 https://doi.org/10.1016/j.scitotenv.2019.134656 STOTEN 134656

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Science of the Total Environment

Received Date: Revised Date: Accepted Date:

20 June 2019 23 September 2019 24 September 2019

Please cite this article as: O. Rahmati, M. Panahi, Z. Kalantari, E. Soltani, F. Falah, K.S. Dayal, F. Mohammadi, R.C. Deo, J. Tiefenbacher, D. Tien Bui, Capability and robustness of novel hybridized models used for drought hazard modeling in southeast Queensland, Australia, Science of the Total Environment (2019), doi: https://doi.org/ 10.1016/j.scitotenv.2019.134656

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Capability and robustness of novel hybridized models used for drought hazard modeling in southeast Queensland, Australia Omid Rahmatia,b, Mahdi Panahic,d, Zahra Kalantarie, Elinaz Soltanif, Fatemeh Falahg, Kavina S. Dayalh, Farnoush Mohammadii, Ravinesh C. Deoj, John Tiefenbacherk, Dieu Tien Buil*

a

Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Viet Nam

b

Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Viet Nam

c

Division of Science Education, Kangwon National University, Chuncheon-si, Gangwon-do 24341, Korea

Geoscience Platform Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro Yuseong-gu, Daejeon 34132, Korea d

Stockholm University, Department of Physical Geography and Bolin Centre for Climate Research, SE-106 91 Stockholm, Sweden e

Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran f

Department of Watershed Management, Faculty of Natural Resources and Agriculture, Lorestan University, Lorestan, Iran g

Commonwealth Scientific and Industrial Research Organisation (CSIRO), Sandy Bay 7005, Tasmania, Australia h

Department of Reclamation of Arid and Mountainous Regions, Faculty of Natural Resources, University of Tehran, Karaj, Iran i

j School

of Agricultural, Computational and Environmental Sciences, Centre for Sustainable Agricultural Systems & Centre for Applied Climate Sciences, Institute of Life Sciences and the Environment, University of Southern Queensland, Springfield, QLD 4300, Australia k Department l

of Geography, Texas State University, San Marcos, TX 78666, USA

Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam

* Corresponding author’s Email address: [email protected]

Capability and robustness of novel hybridized models used for drought hazard modeling in southeast Queensland, Australia

Abstract Widespread detrimental and long-lasting droughts are having catastrophic impacts around the globe. Researchers, organizations, and policy makers need to work together to obtain precise information, enabling timely and accurate decision making to mitigate drought impacts. In this study, a spatial modeling approach based on an adaptive neuro-fuzzy inference system (ANFIS) and several metaheuristic optimizations (ANFIS-BA, ANFIS-GA,

ANFIS-ICA, ANFIS-PSO) was developed to predict the spatial occurrence of drought in a region in southeastern Queensland, Australia. In this approach, data describing the distribution of eight drought-contributing factors were prepared for input into the models to serve as independent variables. Relative departures of rainfall (RDR) and relative departures of soil moisture (RDSM) were analyzed to identify locations where drought conditions have occurred. The set of locations in the study area identified as having experienced drought conditions was randomly divided into two groups, 70% were used for training and 30% for validation. The models employed these data to generate maps that predict the locations that would be expected to experience drought. The prediction accuracy of the model-produced drought maps was scrutinized with two evaluation metrics: area under the receiver operating characteristic curve (AUC) and root mean square error (RMSE). The results demonstrate that the hybridized models (ANFIS-BA (AUCmean=83.7%, RMSEmean=0.236), ANFIS-GA (AUCmean=81.62%, RMSEmean=0.247), ANFIS-ICA (AUCmean=82.12%, RMSEmean=0.247), and ANFIS-PSO (AUCmean=81.42%, RMSEmean=0.255)) yield better predictive performance than the standalone ANFIS model (AUCmean=71.8%, RMSEmean=0.344). Furthermore, sensitivity analyses indicated that plant-available water capacity, the percentage of soil comprised of sand, and mean annual precipitation were the most important predictors of drought hazard. The versatility of the new approach for spatial drought modeling and the capacity of ANFIS model hybridization to improve model performance suggests great potential to assist decision makers in their formulations of drought risk, recovery, and response management, and in the development of contingency plans. Keywords: Drought Hazard; Spatial modeling; Hybrid model; GIS; Australia

1. Introduction Drought is Earth’s most widespread hazard (Mastrangelo et al., 2012). Whether naturally occurring or a product of human-induced climate change, drought adversely effects various environments and social systems (IPCC, 2013; Wang et al., 2018; Di Baldassarre et el., 2017). Drought arises from abnormal temporal or spatial reductions of precipitation, abnormal rates of evaporation, or increased extraction of water resources by people (Huang and Chou, 2008; Huang et al., 2015). The spatial extent of drought is often greater than any other natural event,

and thus the corresponding damages attributable to a drought event can be expected to be large (Mishra and Singh, 2010, 2011; Xu et al., 2014). Therefore, drought monitoring and prediction have attracted the attention of policy makers to acquire greater insight or improve response to drought events (Luo and Wood, 2007). Droughts are often classified as either meteorological, agricultural, hydrological, or socioeconomic events (American Meteorological Society, 2004). A reduction in precipitation results in meteorological drought. This is generally defined as occurrence of lower than normal precipitation amounts over a given period. Meteorological drought affects soil moisture and leads to agricultural drought, a shortage of water available for plant growth. Hydrological drought refers to deficiencies of surface and/or subsurface water supplies. And socioeconomic droughts occur when there is insufficient supply to meet human water demands. It is often difficult to separate these droughts from each other, as they may occur sequentially or simultaneously and are often interconnected (Mo, 2008; Mo and Lettenmaier, 2014; Hao and Singh, 2015). From a drought management viewpoint, it is necessary to define and understand the terms ‘drought hazard’, ‘drought vulnerability’, and ‘drought risk’. IPCC (2012) defines a hazard as “the potential occurrence of a natural or human-induced physical event that may cause loss of life, injury, or other health impacts, as well as damage and loss to property, infrastructure, livelihoods, service provision, and environmental resources”. Drought vulnerability pertains to the characteristics of a place or system that make it susceptible to suffering the consequences of drought (Naumann et al., 2014). Drought vulnerability includes both biophysical and socio-economic drivers of drought impacts and requires a determination of the degree of susceptibility to a drought hazard and also the capacity to cope with drought (Biazin and Sterk, 2013). Risk is “the likelihood over a specified time period of severe alterations in normal functioning of a community or a society due to hazardous physical

events interacting with vulnerable social conditions, leading to widespread adverse human, material, economic, or environmental effects that require immediate emergency response to satisfy critical human needs and that may require external support for recovery” (IPCC, 2012). Therefore, drought risk is a function of drought vulnerability to hazardous conditions and exposure to a drought hazard (Wilhite, 2000). As a result of the insidious nature of drought events, numerous studies have assessed drought hazard around the world, mostly using time series-based drought indices to monitor and evaluate meteorological drought characteristics (e.g., Mishra and Singh, 2010; Dayal et al., 2017; Deo et al., 2017a). In a comprehensive review, Mishra and Singh (2011) describe drought modeling approaches that range from simplistic to more complex models, and they discuss their corresponding advantages and limitations. Drought risk has also been assessed using statistical multivariate joint distribution in a time series approach, e.g., using copula (Zhang et al., 2013; Nguyen-Huy et al., 2017; Ali et al., 2018). A number of drought indices have been applied in different studies to characterize and predict different types of drought. The most commonly used of these drought indices are the standardized precipitation index (SPI) (e.g., McKee et al., 1993; Edwards, 1997; Uddameri et al., 2019), the soil moisture drought index (SMDI) (e.g., Hollinger et al., 1993; Sohrabi et al., 2015; Carrão et al., 2016), the soil moisture deficit index (SMDI) (e.g., Narasimhan and Srinivasan, 2005; Yang et al., 2017), the soil wetness deficit index (SWDI) (e.g., Keshavarz et al., 2014), the standardized runoff index (SRI) (e.g., Shukla and Wood, 2008), the standardized precipitation evapotranspiration index (SPEI) (Vicente-Serrano et al., 2010), the linearly combined drought index (LDI) (Mo and Lettenmaier, 2014; Hao et al., 2016), the aggregated drought index (ADI) (Keyantash and Dracup, 2004), and the multivariate standardized drought index (MSDI) (Hao and AghaKouchak, 2013). This approach uses drought indices based on interpolation techniques for generalizing weather station data (McLeman et al., 2010).

However, such indices are station-based and computed on a point-to-point scale, and they are thus not representative of the spatial variations of droughts. Consequently, they generate relatively high uncertainty in interpolated areas (Brown et al., 2008; Swain et al., 2011). This circumstance has yielded combined applications of remote sensing techniques and drought indices for drought monitoring. Rhee et al. (2010) proposed a new remote sensing-based scaled drought condition index (SDCI) for agricultural drought monitoring. This index uses multi-sensor data comprising normalized difference vegetation index (NDVI) data and land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, and precipitation data from the Tropical Rainfall Measuring Mission (TRMM) satellite. Gouveia et al. (2017) used monthly NDVI and SPEI data at different time scales (1-24 months) to analyze drought impacts on vegetative cover in the Mediterranean region. Nicolai-Shaw et al. (2017) applied satellite-derived soil moisture observations to quantify the relationships between soil moisture drought and evapotranspiration, precipitation, temperature, and vegetation, and demonstrated the usefulness of satellite-based soil moisture for drought assessment. However, there is limited information in the remotely sensed soil moisture signal of the deeper root zone which produces inaccurate predictions of drought conditions. Mariano et al. (2018) investigated biomass anomalies using leaf area index (LAI) and MODIS data to detect trends in drought and land degradation in Brazil, and concluded that there are advantages in and great prospects for remote sensing-based approaches. However, there are also some major challenges as regards unquantified uncertainty, data continuity, community acceptability, sensor changes, and short-period data series (e.g., a decade, which is insufficient for meaningful studies of droughts from a climatological perspective) (AghaKouchak et al., 2015; Liu et al., 2016). In another approach, researchers have used a multi-criteria decision analysis (MCDA) method (e.g., analytical heirarcy process (AHP)) to analyze the relationships between predictor variables, which is based on a

