A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern Iran

Science of the Total Environment 660 (2019) 443–458

Contents lists available at ScienceDirect

Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern Iran Alireza Arabameri a,⁎, Khalil Rezaei b, Artemi Cerdà c, Christian Conoscenti d, Zahra Kalantari e a

Department of Geomorphology, Tarbiat Modares University, Tehran 36581-17994, Iran Faculty of Earth Sciences, Kharazmi University, Tehran 14911-15719, Iran Soil Erosion and Degradation Research Group, Departament de Geografia, Universitat de València, Blasco Ibàñez, 28, 46010, Valencia, Spain d Department of Earth and Marine Sciences (DISTEM), University of Palermo, Palermo, Italy e Stockholm University, Department of Physical Geography and Bolin Centre for Climate Research, SE-106 91 Stockholm, Sweden b c

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Robustness of statistical and MCDM models compared in flood hazard susceptibility mapping. • Validation results show that statistical models have higher prediction accuracy than MCDM model • AHP results indicated that slope had key importance in flood occurrence in the study area. • Scientific methodology introduced in this research is adaptable and can be used in other sites.

a r t i c l e

i n f o

Article history: Received 13 November 2018 Received in revised form 3 January 2019 Accepted 3 January 2019 Available online 05 January 2019 Editor: Ralf Ludwig Keywords: Soil erosion Natural hazard Environmental management Modelling Kiasar watershed

a b s t r a c t In north of Iran, flood is one of the most important natural hazards that annually inflict great economic damages on humankind infrastructures and natural ecosystems. The Kiasar watershed is known as one of the critical areas in north of Iran, due to numerous floods and waste of water and soil resources, as well as related economic and ecological losses. However, a comprehensive and systematic research to identify flood-prone areas, which may help to establish management and conservation measures, has not been carried out yet. Therefore, this study tested four methods: evidential belief function (EBF), frequency ratio (FR), Technique for Order Preference by Similarity To ideal Solution (TOPSIS) and Vlse Kriterijumsk Optimizacija Kompromisno Resenje (VIKOR) for flood hazard susceptibility mapping (FHSM) in this area. These were combined in two methodological frameworks involving statistical and multi-criteria decision making approaches. The efficiency of statistical and multi-criteria methods in FHSM were compared by using area under receiver operating characteristic (AUROC) curve, seed cell area index and frequency ratio. A database containing flood inventory maps and flood-related conditioning factors was established for this watershed. The flood inventory maps produced included 132 flood conditions, which were randomly classified into two groups, for training (70%) and validation (30%). Analytical hierarchy process (AHP) indicated that slope, distance to stream and land use/land cover are of key importance in flood occurrence in the study catchment. In validation results, the EBF model had a better prediction rate

⁎ Corresponding author. E-mail addresses: [email protected], [email protected] (A. Arabameri), [email protected] (A. Cerdà), [email protected] (C. Conoscenti), [email protected] (Z. Kalantari).

https://doi.org/10.1016/j.scitotenv.2019.01.021 0048-9697/© 2019 Elsevier B.V. All rights reserved.

444

A. Arabameri et al. / Science of the Total Environment 660 (2019) 443–458

(0.987) and success rate (0.946) than FR, TOPSIS and VIKOR (prediction rate 0.917, 0.888, and 0.810; success rate 0.939, 0.904, and 0.735, respectively). Based on their frequency ratio and seed cell area index values, all models except VIKOR showed acceptable accuracy of classification. © 2019 Elsevier B.V. All rights reserved.