questionnaire/survey and experts’ opinions (e.g., Chen and Yang, 2011; Palchaudhuri and Biswas, 2016). A major drawback is that the AHP method allows subjective judgments by decision-makers. Machine learning (ML) algorithms have been attracting attention as methods to extract knowledge in different fields involving natural processes (Rahmati et al., 2019a; Tien Bui et al., 2019; Moayedi at al., 2019). ML models have valuable advantages which can overcome the above-mentioned limitations. According to Pal and Mather (2005), state-of-the-art ML procedures, if implemented correctly, have higher accuracy than conventional parametric approaches. Machine learning approaches have the capacity to effectively model non-linear and highly dimensional data with intricate connections and missing values (Recknagel, 2000; Knudby et al., 2010). As a result, ML models have been successfully employed in environmental and natural hazard studies, such as mapping areas susceptible to land subsidence (e.g., Bui et al., 2018a; Rahmati et al., 2019b), landslides (e.g., Trigila et al., 2013; Goetz et al., 2015; Pham et al., 2016), gully erosion (e.g., Pourghasemi et al., 2017; Garosi et al., 2019), and floods (e.g., Darabi et al., 2019). A ML application on time series-based predictors has been used to make drought predictions (e.g., Deo et al., 2017b, c; Prasad et al., 2017; Ali et al., 2018; Dayal et al., 2018). In these models, drought hazard is the dependent variable, while climatological and environmental factors are independent variables. ML algorithms make it possible to analyze complicated and non-linear relationships between drought events and other drought-affecting factors (environmental, topographical, hydrological, etc.). Farokhnia et al. (2011) used sea surface temperature (SST) and sea level pressure (SLP) as inputs to an adaptive neurofuzzy inference system (ANFIS) model to forecast possible droughts in Tehran three, six, and nine months in advance. They assessed the performance of the SST/SLP data sets and the ANFIS model according to a “drought/wet” classification, and concluded that in more than 90% of cases, the ANFIS model detected the

drought status correctly or with only a one-class error (Farokhnia et al., 2011). Therefore, the efficiency and feasibility of this approach has been confirmed in the literature and our study is not intended to evaluate this approach (Deo et al., 2017a, b; Prasad et al., 2017). Instead, our study fills a research gap in the ML-based approach for modeling drought hazard used in previous studies that used data from only a few locations (i.e., weather station records) and often ignored the variations in topo-hydrological and the physical properties of the soils in a given study area. The ANFIS algorithm is a type of neurofuzzy approach that uses an adaptive network for learning and is popular for forecasting time series in water resources management and hydrological modeling (Aqil et al., 2007; Noori et al., 2010). ANFIS is a powerful model that offers great prospects for solving complicated and non-linear problems (Keshtegar et al., 2018; Zare and Koch, 2018). Its main advantages are fuzziness, its handling of temporally or spatially inadequate data, and the ability of the neural network to analyze complicated relationships between dependent and independent variables (Farokhnia et al., 2011). However, numerous studies have also shown that a standalone ANFIS model is prone to a degree of over-fitting, mainly due to intricacies caused by the adjustments of parameters to achieve global optima (Dehnavi et al., 2015; Jaafari et al., 2019b). Recently, hybridized ANFIS models, in which numerous predictive models can collaborate on the same task, have been used in geo-environmental and hydrological studies (e.g., Moretti et al., 2015; Kalantari et al., 2019a). The main reason for developing these hybridized models is that they assimilate a tuning optimization algorithm with the intelligent predictive ANFIS model. However, no previous study has systematically scrutinized the efficiency of hybridized ANFIS models for spatial drought hazard modeling. Specific objectives of this study are thus to: (1) investigate the utility of the ANFIS model for spatially modeling drought hazard; (2) combine the ANFIS model with each of four popular optimization algorithms (Bee algorithm (BA), imperialistic

competitive algorithm (ICA), genetic algorithm (GA), and particle swarm optimization (PSO)) to generate novel hybridized models; (3) compare the performance of the standalone ANFIS model and the hybridized models using statistical evaluation metrics; and (4) assess the relative importance of the independent predictor variables. The aim is to acquire in-depth insights into drought hazard mapping by analyzing past drought events.

2. Material and methods In the following, we provide a step-by-step account of the methodology used for modeling drought hazard using the different ANFIS-based hybrid algorithms. The method contains five steps (Fig. 1): 1) Sourcing the data and preparing the maps of the factors that influence drought; 2) generating drought hazard maps of the study area using the ANFIS approach; 3) generating novel hybridized models by integrating the standalone ANFIS model with metahuristic optimization algorithms (BA, GA, ICA, and PSO); 4) evaluating and comparing the performance of the standalone ANFIS and the hybridized ANFIS models; and 5) selecting the optimum ANFIS-based hybrid algorithm for drought hazard modeling. Fig. 1 HERE 2.1. Study area To test the newly developed hybridized ANFIS models for spatially representative drought risk modeling, a drought-stricken region of 123,897 km2 in southeast Queensland (26°10′29°02′S, 147°00′-153°00′E), eastern Australia was selected for case study (Fig. 2). Australia is, generally speaking, the driest inhabited continent on Earth, and its climates are relatively harsh and extreme, particularly in terms of both floods and droughts. An analysis of historical rainfall records reveals regular drought cycles within eastern Australia, that occasionally persist for a decade or more. For example, the ‘Millennium Drought’ lasted from 1996 to

2010, with a number of years showing below-average rainfall (Sohn, 2007; Murphy and Timbal, 2008). Queensland was selected for the case study because it is Australia’s second largest state by area and the third largest by population, and often faces weather extremes like floods, droughts, heatwaves, and bushfires. The incidence and severity of heatwaves and bushfires are amplified by drought conditions, but climate change and large-scale climate variability are likely to modify historical cycles, change frequencies, and magnify severity of all of these events, as they may be increasingly affected by changes in temperature and rainfall regimes, and in extreme weather conditions (DEHP, 2016). Fig. 2 HERE 2.2. Methodology 2.2.1. Drought event inventories The main focus of this study is on drought events in Queensland in 2009 and 2013, which are benchmarks for major droughts across Australia during the period 1961-2013 (e.g., Dayal et al., 2018). As discussed by Mo (2008), it is difficult to clearly distinguish drought types accurately, as they may occur simultaneously or sequentially in the same region and are generally interconnected. Thus, according to Hao and Singh (2015), a single drought indicator may not sufficiently reflect the extant drought situation of an area due to drought’s complex nature. In this study, we examined both annual rainfall and soil moisture in 2009 and 2013 (i.e., the driest years in recent decades) to identify drought events, since these factors are the most common conditions used for drought monitoring (Tallaksen et al., 2009). Soil moisture content in the upper (from 0 to 0.2 m depth) and lower (from 0.2 to 1.5 m depth) soil layers were considered. Soil moisture data at 100 m resolution for the study region were obtained from Australian Water Availability Project (AWAP), which has created soil moisture maps based on in situ observations and the WaterDyn hydrological model (Raupach et al., 2012).

These data have been used previously by others to study drought and related events in the study area (e.g., Dayal et al., 2017, 2018; Prasad et al., 2018a, 2018b). In this study, relative departure of rainfall (RDR) and relative departure of soil moisture (RDSM), two primary indicators of drought impact, were calculated and used to create an integrated drought inventory. The RDR is a common drought hazard index (Safavi et al., 2014; Jain et al., 2015; Dayal et al., 2018), calculated as: (1) where xi is rainfall for the given year and

is mean annual rainfall over the base period

(Dayal et al., 2018). Currently, there is no distinct temporal threshold between the different types of drought. The lag time between meteorological and agricultural drought is an approximate period that generally ranges between 3 and 6 months according to information tailored for Australia (Mpelasoka et al., 2008; Lobell et al., 2015). In this study, a 3-month lag time between meteorological and agricultural droughts was selected. Similarly to the RDR, the RDSM was calculated as: (2) where si is mean annual soil moisture for the given year and

is mean annual soil moisture

over the base period. Machine learning models require two types of spatial data points to generate a drought hazard map: locations that experiencied drought conditions (also termed presence or positive cases) and locations that had not experienced drought conditions (also known as negative or absence cases) during the study period. Maps of RDR and RDSM were standardized from their original values into a 0-1 scale using a fuzzy logic operation process (Fig. 3). Following Dayal et al. (2018) and Rahmati et al. (2019a), a threshold of 0.5 was then used for both the

standardized RDR (i.e., RDR>0.5) and the RDSM (i.e., RDSM>0.5) to identify drought locations in the study area. The drought inventory data set, which included 257 drought locations, was randomly divided into two groups for training (70%, n=180) and validation (30%, n=77). Non-drought cases were selected randomly and away from drought locations (characterized by RDR<0.5 and RDSM<0.5). In spatial modeling, it is generally recommended that the ratio of positive to negative cases (here drought/non-drought) should be equal to 1 (Schicker and Moon, 2012). Therefore, to complement the drought inventory data set, 257 non-drought pixels were randomly selected from non-drought areas. To investigate the robustness of the models (i.e., model response when the training and validation data sets change), the random partitioning process (i.e., classification of drought and non-drought inventories to the training and validation groups) was run four times, and consequently four sample data sets (D1, D2, D3, and D4) were obtained (Fig. 4). Fig. 3 HERE Fig. 4 HERE 2.2.2. Drought-influencing factors Correct preparation of the thematic data layers and the selection of factors are important tasks for predictive modeling (Crozier, 1986). Although there are no explicit guidelines for selecting the factors (Ayalew et al., 2005), it generally depends on the scale of the analysis and the characteristics of the study area (Glade et al., 2005). A number of factors affect the severity of drought, including geomorphology, precipitation, relief, soil texture, drainage intensity, lithology, depth to watertable, aquifer yield, and surface water bodies (Pandey et al., 2012). In this study, eight factors (i.e., predictor variables) were chosen based on availability of data and used in drought hazard assessment. This set of factors was mapped and they included elevation (Fig. 5a), slope angle in degrees (Fig. 5b), topographical wetness index

(TWI) (Fig. 5c), soil depth (Fig. 5d), clay content as a percentage of a soil (Fig. 5e), sand content of soil as a percentage (Fig. 5f), plant-available water capacity (PAWC) (Fig. 5g), and mean annual precipitation (Fig. 5h). Elevation: Interactions between topography and the atmosphere are likely to influence rainfall patterns and vegetation phenology (Anders et al., 2006). At higher elevation, the higher altitude alters climate conditions and this causes differences in soil conditions and vegetation type (Aniya, 1985). Thus the topographical variability associated with crop production is an integrated reflection of soil properties and factors affecting agricultural productivity (Dinaburga et al., 2010). A Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) was used to prepare an elevation map of the study area. Slope degree: In terms of groundwater storage capacity, flat areas with no topography fall into the ‘very good’ category as they typically have relatively high infiltration rate. Areas with moderate slopes are considered ‘good’ for groundwater storage for the slightly undulating topography that produces some run-off (Shekhar and Pandey, 2015). The slope layer for the study area was generated using the DEM layer and slope tool in ArcGIS software. Topographical wetness index (TWI): TWI is controlled by topography and shows the flow direction of groundwater and soil (Rodhe and Seibert, 1999). TWI has been widely used in estimating soil moisture, its spatial distribution (Grabs et al., 2009), and water flow in the subsurface in a given landscape (Kalantari et al., 2017). It can be calculated as follows (Eq. 3): (3) where a is the specific catchment area (m2/m) and β is the slope gradient (in degrees). Digital elevation models (DEM) are perfect tools to enable the extraction of topographical factors

(Pradhan, 2009). The TWI layer for the study area was derived from the DEM in Systems for Automated Geoscientific Analyses (SAGA) software. Soil texture: Texture refers to the proportional combination of sand, silt, and clay in a soil. Sand particles are much larger than silt and clay particles, and soils high in sand have lower water-holding capacity. Clay soils are denser, with smaller particle sizes and air spaces, and therefore have higher water-holding capacity (Abdulkadir, 2016). Sand and clay content maps of the study area (created in 2014) were sourced from the Terrestrial Ecosystem Research Network (TERN). The TERN maps were generated by combining Australia-wide digital soil attribute maps, derived using consistent data mining-kriging models, and regional maps for parts of Australia, derived using disaggregation and regression modeling (see http://www.clw.csiro.au/aclep/soilandlandscapegrid/ProductDetails-SoilAttributes.html). Digital maps of sand and clay in the study area are readily available for six depth-intervals: 05 cm, 5-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, and 100-200 cm. Following Dayal et al. (2018), in this study the values for all depth levels were averaged to create the single-value representations in the sand and clay data layers. Soil depth: This is one of the most important factors in surface and subsurface runoff generation and infiltration (Mogaji et al., 2014). Shallow and moderately shallow soils with high porosity and low permeability can retain soil moisture for longer periods during drought than can moderately deep and deep soils, where high internal drainage results in faster loss of soil moisture. Therefore, shallow and moderately shallow soils are comparatively less vulnerable to drought than moderately deep and deep soils (Jothibasu and Anbazhagan, 2017). The soil depth map used in the present study was obtained from TERN, where the kriging interpolation method was used to prepare soil depth maps; kriging has a lower root mean square error (RMSE) than other interpolation methods. For full details of soil depth layer

determination in TERN, see http://www.clw.csiro.au/aclep/soilandlandscapegrid/ProductDetails-SoilAttributes.html. Plant-available water capacity: PAWC, another influential variable, is the difference in water content between field capacity and the permanent wilting point. PAWC is highly dependent on soil porosity which in turn depends on soil texture and structure (Łabędzki, 2017). PAWC data for the study area were obtained from the National Agricultural Monitoring System (NAMS; http://www.nams.gov.au). Precipitation: Precipitation patterns play important roles in the initiation of a drought (Martínez-Fernández et al., 2016). A mean annual precipitation data layer was acquired from the Australian Water Availability Project (AWAP) (http://www.csiro.au/awap/). It was generated by AWAP using the kriging method. The rainfall data were from the Bureau of Meteorology’s network of rain gauges and weather stations. There is great spatial variation in the yearly precipitation totals in the study region (Fig. 5h); the long-term mean annual precipitation (1971-2015) ranged from 436 mm in the western part of the study area to 2261 mm in the eastern part. Fig. 5 HERE 2.2.3. Step-wise assessment ratio analysis (SWARA) Step-wise assessment ratio analysis (SWARA), a multi-criteria decision-making method (MCDM) characterized by its ease of application and a design based on expert opinion, has attracted the attention of researchers in diverse fields, especially geology (Keršuliene et al., 2010). In SWARA, the highest and lowest rank, for the most and least valuable criterion respectively, are determined by expert choice. There are four steps in the SWARA method (see Fig. S1):

Step 1: To develop decision-making models within a practical scenario, experts have to ascertain the criteria associated with the model. The priority of each factor determined by each expert depends upon the experience and knowledge of that expert. They arranged the factors from most to least important. Step 2: Based on the rank of each parameter, the following steps are taken to calculate the weight of each factor. First, the relative importance of each criterion is determined relative to the previous most important criterion, i.e., the comparative importance of the average value (Sj) is calculated as: (4) where n represents the number of experts, Ai is the rank proposed by the experts for each factor, and j is the number of factors. The coefficient Kj, a function of the relative importance of each factor, is then defined as: (5) Step 3: The initial weight (Qj) of each factor is calculated using equation (6), where the weight of the first factor, which is the most important, is set to 1: (6) Step 4: The final normalized weight of the criterion is calculated as (7): (7) where Wj is the relative weight of the jth criterion and m is the total number of criteria. After calculating the weights of the classes using SWARA, they were used to train the standalone ANFIS and hybridized models. Hybridization of SWARA and ANFIS has been described by others (e.g., Dehnavi et al., 2015; Hong et al., 2018; Jaafari et al., 2019a). 2.2.4. Application of models

- Adaptive neuro fuzzy inference system (ANFIS) A fuzzy logic approach that can mimic human-like decision making is gaining significance and interest among many researchers. However, this method alone may not yield desirable results when used in unforeseen situations (Dehnavi et al., 2015; Chen et al., 2017). To overcome this shortcoming, artificial neural networks can be integrated with a fuzzy logic model to generate an ANFIS model (Jang, 1993). The ANFIS model can produce a more sophisticated model structure that is more useful for non-linear problem solving than are fuzzy interference systems (FIS) (Chen et al., 2017). The FIS approach uses a multilayer feedforward network for training. ANFIS can train FIS membership function (MF) parameters by applying input training data in the back-propagation, gradient descent, and least-squares methods. Many recent studies have used fuzzy inference styles in ANFIS, with the best known and most commonly used being the Sugeno and Mamdni styles. These styles were also used in the present work (Fig. S2 in Supporting Information). Sugeno FIS includes two inputs, two MFs for each input, and two rules. The Sugeno fuzzy style is developed using two if-then rules: Rule 1: if (x is A1) and (y is B1) then (f1=p1x+q1y+r1)

(8)

Rule 2: if (x is A2) and (y is B2) then (f2=p2x+q2y+r2)

(9)

where x and y are inputs, A and B are fuzzy sets, f is output, and p, q, and r are parameters defined by the artificial neural network. The typical architecture of ANFIS consists of five layers (Fig. S2). A complete description of ANFIS can be found in Jang (1993) and Jang and Sun (1995). - Bee algorithm (BA) The Bee algorithm is a kind of heuristic algorithm (Pham et al., 2006). It is inspired by the foraging behavior of a set of honeybees looking for food sources near their hive. First, scout bees are randomly spread over different regions to identify flower patches. These bees return

to the hive and perform the waggle dance, communicating information about flower patches they have discovered. This information covers flight direction, distance to the hive, and size of flower patches, so the honeybee colony can make an appropriate assessment of all flower patches. The scout bees, along with other bees from the colony, recruit bees, then return to the flower patches. Based on the distance and the amount of each flower patch, different numbers of recruit bees are allocated to each (i.e., larger flower patches attract more recruit bees). The recruit bees continue to assess the quality of flower patches when harvesting nectar and, if it decreases, they abandon that patch. However, if the quality is adequate, then in the next waggle dance other bees are recruited. Before implementing the Bee algorithm, parameters must be defined: number of scout bees (n), number of patches selected out of n visited (m), number of best patches out of m selected patches (e), number of bees recruited for e best patches (nep), number of bees recruited for the other (m-e) selected patches (nsp), patch size (ngh), and the stopping criterion (Bui et al., 2018b). Initially, n scout bees are randomly distributed in the search space. The algorithm assesses the fitness of each point noticed by the scout bees and identifies the best scout bees (elite bees). The sites found by the elite bees are selected for a neighborhood search. Neighborhood searches are executed in these locations by the e best bees. For each site, the algorithm chooses only the most suitable bee to remain in the next bee population, while the remaining bees are randomly allocated around the search space to evaluate new possible solutions. These steps continue until the algorithm reaches convergence (a flowchart of this algorithm is shown in Fig. S3 in Supporting Information). - Genetic algorithm (GA) The genetic algorithm (GA) is a search heuristic algorithm based on natural evolution and Darwin's theory of evolution. This algorithm was introduced by Holland in 1960 and its development continued through the 1960-1970s (Goldberg and Holland, 1988; Holland,

1992). It is among the oldest, most popular, most often used evolutionary algorithms, and it has been applied by researchers in diverse disciplines to optimize complicated problems (Hong et al., 2018; Jaafari et al., 2019a). The structure of GA, like other evolutionary algorithms, considers a population where its component is introduced as a solution to the problem. This component, called chromosome, consists of a set of problem variables that play the role of a gene in this algorithm (Fig. S4 in Supporting Information). In GA, the search procedure is launched by generating a population of chromosomes, which is usually created randomly. Moreover, subsequent generations of this population are constructed by three operators: i) The selection operator: By calculating the fitness function of each chromosome, the best chromosomes in the community, also the best problem solutions, are identified and used as parents for offspring chromosomes and the next generation. ii) The crossover operator: Two chromosomes, considered parents, are used to generate offspring chromosomes. In fact, this operator determines the means and the ratio of the gene selection of an offspring’s chromosomes from its parents. There are various methods to produce this operator: ranking selection, n-point, cycle, order, uniform, tournament, and partially mapped. The uniform method is used here (Fig. S5 in Supporting Information). iii) The mutation operator: This is used to seek new areas of the search space and prevents the optimal localization from being accepted as the best solution. To achieve this, it is adequate to randomly alert some of the genes in chromosomes (Fig. S6 in Supporting Information). Offspring created by these three operators are employed as the next-generation parents, and the process continues until the termination terms and conditions are met. - Imperialistic competitive algorithm (ICA)