1. Introduction The Kiasar watershed is known as one of the critical areas in north of Iran, due to numerous floods and waste of water and soil resources, as well as related economic and ecological losses. However, a comprehensive and systematic research to identify flood-prone areas, which may help to establish management and conservation measures, has not been carried out yet. Flooding is one of the most devastating natural hazards, with extensive financial and human losses (Perucca and Angilieri, 2011, Tierney et al., 2001). Flood events show an increasing frequency and affect every year vast areas worldwide (Nandi et al., 2016), with an average of 140 million people suffering from flood damages all around the world (WHO, 2003). This phenomenon often causes secondary disasters such as erosion, landslide, and sink holes (Cao et al., 2016). Floods happen at varying intervals and have different durations (Shafapour Tehrany et al., 2015a, 2015b). Prediction of flood occurrence is thus crucial to reduce the damage and mitigate the associated risk. However, flooding is a complex phenomenon controlled by many physical factors and, as such, its prediction remains challenging (Cao et al., 2016; Kalantari et al., 2014). The spatial and temporal variation of climate conditions as well as population growth and urbanization have caused in Iran, over the past decade, massive floods which caused an economic loss of $ 1.7 million USD (Norouzi and Taslimi, 2012). In Iran, N3700 flood events have been reported over the past 60 years (WDI, 2002) whereas damage caused by flooding has increased by 250% in the last decade (Norouzi and Taslimi, 2012). Accordingly, flood events are expected to further increase in low-lying areas due to the growth of urbanization, and increasing deforestation. The flood-prone areas in Iran are estimated to be 91 million ha, which means, in other words, that 55% of the watersheds in Iran are involved in rapid runoff production, of which 42 million ha have moderate to very high flooding susceptibility (WDI, 2002). Floods can inform also about the changes in the hydrological system due to the impact of human management and use of the land (Keesstra, 2007; Keesstra et al., 2009). This is because floods inform about the connectivity of the land flows of water and sediments (Keesstra et al., 2014; Masselink et al., 2017; Keesstra et al., 2018a). Floods also transform the river morphology and can be used as a tracer of the hydrological changes (Yousefi et al., 2018). Investigations of the environmental factors have shown that human intervention in the natural cycle of water through degradation of vegetation in the watershed area, inappropriate land use change, soil erosion and expansion of paved surface area has increased the potential for flooding in different regions. Hence, the floodplain area has expanded and more land has become prone to flood occurrence (Shafapour Tehrany et al., 2015a, 2015b). This is a key issue for the environmental health of the watersheds (Hazbavi et al., 2018; Kalantari et al., 2018; Alilou et al., 2019). Although preventing the occurrence of floods is not possible, accurate predictions as well as flood control may be achieved by employing appropriate methods and performing proper analyses (Cloke and Pappenberger, 2009). Indeed, it is imperative to take measures to prevent flood damage and reduce its destructive effects (Billa et al., 2006; Dang et al., 2011; Alvarado-Aguilar et al., 2012). An instrument that can help in the prevention of damage caused by flooding is flood hazard susceptibility mapping (FHSM) (Bubeck et al., 2012). FHSM is the first step in preventing and managing future floods (Wu et al., 2010). In general, FHSM involves determination of flooding-susceptible areas according to flood-related conditioning factors (FRCFs) (Liu et al., 2008; Pradhan and Youssef, 2011). FHSM can