Inspired by social-political relations in imperialistic competition, the imperialistic competitive algorithm (ICA) is another recent evolutionary algorithm (Atashpaz-Gargari and Lucas, 2007). It is now used in many optimization problems and is known as a strong metaheuristic evolutionary algorithm (Bui et al., 2018a; Jaafari et al., 2019b). Like the other evolutionary algorithms, ICA includes a set of components, each representing a solution, and by searching in a problem space it tries to find the optimized solution for the problem. It is implemented in seven steps (see also Fig. S7 in Supporting Information): Step 1: Generating the primitive empires. Any of the components of the ICA population is called a country. Given that each country comprises n variables, it is defined as: Country= [P1, P2, …, PN]. According to the cost function devoted to each country, countries with the least cost are imperialists and those with higher costs are colonies. As the cost function of the imperialist has an inverse relationship with its power, using this function and computing the power of each imperialist determines the number of their colonies. The result generates the imperialists. Step 2: Absorption. In ICA, imperialists always capture new colonies to increase their power. This step involves a vector where its value is a random number from the normal distribution and its direction is from colony to imperialist. Step 3: Revolution. In real life, unexpected and quick changes in social and political parameters of countries lead to revolution. To initialize this step in ICA, some colonies are selected at random and their position is altered. If there is any minimum local problem, implementing the revolution step can remove it. Step 4: Exchanging the positions of a colony and an imperialist. Following the previous steps, cost functions of colonies of the imperialists are calculated, which may reveal that one or

more colonies can have a better position than their imperialists. This exchanges the position of the best colony with its imperialist. Step 5: Computing total power of empires. Most of the power of an empire comes from its imperialist, but the sum of its colonies’ power can impact its total power. Therefore, total cost of an empire is obtained as: (10) where T, Cn is the total cost of the nth empire and ξ is a coefficient indicating the extent to which average costs of colonies affect the total costs of the empire and its value is between 0 and 1. The higher the coefficient, the greater the impact of colonies on the empire, and vice versa. Step 6: Competition of the empires. Empires are in constant competition to gain more power. This competition is based on evaluating the power of imperialists. The strongest imperialist acquires the weakest colony of the weakest imperialist and adds it to its set of colonies. This process continues until all of the colonies of the weak imperialists are absorbed by the strongest imperialist and the weakest imperialist remains on its own. Finally, the empire of the now powerless imperialist collapses and it is annexed to the most powerful imperialist as a colony. Step 7: Convergence. Steps 2-6 are repeated incessantly until all the imperialists are defeated by a single imperialist that takes possession of all the colonies of other imperialists and makes them its own colonies. In the end, the most powerful imperialist remains the only imperialist. In some optimization problems, the convergence condition can be the number of iterations or the extent of error. - Particle swarm optimization (PSO)

Particle swarm optimization (PSO) is a meta-heuristic algorithm first developed in 1995 (Poli et al., 2007; Kennedy, 2011). Some of its advantages are quick convergence, shorter computation time, and high suitability for optimizing non-linear problems. These characteristics distinguish PSO from other evolutionary algorithms like GA. PSO has gained popularity amongst researchers for solving optimization problems. Its design is based on the shoaling behavior of fish or the flocking behavior of birds searching the best path for food. Each bird/fish is considered a particle that in fact represents a solution to the problem. The particle searches in a n-dimensional space, where n denotes number of parameters of the problem, and finds the best solution to the given problem. On first implementing the algorithm, particles are randomly scattered in the problem space. During each iteration, they can update their position through finding the optimal answer. Note that this algorithm goes on until the best position found for each particle is the same as the overall best position. In other words, the PSO algorithm ends when all particles are concentrated at the same point and so the solution to the problem is optimized. In this study, the standalone and hybridized ANFIS models were all implemented in Matlab software. Our written codes are freely available at https://github.com/orahmati68/ANFIS. These codes have been used and tested in other studies to predict landslides (Aghdam et al., 2017; Chen et al., 2017), floods (Bui et al., 2018a; Hong et al., 2018), and wildfires (Jaafari et al., 2019b), for example. 2.2.5. Accuracy assessment A validation procedure is conducted to ensure that models perform well for their intended purposes (Robinson, 2014). In the case of hazard investigations, validation involves the delineation of zones with different levels of hazard, which may require different management actions. Prediction accuracy was assessed here using the area under the receiver operating characteristic curve (AUC) and root mean square error (RMSE).

Among a wide variety of validation techniques, the AUC metric is one of the robust methods for model evaluation (Rahmati et al., 2019c). It has been used in several natural hazards studies (Pourghasemi et al., 2012; Falah et al., 2019). The receiver operating characteristic (ROC) curve is a two-dimensional approach, reflecting two kinds of possible events: i) the success rate in detecting signals (y-axis), and ii) the error rate of falsely identifying noise events (x-axis) (Eqs. 11 and 12). Conversely, it plots the rate of true-positive (TP) detection against the corresponding rate of false-positive (FP) error (Maxion and Roberts, 2004). (11) (12) (13) (14) where TN and FN are the number of pixels that are correctly classified and the numbers of pixels erroneously classified, respectively. The AUC value for the ROC curve varies from 0 to 1 and trustable values are considered to be closer to 1. AUC values fall into one of five categories: 0.5-0.6 (poor), 0.6-0.7 (average), 0.7-0.8 (good), 0.8-0.9 (very good), and 0.9-1.0 (excellent) (Bui et al., 2018b). Root mean square error is also often used as a measure of the difference between predicted and observed values (Hyndman and Koehler, 2006). RMSE is calculated as:

(15) where Xest and Xobs are the estimated and observed values, respectively, and n is the number of samples. Low values (values closer to zero) of RMSE are desirable.

2.2.6. Sensitivity analysis Sensitivity analysis is important for calibration, uncertainty analysis, and decision making, and reflects the contribution of factors to drought-hazard modeling (Yang, 2011). Additionally, sensitivity analysis estimates how variations in the output of a model can be partitioned to different sources of variations (i.e., uncertainties), and determines how the given model depends upon the input information (Saltelli et al., 2000). Map removal sensitivity analysis was conducted in this study using the best model to investigate the importance of variables (Rahmati et al., 2016). This method has been used widely in previous spatial modeling studies (e.g., Oh et al., 2011; Saidi et al., 2011; Fenta et al., 2015). A variation index called the relative decrease (RD) was calculated by excluding each factor in several stages: (16) where AUCall denotes the final AUC value from drought hazard prediction using all predictive factors and AUCi is the AUC value when the ith predictive factor has been excluded. Relative ranking of predictive factors was therefore determined using the RD index.

3. Results 3.1. Drought hazard mapping The aim of this study is to improve the accuracy of the ANFIS approach by comparing it to combinations of ANFIS and four different metaheuristic optimization algorithms (Bee, GA, IC, and PSO) for spatial drought prediction. The resulting maps of drought hazard obtained by the application of the standalone ANFIS and hybridized ANFIS-BA, ANFIS-GA, ANFISICA, and ANFIS-PSO models are shown in Fig. 6a-e. For enable interpretation of the results,

all five drought hazard maps generated for the study area had their values classified into five drought classes: very low (0-0.2), low (0.2-0.4), moderate (0.4-0.6), high (0.6-0.8), and very high (0.8-1) (Fig. 7). The maps showed that the majority of the study area has moderate to very high drought hazard. The spatial distribution of low and very low classes tended to be more frequent in the east and center of the study area. In addition, the boundaries between each drought hazard class were more obvious in maps generated by the hybrid models than in those values produced by the standalone ANFIS approach. Fig. 6 HERE Fig. 7 HERE Table 1 HERE 3.2. Accuracy assessment The goodness-of-fit of the models based on the four sample data sets (D1-D4) and two performance evaluation metrics is summarized in Table 1. For the standalone ANFIS model, the AUC ranged from 84.8% to 85.9% (mean=85.42%), and the RMSE ranged from 0.063 to 0.098 (mean=0.078). For ANFIS-BA, AUC varied between 84.5% and 85.6% (mean=84.97%), and RMSE ranged between 0.211 and 0.303 (mean=0.237). For ANFIS-GA, AUC ranged between 82.6% and 83.3% (mean=82.92%) and RMSE between 0.221 and 0.311 (mean=0.246). For ANFIS-ICA, the AUC range was 82.7-83.9% (mean=83.2%), and the RMSE range was 0.219-0.31 (mean=0.244). For ANFIS-PSO, AUC ranged from 82.3% to 83.2% (mean=82.72%), and RMSE from 0.227 to 0.315 (mean=0.251). Therefore, ANFIS had the highest goodness-of-fit based on both the AUC and RMSE metrics, followed by ANFIS-BA, ANFIS-ICA, ANFIS-GA, and ANFIS-PSO. Table 1 HERE The goodness-of-fit merely shows how well the model fits the training data set. The prediction capability of the model cannot be judged by the goodness-of-fit, because it is

measured by the sampled drought locations that were used to calibrate the model (Deo et al., 2017a). Predictive performance was used to evaluate the prediction capacity of the model, because it was determined by comparing the drought hazard map with the drought validation data set (Rahmati et al., 2019a). In the validation step, the results of the standalone and hybridized models were verified using validation data sets and the two different evaluation metrics (Table 2). In all four data sets (D1-D4), the standalone ANFIS model had AUC values between 69.4% and 73.2% (mean=71.8), and RMSE values between 0.332 and 0.355 (mean=0.344). For ANFIS-BA, AUC was 83.4-84.1% (mean=83.7%) and RMSE was 0.2270.242 (mean=0.236). In the case of ANFIS-GA, AUC was 81.2-82.4% (mean=81.62%) and RMSE was 0.236-0.256 (mean=0.247). The ANFIS-ICA model had AUC values of 81.682.7% (mean=82.12%) and RMSE values of 0.233-0.258 (mean=0.247). For ANFIS-PSO, AUC was 80.9-82.1 (mean=81.42%) and RMSE was 0.241-0.267 (mean=0.255). Therefore, both the AUC and RMSE metrics in all four data sets (D1-D4) clearly indicate that ANFISBA outperformed other models. It was followed by ANFIS-ICA, ANFIS-GA, and ANFISPSO. The standalone ANFIS model had the lowest predictive performance in all four data sets, based on both evaluation metrics. Therefore, the predictive performance and generalizability of all four hybridized models were considerably better than those of the standalone ANFIS model. The hybridized models also showed more robustness compared to the standalone ANFIS model, as they had very good predictive performance even when the training and validation data sets changed (i.e., data sets D1-D4). Table 2 HERE 3.3. Sensitivity analysis Sensitivity analysis was performed using the ANFIS-BA model, as it had the highest accuracy of all models tested (Table 3). The higher the RD percentage, the higher the importance of the predictive variable. The results clearly demonstrate that PAWC and distribution of the sand

percentage were the two most important variables for spatially predicting drought hazard; RDs were 16.41% and 11.77%, respectively, after removal of the variable from the drought modeling. Mean annual precipitation (RD=8.68%), distribution of the clay percentage (RD=6.66%), elevation (RD=3.8%), and soil depth (RD=3.21%) were of moderate importance for drought modeling. The slope angle and TWI were the least important factors, with RD values of 1.55% and 1.43%, respectively. Table 3 HERE 4. Discussion 4.1. Predictive performance of models In this study, four hybridized models (ANFIS-BA, ANFIS-GA, ANFIS-ICA, and ANFISPSO) were developed using the ANFIS model and different metaheuristic optimization algorithms. Their goodness-of-fit and predictive performance compared with the standalone ANFIS model were evaluated using the RMSE and AUC metrics. In the training step, the results revealed that the ANFIS model was better trained than the four hybridized models. In the validation step, however, the RMSE value of the standalone ANFIS model was much higher, indicating a sharp reduction in model performance. This is because the standalone ANFIS model structure learns the input pattern of the training data entirely (termed overfitting) and describes the fuzzy rules, and hence is eager to memorize relationships between the predictor factors and historical events, instead of learning to generalize from the trend (Esfahanian et al., 2017). This is why the ANFIS model often does not succeed in proper fitting in the validation step (Tahmasebi and Hezarkhani, 2012). This is in line with findings of Farokhnia et al. (2011), which evaluated the influence of sea surface temperature and sea level pressure on the accuracy of drought predictions made by the ANFIS model.

They concluded that the ANFIS model had considerable capacity to predict drought hazard, but that its prediction capacity needed improvement. In the validation step, the ANFIS-BA model outperformed other models. ANFIS-ICA was the second best model in terms of predictive performance, followed by ANFIS-GA and ANFIS-PSO. Importantly, all four hybridized models showed a marked improvement over the standalone ANFIS model. This means that the metaheuristic optimization algorithms enhanced the learning rate, generalizability, and predictive performance of the standalone ANFIS model. Similarly, Shirmohammadi et al. (2013) found that a new hybrid model for drought hazard prediction based on the wavelet transform technique and ANFIS (named wavelet-ANFIS model) performed better than the ANFIS model alone. Our results are consistent with Mokhtarzad et al. (2017) which compared the efficiency of ANFIS, support vector machines (SVM), and artificial neural networks (ANN) for predicting standardized precipitation index (SPI) and found that ANFIS had drawbacks and showed poor accuracy. However, both those studies used point data (i.e., weather station data), making it particularly challenging to interpolate drought indices over a study area. In this study, the shortcomings of previous approaches were overcome by spatially predicting drought hazard using several hybridized ANFIS models. Our results confirm Bui et al. (2018a) in that ANFIS-BA algorithms are better predictors than other hybridized ANFIS models for flood hazard mapping. In a study focusing on landslide modeling, Chen et al. (2017) demonstrated that optimizing the parameters of the ANFIS model through metaheuristic algorithms improved accuracy by decreasing local minimum and dimensional problems. Similarly, Aghdam et al. (2017), Chen et al. (2017), and Hong et al. (2018) found that a hybridized ANFIS model outperformed a standalone ANFIS model. The prediction models developed here may also be applicable in other regions with similar geomorphological, soil, and land use characteristics as the study area. For areas with

considerably different drought-influencing factors, new prediction models can be developed using the model development and evaluation approach described here. Overall, the approach can assist planning and environmental authorities with their estimations of and mitigation responses to drought hazards. Hybridized models using artificial intelligence can also be useful for the creation of drought hazard maps and to increase awareness among decision makers and relevant authorities tasked with revising drought risk management policy so they may improve preparedness and response systems. These improvements are important for the enhancement of resilience and the push toward sustainability of urban and rural areas prone to drought around the world (Kalantari et al., 2018 & 2019b).

4.2. Assessment of variable importance Investigating the importance of predictor variables is of practical relevance to researchers and environmental managers seeking to achieve efficient management of natural resources (Williams, 2011). Gómez and Blanco (2012) claims that analysis of the relationship between drought events and environmental factors allows managers to adapt to drought conditions. Various index-based methods have been proposed to predict drought hazard, but the relative importance of specific environmental factors is still debated (Banimahd and Khalili, 2013). Machine learning models can help decision makers gain new insights by extracting information about the relationships between drought and environmental factors (Deo et al., 2017a). In this study, the relative importance of predictor variables was analyzed using map removal sensitivity analysis (also known as one-at-a-time analysis) and it was found that the ANFIS-BA model showed the best predictive performance. The results revealed that PAWC and distribution of the percentage of the soil comprised of sand made the greatest contributions to spatial drought modelling. Both PAWC and distribution of the sand percentage influence soil-water availability, and thus plant ecophysiological responses and

crop yield (Agaba et al., 2010). Similarly, Hansen et al. (2016) found that soil physical properties, especially soil texture, can affect soil-water retention and the risk of drought in dry periods. However, as Bui et al. (2016) states, the relative importance of predictor variables to a model (e.g., a drought hazard model) is probably affected by the modeling strategy (structure of model, etc.). Therefore, further research should be performed using other modeling approaches (e.g., ML, data-mining, etc.) with a similar spatial framework, to investigate the relative importance of predictor variables in the spatial prediction of drought hazard.

5. Concluding remarks Drought is an abnormal, extreme, and prolonged natural event affecting almost all regions and threatening water supplies around the world. Drought monitoring and prediction using the best predictors is thus of vital importance. Hybridizing models can improve their drought hazard prediction performance, where accuracy is essential for preparing the best adaptation and mitigation measures. The following conclusions can be drawn: 

This study advanced model hybridization research by introducing a versatile drought modeling approach and demonstrating the capability of new hybrid models (ANFIS-BA, ANFIS-GA, ANFIS-ICA, and ANFIS-PSO) for detecting the spatial distribution of drought probabilities. In the validation step, ANFIS-BA outperformed other hybrid ANFIS-based models and the standalone ANFIS model. All hybrid models tested successfully improved the reliability of drought hazard maps in comparison to the standalone ANFIS model. In addition to more accuracy, all four hybridized ANFIS models demonstrated robust capacity to spatially model drought hazard. From an operational viewpoint, machine learning algorithms are consistent and suitable for all regions in the world.



Sensitivity analysis revealed that PAWC and distribution of the sand percentage were the two most important factors for spatially predicting drought hazard. Mean annual precipitation, distribution of the clay percentage, elevation, and soil depth, while slope percent and TWI made a low contribution to the drought modeling process.

 The drought hazard map generated by ANFIS-BA can serve as a useful screening tool illustrating where local risk assessments should be concentrated to improve drought preparedness and develop appropriate drought management policies. Overall, the proposed methodology, which provides a spatial framework for drought risk identification, can provide reliable information that assists decision makers in making effective and costeffective decisions. 

Although excellent results were obtained for the hybridized ANFIS models used in this study, the maps produced should not be regarded as representative of seasonal patterns of rainfall and soil moisture due to a lack of continuous soil moisture data. The soil moisture data used were for a short period and only available at 100 m resolution. Studies using soil moisture data at finer spatial and temporal resolution are needed. Furthermore, the efficiency of the proposed hybridized models should be investigated and compared to previous methods (e.g., analytical hierarchy process (AHP), drought indices) in other regions of the world. In this study, we set a threshold of 0.5 for both the standardized RDR (i.e., RDR>0.5) and the RDSM (i.e., RDSM>0.5) to identify drought locations in the study area. The effects of these thresholds on the modeling results should be evaluated.

Acknowledgments The authors thank the Australian Bureau of Statistics (ABS), Queensland Land Use Mapping Program (QLUMP), Terrestrial Ecosystem Research Network (TERN), and the National

Agricultural Monitoring Systems (NAMS) for providing data and relevant maps, and the University of Southern Queensland Postgraduate Research Scholarship for funding (2015– 2018) that supported the PhD study (K.S. Dayal). In addition, this research was partially supported by the Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Viet Nam. We greatly appreciate the assistance of the associate editor, Prof. Ashantha Goonetilleke, and four anonymous reviewers for their constructive comments that helped us to improve the paper.

References Abdulkadir, M.O., 2016. Fundamentals of Soil Science: 1st Edition, Chapter Two, Publisher: Thelemon Productions, pp. 27-62. Agaba, H., Baguma Orikiriza, L.J., Osoto Esegu, J.F., Obua, J., Kabasa, J.D. and Hüttermann, A., 2010. Effects of hydrogel amendment to different soils on plant available water and survival of trees under drought conditions. Clean–Soil, Air, Water, 38(4), pp.328-335. AghaKouchak, A., Farahmand, A., Melton, F.S., Teixeira, J., Anderson, M.C., Wardlow, B.D. and Hain, C.R., 2015. Remote sensing of drought: Progress, challenges and opportunities. Reviews of Geophysics, 53(2), pp.452-480. Aghdam, I.N., Pradhan, B. and Panahi, M., 2017. Landslide susceptibility assessment using a novel hybrid model of statistical bivariate methods (FR and WOE) and adaptive neuro-fuzzy inference system (ANFIS) at southern Zagros Mountains in Iran. Environmental Earth Sciences, 76(6), p.237. Ali, M., Deo, R.C., Downs, N.J., Maraseni, T., 2018. Multi-stage hybridized online sequential extreme learning machine integrated with Markov Chain Monte Carlo copula-Bat algorithm for rainfall forecasting. Atmospheric Research, 213, 450-464. American Meteorological Society (AMS), 2004. Statement on meteorological drought. Bull. Am. Meteorol. Soc. 85, 771–773. Anders, A.M., Roe, G.H., Hallet, B., Montgomery, D.R., Finnegen, J., Putkonen, J., 2006. Spatial patterns of precipitation and topography in the Himalaya. Geological Society of America Special Paper, 398, 39.53. Aniya, M., 1985. Landslide-susceptibility mapping in the Amahata River Basin, Japan. Ann Assoc Am Geogr. 75, 102–114. Aqil, M., Kita, I., Yano, A., Nishiyama, S., 2007. A comparative study of artificial neural networks and neuro-fuzzy in continuous modeling of the daily and hourly behaviour of runoff. J Hydrol 337, 22– 34 Atashpaz-Gargari, E., Lucas, C. 2007. Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. Proc., Evolutionary computation, CEC 2007. IEEE Congress on, IEEE, 4661-4667.