be used by decision makers as an essential and fundamental approach for water resources management, erosion control, sustainable land planning, designing hydraulic structures and infrastructure and reducing flood damage (Wu et al., 2010). To this aim, GIS (geographic information system) and statistical analysis are employed to establish functional relationships between flood occurrence and controlling physical variables, which are largely derived from available topographic data (Bajabaa et al., 2014; Zhang et al., 2015; Youssef et al., 2016). Topography indeed is a key factor in flood studies, with a dramatic effect on hydrological and hydraulic parameters (Vaze et al., 2010; Shafique et al., 2011; Caviedes-Voullième et al., 2012). The lack of detailed topographic data, which is frequent in data-poor regions, makes flood prediction a very challenging task. However, in recent years, the availability of free-of-charge digital elevation models (DEMs) at suitable resolution has increased and therefore they have been extensively used in various studies related to natural hazards (Wechsler, 2007). Access to satellite and radar remote sensing (RS) data and improved available methods have increased the use of GIS in the field of FHSM (Pradhan et al., 2014; Xiao et al., 2017; Kalantari et al., 2017a; Tien Bui and Hoang, 2017; Ahlmer et al., 2018). Conventional rainfall-runoff modelling methods are not always suitable for comprehensive river and flood analysis. One of the main limitations of these methods is their need for different data, which are often not available at the watershed or regional scale. Therefore, the number of hydrological and flood studies that use GIS and statistical analysis of landscape attributes has increased in the recent years (Kalantari et al., 2017a; Shafapour Tehrany and Kumar, 2018). A wide range of modelling techniques has been used by various researchers to assess risk of flooding around the world. The models used include hydrological, statistical and data mining/machine learning algorithms, including WetSpa (Bahremand et al., 2007); The Soil and Water Assessment Tool (SWAT) (Oeurng et al., 2011); frequency ratio (FR) (Lee et al., 2012; Shafapour Tehrany et al., 2013; Rahmati et al., 2015; Samanta et al., 2018); analytical hierarchy process (AHP) (Chen et al., 2011; Zou et al., 2013; Kazakis et al., 2015; Rahmati et al., 2016b); artificial neural network (ANN) (Kia et al., 2012; Nikoo et al., 2016; Kourgialas and Karatzas, 2017); k-nearest neighbor (KNN) (Liu et al., 2016); weights-of-evidence (WofE) (Rahmati et al., 2016a, 2016b); fuzzy weight of evidence (fuzzy-WofE) (Hong et al., 2018b); fuzzy logic (Zou et al., 2013); random forest (RF) (Zhao et al., 2018); index of entropy (IOE) (Haghizadeh et al., 2017); support vector machine (SVM) (Shafapour Tehrany et al., 2015a, 2015b); statistical index (SI) (Cao et al., 2016); logistic regression (LR) (Pradhan, 2010; Nandi et al., 2016; Ettinger et al., 2016); adaptive neuro-fuzzy inference system (ANFIS) (Hong et al., 2018a, 2018b); naïve Bayes (NB) (Liu et al., 2015); decision tree (Shafapour Tehrany et al., 2013); neural-fuzzy approach (NF) (Tien Bui et al., 2016); and evidential belief function (EBF) (Shafapour Tehrany and Kumar, 2018). Multi-criteria decision analysis is a useful and powerful tool for solving complex decision problems that often require non-comparable criteria and data (Hwang and Yoon, 1981; Malczewski, 2006; Arabameri et al., 2017a, 2018a). A combination of GIS with multicriteria decision-making (MCDM) models has been used by some researchers in spatial modelling and analysis of natural disasters, especially flooding (Scheuer et al., 2011; Paquette and Lowry, 2013; Kazakis et al., 2015; Rahmati et al., 2016a, 2016b; Samanta et al., 2016; Karlsson et al., 2017). the main objectives of this study are to: i) evaluate the performance of TOPSIS and VIKOR in predicting the spatial distribution of flooded

A. Arabameri et al. / Science of the Total Environment 660 (2019) 443–458

areas and predictive ability; ii) compare the ability to identify flooded areas of these MCDM methods with that of the two statistical methods, namely Frequency Ratio (FR) and Evidential Belief Function (EBF). 2. Material and methods 2.1. Study area The Kiasar watershed, located in Mazandaran province, Northern Iran (35°56′22″–36°49′20″N, 53°00′34″–53°43′41″E); occupies an area of 3648.32 km2 (Fig. 1). The minimum and maximum elevation of the study area is 1 m.a.s.l and 3725 m.a.s.l, respectively (mean elevation 932.62 m.a.s.l.) and the slope varies from 0 to 72.45° (mean 15.70°). Northern parts of the watershed have smooth topography with gentle slopes, while the rest has mountainous topography, with much being located in the Alborz Mountains. The mean annual rainfall in the Kiasar watershed is around 600 mm (IRIMO, 2012). Based on LU/LC parameters, the study area is covered by 13 classes including Agriculture (A) (9.31%), Airport (B) (0.017%), Orchard (C) (2.48%), Dense forest (D) (64.25%), Good range (E) (3.83%), Low forest (F) (1.32%), Agriorchard (J) (3.96%), Agri-dryfarming (H) (0.902%), Dryfarming (I) (9.29%), Mod-forest (J) (2.90%), Mod-range (K) (0.980%), Urban (L) (608%), and Water (M) (0.112%). The main lithology units are marl, calcareous sandstone, sandy limestone and minor conglomerate (Mm,s,l), and red conglomerate and sandstone (Mc) (GSI, 1997). Alfisols (58.30%), Mollisols (25.76%) and rock outcrops/Entisols (8.73%) are the most common soils in the study area. 2.2. Methodology This study comprised four main stages (see Fig. 2): i) Establishing a database including flood inventory maps (FIM) and flood-related

445

conditioning factors (FRCFs) using data sources including a 1:100,000 geological map, a 1:50,000 topographical map, Landsat 8 OLI images, ALOS (The Advanced Land Observing Satellite) Phased Array type Lband Synthetic Aperture Radar (PALSAR) digital elevation model (DEM) with spatial resolution of 30 m, and extensive field survey, ii) Applying multicollinearity analysis with tolerance (TOL) and variance inflation factor (VIF) for selection of FRCFs. In FHSM, testing for collinearity among the effective parameters in flooding is very important. VIF and TOL are very commonly used indicators for checking multicollinearity among parameters (Arabameri et al., 2017b, 2018b, 2018c, 2018d, 2019; Arabameri and Pourghasemi, 2019). TOL values b0.1 and VIF values N10 indicate collinearity between the parameters (W. Chen et al., 2017; Z. Chen et al., 2017). The collinearity reduces the accuracy of the FHSM. In this study, the values of VIF and TOL were obtained using SPSS16 software. iii) applying the EBF and FR statistical models in a GIS environment for flood hazard susceptibility mapping (FHSM), iv) applying the TOPSIS and VIKOR MCDM models in SPSS software for FHSM, and v) comparing the efficiency of statistical and multi-criteria methods in FHSM, using area under curve (AUC), seed cell area index (SCAI) and frequency ration (FR). 2.3. Database 2.3.1. Flood inventory map The FIM is the most important prerequisite in FHSM (Manandhar, 2010; Shafapour Tehrany and Kumar, 2018). There are several methods for preparation of The FIM (Zou et al., 2013). The choice of better method depends on several parameters such as the research purpose, the environmental conditions of the study area and access to RS and GIS data (Pradhan, 2013; Pourghasemi et al., 2013). In this study, we used reports from the Iranian Water Resources Department for the period 2001–2009, investigative reports on crisis management in

Fig. 1. Study area. a) Location of Iran in world. b) Location of Kiasar watershed in Iran and Mazandaran province. c) Location of training and validation floods in the study area.

446

A. Arabameri et al. / Science of the Total Environment 660 (2019) 443–458

Fig. 2. Flowchart of research.

Mazandaran province and extensive field surveys. The FIM produced contained 132 flood conditions, which were randomly classified into two groups, for training (70%) and validation (30%). An example of flood damage in the study area is shown in Fig. 3. 2.3.2. Flood-related conditioning factors Floods are caused by a variety of reasons that are known as floodrelated conditioning factors (FRCFs). In order to carry out sensitivity analysis and FHSM, the importance and relation of each of these conditioning factors should be evaluated (Cao et al., 2016). In this study, based on the literature (Shafapour Tehrany et al., 2013, 2017a, 2017b; Shafapour Tehrany and Kumar, 2018; Khosravi et al., 2016a, 2016b; Rahmati et al., 2016a, 2016b; Haghizadeh et al., 2017; Mojaddadi et al., 2017; Rahmati and Pourghasemi, 2017; Termeh et al., 2018) and environmental conditions in the study area, 12 FRCFs were used. They were: elevation, slope, aspect, plan curvature, topography wetness index (TWI), stream power index (SPI), distance to stream, drainage density, normalised difference vegetation index (NDVI), soil type, lithology and land use/land cover (Fig. 4a–l). PALSAR DEM (http://www.eorc.