Ayalew, L., Yamagishi, H., Marui, H., Kanno, T., 2005. Landslides in Sado Island of Japan Part II. GIS-based susceptibility mapping with comparisons of results from two methods and verifications. Engineering Geology 81, 432–445 Banimahd, S.A., Khalili, D., 2013. Factors influencing Markov chains predictability characteristics, utilizing SPI, RDI, EDI and SPEI drought indices in different climatic zones. Water resources management, 27(11), 3911-3928. Biazin, B., Sterk, G., 2013. Drought vulnerability drives land-use and land cover changes in the Rift Valley dry lands of Ethiopia. Agriculture, Ecosystems & Environment, 164, 100-113. Brown, J.F., Wardlow, B.D., Tadesse, T., Hayes, M.J., Reed, B.C., 2008. The VegetationDrought Response Index (VegDRI): a new integrated approach for monitoringdrought stress in vegetation. GIScience Remote Sens. 45 (1), 16–46. Bui, D. T., Panahi, M., Shahabi, H., Singh, V. P., Shirzadi, A., Chapi, K., Khosravi, K., Chen, W., Panahi, S., Li, S. 2018a. Novel Hybrid Evolutionary Algorithms for Spatial Prediction of Floods. Scientific reports, 8(1), 15364. Bui, D.T., Khosravi, K., Li, S., Shahabi, H., Panahi, M., Singh, V., Chapi, K., Shirzadi, A., Panahi, S., Chen, W., Bin Ahmad, B., 2018b. New hybrids of ANFIS with several optimization algorithms for flood susceptibility modeling. Water, 10(9), p.1210. Bui, D.T., Tuan, T.A., Klempe, H., Pradhan, B., Revhaug, I., 2016. Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 13 (2), 361–378. Caball, R., Malekpour, S., 2019. Decision making under crisis: Lessons from the Millennium Drought in Australia. International Journal of Disaster Risk Reduction 34, 387-396. Carrão, H., Russo, S., Sepulcre-Canto, G., Barbosa, P., 2016. An empirical standardized soil moisture index for agricultural drought assessment from remotely sensed data. International journal of applied earth observation and geoinformation, 48, pp.74-84. Chen, J. and Yang, Y., 2011. A fuzzy ANP-based approach to evaluate region agricultural drought risk. Procedia Engineering, 23, 822-827. Chen, W., Panahi, M., Pourghasemi, H.R., 2017. Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling. Catena, 157, 310-324. Crozier, M. J., 1986. Landslides: Causes, Consequences and Environment, Croom Helm Australia Private Limited, 252p. Darabi, H., Choubin, B., Rahmati, O., Haghighi, A.T., Pradhan, B., Kløve, B., 2019. Urban flood risk mapping using the GARP and QUEST models: A comparative study of machine learning techniques. Journal of Hydrology, 569, 42-154. Dayal, K. S., Deo, R. C., Apan, A. A. 2017. Investigating drought duration-severity-intensity characteristics using the Standardized Precipitation-Evapotranspiration Index: case studies in drought-prone Southeast Queensland. Journal of Hydrologic Engineering, 23(1), 05017029. Dayal, K.S., Deo, R.C., Apan, A.A., 2018. Spatio-temporal drought risk mapping approach and its application in the drought-prone region of south-east Queensland, Australia. Natural Hazards, 93, 823-847.

Dehnavi, A., Aghdam, I. N., Pradhan, B., Varzandeh, M. H. M. 2015. A new hybrid model using stepwise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran. Catena, 135, 122-148. DEHP (Department of Environment and Heritage Protection), 2016. Climate change in Queensland. Deo, R.C., Byun, H.-R., Adamowski, J.F., Begum, K., 2017a. Application of effective drought index for quantification of meteorological drought events: a case study in Australia. Theoretical and applied climatology, 128(1-2), 359-379. Deo, R.C., Kisi, O., Singh, V.P., 2017b. Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model. Atmospheric Research, 184, 149-175. Deo, R.C., Tiwari, M.K., Adamowski, J.F., Quilty, J.M., 2017c. Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model. Stochastic environmental research and risk assessment, 31(5), 1211-1240. Di Baldassarre, G., Martinez, F., Kalantari, Z., Viglione, A. 2017. Drought and flood in the Anthropocene: feedback mechanisms in reservoir operation. Earth Syst. Dynam., 8(1), 225-233. doi:10.5194/esd-8-225-2017 Dinaburga, G., Lapins, D. and Kopmanis, J., 2010. Differences of soil agrochemical properties in connection with altitude in winter wheat. In Proceedings of Engineering for Rural Development Conference (pp. 27-28). Djerbouai, S., Souag-Gamane, D., 2016. Drought forecasting using neural networks, wavelet neural networks, and stochastic models: case of the Algerois Basin in North Algeria. Water Resources Management, 30(7), 2445-2464. Edwards, D.C., 1997. Characteristics of 20th century drought in the United States at multiple time scales. Atmospheric Science Paper No. 634, May 1-30. Esfahanian, E., Nejadhashemi, A.P., Abouali, M., Adhikari, U., Zhang, Z., Daneshvar, F., Herman, M.R., 2017. Development and evaluation of a comprehensive drought index. Journal of Environmental Management 185, 31-43. Falah, F., Rahmati, O., Rostami, M., Ahmadisharaf, E., DaliakopoulosI.N., Pourghasemi, H.R. 2019. Artificial Neural Networks for Flood Susceptibility Mapping in Data-Scarce Urban Areas, In book spatial modeling in GIS and R for earth and environmental science, Elsevier, pp 323-336 Farokhnia, A., Morid, S., Byun, H.R., 2011. Application of global SST and SLP data for drought forecasting on Tehran plain using data mining and ANFIS techniques. Theor Appl Climatol, 104, 71–81 Fenta, A.A., Kifle, A., Gebreyohannes, T., Hailu, G., 2015. Spatial analysis of groundwater potential using remote sensing and GIS-based multi-criteria evaluation in Raya Valley, northern Ethiopia. Hydrogeology Journal 23(1), 195-206. Garosi, Y., Sheklabadi, M., Conoscenti, C., Pourghasemi, H.R., Van Oost, K., 2019. Assessing the performance of GIS-based machine learning models with different accuracy measures for determining susceptibility to gully erosion. Science of The Total Environment, 664, 1117-1132. Glade, T., Anderson, M., Crozier, M.J., (eds) 2005. Landslide hazard and risk. Wiley, NewYork. Goetz, J.N., Brenning, A., Petschko, H., Leopold, P., 2015. Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Computers & geosciences, 81, 1-11. Goldberg, D. E., Holland, J. H. 1988. Genetic algorithms and machine learning. Machine learning, 3(2), 95-99.

Gómez, C.M.G., Blanco, C.D.P., 2012. Do drought management plans reduce drought risk? A risk assessment model for a Mediterranean river basin. Ecological Economics, 76, 42-48. Gouveia, C.M., Trigo, R.M., Beguería, S., Vicente-Serrano, S.M., 2017. Drought impacts on vegetation activity in the Mediterranean region: An assessment using remote sensing data and multi-scale drought indicators. Global and Planetary Change, 151, 15-27. Grabs, T., Seibert, J., Bishop, K., Laudon H., 2009. Modeling spatial patterns of saturated areas: A comparison of the topographic wetness index and a dynamic distributed model. Journal of Hydrology, 373, 15-23, 2009. Hansen, V., Hauggaard-Nielsen, H., Petersen, C.T., Mikkelsen, T.N., Müller-Stöver, D., 2016. Effects of gasification biochar on plant-available water capacity and plant growth in two contrasting soil types. Soil and Tillage Research, 161, 1-9. Hao, Z., AghaKouchak, A., 2013. Multivariate standardized drought index: a parametric multi-index model. Advances in Water Resources, 57, 12-18. Hao, Z., Hao, F., Singh, V.P., Xia, Y., Ouyang, W., Shen, X., 2016. A theoretical drought classification method for the multivariate drought index based on distribution properties of standardized drought indices. Advances in water resources, 92, 240-247. Hao, Z., Singh, V.P., 2015. Drought characterization from a multivariate perspective: A review. Journal of Hydrology, 527, 668-678. Holland, J. H. 1992. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, MIT press. Hollinger, S. E., S. A. Isard, M. R. Welford, 1993: A new soil moisture drought index for predicting crop yields. Preprints, Eighth Conf. on Applied Climatology, Anaheim, CA, Amer. Meteor. Soc., 187–190. Hong, H., Panahi, M., Shirzadi, A., Ma, T., Liu, J., Zhu, A.-X., et al. 2018. Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution. Science of The Total Environment, 621, 1124-1141. Huang, S.Z., Chang, J.X., Huang, Q., et al., 2015. Identification of abrupt changes of the relationship between rainfall and runoff in the Wei River basin, China. Theor. Appl. Climatol. 120(1-2), 299-310. Huang, W.C., Chou, C.C., 2008. Risk-based drought early warning system in reservoir operation. Advances in Water Resources 31, 649-660. Hyndman, R.J., Koehler, A.B., 2006. Another look at measures of forecast accuracy. International Journal of Forecasting. 22 (4), 679–688. doi:10.1016/j.ijforecast.2006.03.001 IPCC (Intergovernmental Panel on Climate Change), 2013. Summary for policy- makers. In: Stocker, T.F., Qin, D., Plattner, G.K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V., Midgley, P.M. (Eds.), Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge Uni- versity Press, Cambridge, United Kingdom and New York, NY, USA, p. 28. IPCC 2012. Summary for policymakers: a special report of working groups I and II of the intergovernmental panel on climate change. In: Field CB et al (eds) Managing the risks of extreme events and disasters to advance climate change adaptation. Cambridge University Press, Cambridge. Jaafari A., Panahi, M., Phamc, B.T., Shahab, H., Bui,D.T., Rezaie, F., Leef, S., 2019a. Meta optimization of an adaptive neuro-fuzzy inference system with grey wolf optimizer and biogeography-based optimization algorithms for spatial prediction of landslide susceptibility, Catena 175, 430–445

Jaafari, A., Zenner, E. K., Panahi, M., Shahabi, H. 2019b. Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability. Agricultural and Forest Meteorology, 266, 198-207. Jain, V.K., Pandey, R.P., Jain, M.K., Byun, H.R., 2015. Comparison of drought indices for appraisal of drought characteristics in the Ken River Basin. Weather and Climate Extremes, 8, 1-11. Jang, J.S. and Sun, C.T., 1995. Neuro-fuzzy modeling and control. Proceedings of the IEEE, 83(3), 378-406. Jang, J.S.R., 1993. ANFIS: adaptive-network-based fuzzy inference systems. IEEE Trans Syst Man Cybern 23, 665–685 Jothibasu, A., Anbazhagan, S., 2017. Drought Hazard Assessment in Ponnaiyar River Basin, India Using Remote Sensing and Geographic Information System, International Journal of Earth Sciences and Engineering, 10(02), 247-256. Kalantari, Z., Cavalli, M., Cantone, C., Crema, S., Destouni, G. 2017. Flood probability quantification for road infrastructure: Data-driven spatial-statistical approach and case study applications. Science of The Total Environment, 581-582, 386-398. https://doi.org/10.1016/j.scitotenv.2016.12.147 Kalantari, Z., Ferreira, C. S. S., Koutsouris, A. J., Ahmer, A.-K., Cerdà, A., Destouni, G. 2019a. Assessing flood probability for transportation infrastructure based on catchment characteristics, sediment connectivity and remotely sensed soil moisture. Science of The Total Environment, 661, 393-406. https://doi.org/10.1016/j.scitotenv.2019.01.009 Kalantari, Z., Ferreira, C.S.S., Keesstra, S., Destouni, G., 2018. Nature-based solutions for flooddrought risk mitigation in vulnerable urbanizing parts of East-Africa. Current Opinion in Environmental Science & Health 5, 73-78. https://doi.org/10.1016/j.coesh.2018.06.003 Kalantari, Z., Santos Ferreira, C.S., Page, J., Goldenberg, R., Olsson, J., Destouni, G., 2019b. Meeting sustainable development challenges in growing cities: Coupled social-ecological systems modeling of land use and water changes. Journal of Environmental Management 245, 471-480. https://doi.org/10.1016/j.jenvman.2019.05.086 Kennedy, J. 2011. Particle swarm optimization Encyclopedia of machine learning (pp. 760-766): Springer. Keršuliene, V., Zavadskas, E. K., Turskis, Z. 2010. Selection of rational dispute resolution method by applying new step‐wise weight assessment ratio analysis (SWARA). Journal of business economics and management, 11(2), 243-258. Keshavarz, M., Karami, E., 2014. Farmers' decision-making process under drought. Journal of arid environments, 108, 43-56. Keshavarz, M.R., Vazifedoust, M., Alizadeh, A., 2014. Drought monitoring using a Soil Wetness Deficit Index (SWDI) derived from MODIS satellite data. Agricultural Water Management, 132, 37-45. Keshtegar, B., Kisi, O., Arab, H.G. and Zounemat-Kermani, M., 2018. Subset modeling basis ANFIS for prediction of the reference evapotranspiration. Water Resources Management, 32(3), 11011116. Keyantash, J.A. and Dracup, J.A., 2004. An aggregate drought index: Assessing drought severity based on fluctuations in the hydrologic cycle and surface water storage. Water Resources Research, 40(9). Knudby, A., Brenning, A., LeDrew, E., 2010. New approaches to modelling fish-habitat relationships. Ecological Modelling, 221(3), 503–511