jaxa.jp/ALOS/en/aw3d30), with spatial resolution of 30 m, was used for extraction of topographical parameters. The basic maps used in this study were geological maps (scale 1:100,000), topographical maps (scale 1:50,000) and Landsat 8 images (spatial resolution 30 m). Elevation, slope, aspect, plan curvature, SPI and TWI were extracted from PALSAR DEM in ArcGIS10.5. In order to provide the parameters of distance to stream and drainage density, the stream network was extracted from PALSAR DEM in Arc Hydro environment. In order to provide more accurate flow direction and flow accumulation, holes in DEM were filled and then flow direction and accumulation were extracted. A threshold of 1000 cells was used for extraction of the stream network. After stream extraction, the Euclidean Distance and Line Density tools in ArcGIS10.5 were used for calculation of distance and density of streams. A soil type map (1:250,000) was obtained from Mazandaran Agricultural and Natural Resources Research Center and classified into five classes. Based on the separation and digitisation of the polygons of the lithological units from the geological map with scale 1:100,000, a lithology map was prepared in ArcGIS10.1 and classified into nine groups (Table 1).

A. Arabameri et al. / Science of the Total Environment 660 (2019) 443–458

447

Fig. 3. An example of flood damages in the study area.

A land use/land cover (LU/LC) map of the study area was produced using Landsat 8 images from 09/10/2018, covering path 163 and row 35. To create the LU/LC map, a supervised classification using the maximum likelihood algorithm was applied. The map produced was verified using 650 ground control points (GCP) in the field. The Kappa coefficient of the map generated was found to be 98.95%. Table 2 shows the range of parameters used for classification along with their classes and methods. 2.4. Statistical models Frequency ratio is the probability of occurrence of a particular phenomenon. In other words, this method determines the level of correlation between flood locations. The greater the proportion of this ratio in a class of the specific factor, the more important that class of the relevant factor is in the field of flood occurrence. Flood potential index for each pixel is equal to the sum of FR of that pixel in all factors. If there are M effective factors, this index is calculated as follows: FPIFR ¼

M X

ð1Þ

FRi

i¼1

m X

WCij D

ð2Þ

j¼1

wCij D ¼

m X

WCij D

ð4Þ

j¼1

wCij D ¼

    N C ⋂D =N Cij   ij     NðTÞ−N ðDÞ− N Cij −N Cij ⋂D =N ðTÞ−N Cij

ð5Þ

h    i Unc ¼ 1− BelCij − DisCij

ð6Þ

h  i Pls ¼ 1− DisCij

ð7Þ

where BelC ij is the belief value, DisC ij is the disbelief value, N(Cij ⋂ D) is the density of flood pixels in class D, N(Cij) is the total number of floods in the study area, N(D) is the number of pixels in class D and N (T) is the total number of pixels in the study area (Shafapour Tehrany and Kumar, 2018). 2.5. Multi criteria decision making models

Finally, the values obtained for each class are added to relevant thematic layers and the FHSM is obtained using Weighted Sum tools in ArcGIS10.5. The Evidential belief function statistical model is based on a theory which was originally introduced by Dempster (1968) and refined by Shafer (1976). The Dempster-Shafer theory provides a framework for evaluating EBF based on the Dempster computation law. The EBF is a combination of the degree of belief (Bel), disbelief (Dis), uncertainty (Unc) and plausibility (Pls) (Nampak et al., 2014). Values of this indicator vary from 0 to 1 (Ghosh and Carranza, 2010). These four indicators were calculated using Eqs. (2)–(7). BelCij ¼ wCij D =