Łabędzki, L., 2017. Parameterization of drought vulnerability assessment in agriculture. Infrastructure and ecology of rural areas. Nr II/1/2017, POLSKA AKADEMIA NAUK, Oddział w Krakowie, s. 535–544 Liu, X., Zhu, X., Pan, Y., Li, S., Liu, Y., Ma, Y., 2016. Agricultural drought monitoring: Progress, challenges, and prospects. Journal of Geographical Sciences, 26(6), 750-767. Lobell, D.B., Hammer, G.L., Chenu, K., Zheng, B., McLean, G. and Chapman, S.C., 2015. The shifting influence of drought and heat stress for crops in northeast Australia. Global change biology, 21(11), 4115-4127. Luo, L., Wood, E.F., 2007. Monitoring and predicting the 2007 US drought. Geophys.Res. Lett. 34 (22). Mariano, D.A., dos Santos, C.A., Wardlow, B.D., Anderson, M.C., Schiltmeyer, A.V., Tadesse, T., Svoboda, M.D., 2018. Use of remote sensing indicators to assess effects of drought and humaninduced land degradation on ecosystem health in Northeastern Brazil. Remote Sensing of Environment, 213, 129-143. Martínez-Fernández, J., González-Zamora, A., Sánchez, N., Gumuzzio, A., Herrero-Jiménez, C.M., 2016. Satellite soil moisture for agricultural drought monitoring: Assessment of the SMOS derived Soil Water Deficit Index. Remote Sensing of Environment, 177, 277-286. Mastrangelo, A.M., Mazzucotelli, E., Guerra, D., De Vita, P., Cattivelli, L., 2012. Improvement of drought resistance in crops: from conventional breeding to genomic selection. Crop Stress and Its Management: Perspectives and Strategies. Springer, pp. 225–259. Maxion, R. A., Roberts, R. R., 2004. Proper Use of ROC Curves in Intrusion/Anomaly Detection, Technical Report Series CS-TR-871, Published by the University of Newcastle upon Tyne, School of Computing Science, Claremont Tower, Claremont Road, Newcastle upon Tyne, NE1 7RU, Uk, pp32 McKee, T.B., Doesken, N.J., Kleist, J., 1993. The relationship of drought frequency and duration to time scales, Proceedings of the Eighth Conference on Applied Climatology. American Meteorological Society, Anaheim, CA, pp. 179-183. McLeman, R., Herold, S., Reljic, Z., Sawada, M., McKenney, D., 2010. GIS-based modeling of drought and historical population change on the Canadian Prairies. Journal of Historical Geography, 36(1), 43-56. Mishra, A. K., Singh, V. P. 2011. Drought modeling–A review. Journal of Hydrology, 403(1-2), 157175. Mishra, A.K., Singh, V.P., 2010. A review of drought concepts. J. Hydrol. 391, 202-216. Mo, K.C., 2008. Model-based drought indices over the United States. J. Hydrometeorol. 9 (6), 1212– 1230. Mo, K.C., Lettenmaier, D.P., 2014. Objective drought classification using multiple land surface models. Journal of Hydrometeorology, 15(3), 990-1010. Moayedi H, Tien Bui D, Gör M, Pradhan B, Jaafari A. The Feasibility of Three Prediction Techniques of the Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System, and Hybrid Particle Swarm Optimization for Assessing the Safety Factor of Cohesive Slopes. ISPRS International Journal of Geo-Information 2019; 8: 391. Mogaji, KA., Lim, H.S., Abdullah, K., 2014. Regional prediction of groundwater potential mapping in a multifaceted geology terrain using GIS-based Dempster–Shafer model. Arab J Geosci. doi:10.1007/s12517-014-1391-1 Mokhtarzad, M., Eskandari, F., Vanjani, N.J., Arabasadi, A., 2017. Drought forecasting by ANN, ANFIS, and SVM and comparison of the models. Environmental Earth Sciences, 76(21), 729.

Moretti, F., Pizzuti, S., Panzieri, S., Annunziato, M., 2015. Urban traffic flow forecasting through statistical and neural network bagging ensemble hybrid modeling. Neurocomputing, 167, 3-7. Mpelasoka, F., Hennessy, K., Jones, R., Bates, B., 2008. Comparison of suitable drought indices for climate change impacts assessment over Australia towards resource management. International Journal of Climatology: A Journal of the Royal Meteorological Society, 28(10), 1283-1292. Murphy, B.F., Timbal, B., 2008. A review of recent climate variability and climate change in southeastern Australia, Int. J. Climatol., 28, 859– 879. Narasimhan, B., Srinivasan, R., 2005. Development and evaluation of Soil Moisture Deficit Index (SMDI) and Evapotranspiration Deficit Index (ETDI) for agricultural drought monitoring. Agricultural and Forest Meteorology, 133(1-4), 69-88. Naumann, G., Barbosa, P., Garrote, L., Iglesias, A. and Vogt, J., 2014. Exploring drought vulnerability in Africa: an indicator based analysis to be used in early warning systems. Hydrology and Earth System Sciences, 18(5), 1591-1604. Nguyen-Huy, T., Deo, R.C., An-Vo, D.A., Mushtaq, S., Khan, S., 2017. Copula-statistical precipitation forecasting model in Australia’s agro-ecological zones. Agricultural Water Management, 191, 153-172. Nicolai-Shaw, N., Zscheischler, J., Hirschi, M., Gudmundsson, L., Seneviratne, S.I., 2017. A drought event composite analysis using satellite remote-sensing based soil moisture. Remote Sensing of Environment, 203, 216-225. Noori, R., Hoshyaripour, G., Ashrafi, K., Nadjar-Araabi, B., 2010. Uncertainty analysis of developed ANN and ANFIS models in prediction of carbon monoxide daily concentration. Atmos Environ 44, 476–482 Oh, H.J., Kim, Y.S., Choi, J.K., Park, E., Lee, S., 2011. GIS mapping of regional probabilistic groundwater potential in the area of Pohang City, Korea. Journal of Hydrology, 399(3-4), 158172. Pal, M., Mather, P.M., 2005. Support vector machines for classification in remote sensing. International Journal of Remote Sensing, 26(5), 1007-1011. Palchaudhuri, M., Biswas, S., 2016. Application of AHP with GIS in drought risk assessment for Puruliya district, India. Natural Hazards, 84(3), 1905-1920. Pandey, S., Pandey, A. C., Nathawat, M. S., Kumar, M., Mahanti, N.C., 2012. Drought hazard assessment using geoinformatics over parts of Chotanagpur plateau region, Jharkhand, India, Nat Hazards, Volume 63, Issue 2, 279–303. Pham, B.T., Pradhan, B., Bui, D.T., Prakash, I., Dholakia, M.B., 2016. A comparative study of different machine learning methods for landslide susceptibility assessment: a case study of Uttarakhand area (India). Environmental Modelling & Software, 84, pp.240-250. Pham, D. T., Ghanbarzadeh, A., Koç, E., Otri, S., Rahim, S., Zaidi, M. 2006. The Bees Algorithm—A Novel Tool for Complex Optimisation Problems. Intelligent production machines and systems, Elsevier, 454-459. Poli, R., Kennedy, J., Blackwell, T., 2007. Particle swarm optimization. Swarm intelligence, 1(1), 3357. Cite as DOI 10.1007/s11069-012-0093 Pourghasemi, H.R., Pradhan, B., Gokceoglu, C., 2012. Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazards. 63(2), 965–996

Pourghasemi, H.R., Yousefi, S., Kornejady, A., Cerdà, A., 2017. Performance assessment of individual and ensemble data-mining techniques for gully erosion modeling. Science of the Total Environment, 609, pp.764-775. Pradhan, B., 2009. Ground water potential zonation for basaltic watersheds using satellite remote sensing data and GIS techniques. Cent Eur J Geosci. 1, 120–129 Prasad, R., Deo, R. C., Li, Y., Maraseni, T. 2017. Input selection and performance optimization of ANN-based streamflow forecasts in the drought-prone Murray Darling Basin region using IIS and MODWT algorithm. Atmospheric Research, 197, 42-63. Prasad, R., Deo, R.C., Li, Y., Maraseni, T., 2018a. Ensemble committee-based data intelligent approach for generating soil moisture forecasts with multivariate hydro-meteorological predictors. Soil and Tillage Research, 181, 63-81. Prasad, R., Deo, R.C., Li, Y., Maraseni, T., 2018b. Soil moisture forecasting by a hybrid machine learning technique: ELM integrated with ensemble empirical mode decomposition. Geoderma, 330, 136-161. Rahmati, O., Falah, F., Dayal, K., Deo, R.C., Mohammadi, F., Biggs, T., Moghaddam, D.D., Naghibi, S.A., Bui, D.T., 2019a. Machine learning approaches for spatial modeling of agricultural droughts in south-east region of Queensland Australia. Science of The Total Environment, p.134230. doi: 10.1016/j.scitotenv.2019.134230. Rahmati, O., Falah, F., Naghibi, S.A., Biggs, T., Soltani, M., Deo, R.C., Cerdà, A., Mohammadi, F., Bui, D.T., 2019b. Land subsidence modelling using tree-based machine learning algorithms. Science of The Total Environment 672, 239-252. Rahmati, O., Kornejady, A., Samadi, M., Deo, R.C., Conoscenti, C., Lombardo, L., Dayal, K., Taghizadeh-Mehrjardi, R., Pourghasemi, H.R., Kumar, S., Bui, D.T., 2019c. PMT: New analytical framework for automated evaluation of geo-environmental modelling approaches. Science of the total environment, 664, 296-311. Rahmati O, Yousefi S, Kalantari Z, Uuemaa E, Teimurian T, Keesstra S, et al. Multi-Hazard Exposure Mapping Using Machine Learning Techniques: A Case Study from Iran. Remote Sensing 2019d; 11: 1943. Rahmati, O., Pourghasemi, H.R., Melesse, A.M., 2016. Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: a case study at Mehran Region, Iran. Catena, 137, 360-372. Raupach, M.R., Briggs, P.R., Haverd, V., King, E.A., Paget, M., Trudinger, C.M., 2012. Australian water availability project. Canberra: CSIRO Marine and Atmospheric Research. Recknagel, F., Bobbin, J., Whigham, P.A., Wilson, H., 2000. Multivariate time-series modelling of algal blooms in freshwater lakes by machine learning. In Proceedings of the 5th International Symposium WATERMATEX’2000 on Systems Analysis and Computing in Water Quality Management. Ghent, Belgium. Rhee, J., Im, J., Carbone, G.J., 2010. Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data. Remote Sensing of Environment, 114(12), 2875-2887. Robinson, S., 2014. Simulation: The Practice of Model Development and Use. Palgrave Macmillan, 2nd edition. Rodhe, A., Seibert, J., 1999. Wetland occurrence in relation to topography: a test of topographic indices as moisture indicators. Agricultural and Forest Meteorology 98–99, 325–340. Safavi, H.R., Esfahani, M.K., Zamani, A.R., 2014. Integrated index for assessment of vulnerability to drought, case study: Zayandehrood River Basin, Iran. Water Resources Management, 28(6), 1671-1688.

Saidi, S., Bouri, S., Ben Dhia, H., 2011. Sensitivity analysis in groundwater vulnerability assessment based on GIS in the Mahdia-Ksour Essaf aquifer, Tunisia: a validation study. Hydrological Sciences Journal–Journal des Sciences Hydrologiques, 56(2), 288-304. Schicker, R., Moon, V., 2012. Comparison of bivariate and multivariate statistical approaches in landslide susceptibility mapping at a regional scale. Geomorphology 161, 40–57. Shekhar, S., Pandey, A.C., 2015. Delineation of groundwater potential zone in hard rock terrain of India using remote sensing, geographical information system (GIS) and analytic hierarchy process (AHP) techniques. Geocarto Int. 30, 402–421. Shirmohammadi, B., Moradi, H., Moosavi, V., Semiromi, M.T., Zeinali, A., 2013. Forecasting of meteorological drought using Wavelet-ANFIS hybrid model for different time steps (case study: southeastern part of east Azerbaijan province, Iran). Natural Hazards, 69(1), 389-402. Shukla, S., Wood, A.W., 2008. Use of a standardized runoff index for characterizing hydrologic drought. Geophys. Res. Lett. 35 (2), L02405. https://doi.org/10.1029/2007GL032487. Sohn, E., 2007. The big dry: Prolonged drought threatens Australia's people, wildlife, and economy. Science News, 172(17), 266-268. Sohrabi, M.M., Ryu, J.H., Abatzoglou, J., Tracy, J., 2015. Development of soil moisture drought index to characterize droughts. J. Hydrol. Eng. 4015025. http://dx.doi. org/10.1061/(ASCE)HE.19435584.0001213. Swain, S., Wardlow, B.D., Narumalani, S., Tadesse, T., Callahan, K., 2011. Assessment of vegetation response to drought in Nebraska using Terra-MODISland surface temperature and normalized difference vegetation index.GIScience Remote Sens. 48 (3), 432–455. Tahmasebi, P., Hezarkhani, A., 2012. A hybrid neural networks-fuzzy logic-genetic algorithm for grade estimation. Comput. Geosci. 42, 18–27. Tallaksen, L.M., Hisdal, H., Van Lanen, H.A., 2009. Space–time modelling of catchment scale drought characteristics. Journal of Hydrology, 375(3-4), 363-372. Tien Bui D, Hoang N-D, Martínez-Álvarez F, Ngo P-TT, Hoa PV, Pham TD, et al. A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area. Science of The Total Environment 2019: 134413. Trigila, A., Frattini, P., Casagli, N., Catani, F., Crosta, G., Esposito, C., Iadanza, C., Lagomarsino, D., Mugnozza, S., Segoni, S., Spizzichino, D., Tofani, V., Lari S., 2013. Landslide susceptibility mapping at national scale: the Italian case study. Landslide Sci Prac 1:287–295 Uddameri, V., Singaraju, S., Hernandez, E.A., 2019. Is Standardized Precipitation Index (SPI) a Useful Indicator to Forecast Groundwater Droughts?—Insights from a Karst Aquifer. JAWRA Journal of the American Water Resources Association, 55(1), 70-88. Vicente-Serrano, S.M., Beguería, S. and López-Moreno, J.I., 2010. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. Journal of climate, 23(7), 1696-1718. Wang, R., Zhang, J., Guo, E., Alu, S., Li, D., Ha, S., Dong, Z., 2018. Integrated drought risk assessment of multi-hazard-affected bodies based on copulas in the Taoerhe Basin, China. Theoretical and Applied Climatology, 1-16. Wilhite, D.A., 2000. Drought as a natural hazard: concepts and definitions, Chap 1. In: Wilhite DA (ed) Drought: A Global Assessment. Natural Hazards and Disasters Series. Routledge Publishers, UK. Williams, B.K., 2011. Adaptive management of natural resources—framework and issues. Journal of environmental management, 92(5), 1346-1353.

Xu, K., Yang, D.W., Yang, H.B., et al., 2014. Spatio-temporal variation of drought in China during 1961-2012: a climatic perspective. J. Hydrol. 526, 253-264 Yang, H., Wang, H., Fu, G., Yan, H., Zhao, P., Ma, M., 2017. A modified soil water deficit index (MSWDI) for agricultural drought monitoring: Case study of Songnen Plain, China. Agricultural water management, 194, 125-138. Yang, J., 2011. Convergence and uncertainty analyses in Monte-Carlo based sensitivity analysis. Environmental Modelling & Software, 26(4), 444-457. Zare, M., Koch, M., 2018. Groundwater level fluctuations simulation and prediction by ANFIS-and hybrid Wavelet-ANFIS/Fuzzy C-Means (FCM) clustering models: Application to the Miandarband plain. Journal of Hydro-environment Research, 18, 63-76. Zhang, Q., Xiao, M., Singh, V. P., Chen, X. 2013. Copula-based risk evaluation of hydrological droughts in the East River basin, China. Stochastic environmental research and risk assessment, 27(6), 1397-1406.

Highlights 

An integrated agricultural-meteorological drought was spatially modeled.



The hybridized drought hazard model ANFIS-BA performed best.



Hybrid models prevent overfitting and can improve ANFIS performance.



The most important factors for spatial drought hazard were determined.

Fig. 1 Methodological flowchart of this study.

Fig. 2 Map of the study area showing topographical variation and the locations of important cities (To: Toowoomba, Wa: Warwick, GC: Gold Coast, Ox: Oxenford, Sp: Springwood, Cl: Cleveland, Bri: Brisbane, Bre: Brendal, KR: Kippa-Ring).

Fig. 3 The standardized relative departure of rainfall (RDR) and the corresponding relative departure of soil moisture (RDSM) using fuzzy membership functions bounded by [0, 1] presented for 2009 and 2013: (a) RDR in 2009, (b) RDR in 2013, (c) RDSM in the upper soil layer in 2009, (d) RDSM in lower soil layer in 2009, (e) RDSM in the upper soil layer in 2013, and (f) RDSM in lower soil layer in 2013.

Fig. 4 Four sample data sets for positive (i.e. drought) and negative (i.e., non-drought) cases in the study area: a) dataset D1, b) dataset D2, c) dataset D3, and 4) dataset D4.

Fig. 5 Drought-related factors: a) elevation, b) slope degree, c) topographic wetness index (TWI), d) distribution of the clay percentage, e) depth of soil, f) distribution of the sand percentage, g) plant available water holding capacity (PAWC), and h) mean annual precipitation.

Fig. 6 Drought probability maps produced by a) ANFIS, b) ANFIS-BA, c) ANFIS-GA, d) ANFIS-ICA, and e) ANFIS-PSO

Fig. 7 Drought hazard maps (produced with data set D2 which had the highest accuracy).

Table 1 Goodness-of-fit of models based on two evaluation metrics (AUC and RMSE) Evaluation metric AUC (%)

RMSE

Models Data set

ANFIS

ANFIS-BA

ANFISGA

ANFISICA

ANFISPSO

D1

85.3

84.7

82.7

82.8

82.5

D2

85.9

85.6

83.3

83.9

83.2

D3

85.7

85.1

83.1

83.4

82.9

D4

84.8

84.5

82.6

82.7

82.3

D1

0.072

0.215

0.228

0.225

0.234

D2

0.063

0.211

0.221

0.219

0.227

D3

0.081

0.219

0.225

0.222

0.229

D4

0.098

0.303

0.311

0.310

0.315

Table 2 Predictive performance of models based on two evaluation metrics (AUC and RMSE) Evaluation metric

Models Data set

ANFIS

ANFIS-BA

ANFISGA

ANFISICA

ANFISPSO

D1

71.7

83.8

81.6

81.9

81.2

D2

73.2

84.1

82.4

82.7

82.1

D3

72.9

83.5

81.2

82.3

81.5

D4

69.4

83.4

81.3

81.6

80.9

D1

0.341

0.236

0.244

0.248

0.252

D2

0.332

0.227

0.236

0.233

0.241

D3

0.349

0.239

0.253

0.252

0.261

D4

0.355

0.242

0.256

0.258

0.267

AUC (%)

RMSE

Table 3 Results of the sensitivity analysis using ANFIS-BA model No. Excluded factor

AUCi (%)

Relative decrease (RD) of AUC (%)

1

70.3

16.41

74.2

11.77

76.8

8.68

78.5

6.66

5

PAWC Distribution of the sand percentage Mean annual precipitation Distribution of the clay percentage Elevation

80.9

3.8

6

Soil depth

81.4

3.21

2 3 4

7

Slope percent

82.8

1.55

8

TWI

82.9

1.43

AUCi: the accuracy of the model when a factor is excluded from the modeling