DisCij ¼ wCij D =

    N C ⋂D =N Cij   ij    NðDÞ− N Cij ⋂D =N ðTÞ−N Cij

ð3Þ

TOPSIS is a very useful MCDM model introduced by Hwang and Yoon (1981). The underlying logic of TOPSIS is to define the positive and negative ideal solutions. The positive ideal solution is the solution that maximises the benefit criteria and minimises the cost criteria, whereas the negative ideal solution is the solution that maximises the cost criteria and minimises the benefit criteria. In short, the positive ideal solution consists of all the best values attainable of criteria, whereas the negative ideal solution is composed of all the worst values attainable of criteria. The optimal alternative is the one which has the shortest distance from the positive ideal solution and the longest distance from the negative ideal solution. The VIKOR method was first introduced by Opricovic (1998) and modified by Opricovic and Tzeng (2002) to optimise MCDM in complex systems. The Serbian name of the model, Vlse Kriterijumsk Optimizacija Kompromisno Resenje, means multi-criteria optimisation and consolidation. This method involves categorising and choosing from one set of options and defines compromise responses for a problem with opposing criteria, to help decision makers reach a final decision.

448

A. Arabameri et al. / Science of the Total Environment 660 (2019) 443–458

Fig. 4. Flood conditioning factors researched. a) elevation, b) slope, c) aspect, d) plan curvature, e) topography wetness index, f) stream power index, j) distance to stream, h) drainage density, i) NDVI, j) soil type, k) lithology, m) land use/land cover.

2.6. Determining the weight of criteria in TOPSIS and VIKOR using the AHP model Analytical hierarchy process is a semi-quantitative, multi-criteria and multi-objective method in which the weight of each of the

parameters is calculated based on the knowledge and skills of experts in the form of pair-wise comparisons without any incompatibility (Saaty and Vargas, 1998). This method uses a numerical scale ranging from 1 to 9 in order to prioritise quantitative and qualitative parameters relative to one another (Table 3).

A. Arabameri et al. / Science of the Total Environment 660 (2019) 443–458 Table 1 Lithology of study area. Group Code A

B

C

D

E

F

G

H

I

449

Table 3 Weighting the factors based on preference in paired comparison.

Description

Formation

Cm

Dark grey to black fossiliferous limestone with Mobarak subordinate black shale Cb Alternation of dolomite, limestone and variegated Barut shale – DCkh Yellowish, thin to thick - bedded, fossiliferous argillaceous limestone, dark grey limestone, greenish marl and shale, locally including gypsum Db-sh Undifferentiated limestone, shale and marl – Ek Well bedded green tuff and tuffaceous shale Karaj E1m Marl, gypsiferous marl and limestone – E1l Nummulitic limestone – Jk Conglomerate, sandstone and shale with plant Kashafrud remains and coal seams Dalichal Jd Well - bedded to thin - bedded, greenish - grey argillaceous limestone with intercalations of calcareous shale Jl Light grey, thin - bedded to massive limestone Lar Kupl Globotrunca limestone – K2l1 Hyporite bearing limestone – K2l2 Thick - bedded to massive limestone – Ktzl Thick bedded to massive, white to pinkish orbitolina Tizkuh bearing limestone Mm,s,l Marl, calcareous sandstone, sandy limestone and – minor conglomerate Mc Red conglomerate and sandstone – PeEz Reef-type limestone and gypsiferous marl Ziarat Pd Red sandstone and shale with subordinate sandy Dorud limestone Plc Polymictic conglomerate and sandstone – Pr Dark grey medium - bedded to massive limestone – PeEm Marl and gypsiferous marl locally gypsiferous – mudstone Qm Swamp and marsh – Qft1 High level piedmont fan and valley terrace deposits – Qft2 Low level piedmont fan and valley terrace deposits – TRJs Dark grey shale and sandstone Shemshak TRe1 Thin bedded, yellow to pinkish argillaceous – limestone with worm tracks Elikah TRe Thick bedded grey oolitic limestone; thin - platy, yellow to pinkish shaly limestone with worm tracks and well to thick - bedded dolomite and dolomitic limestone

In order to prepare FHSM with the TOPSIS and VIKOR methods, 500 points were first randomly determined at the surface of the study area (Fig. 5), using the Create Random Point tool in ArcGIS10.5. In the next step, the values of each of the criteria (FRCFs) for each point were

Numerical values

Preferences (judging verbal)

9 7 5 3 1 8, 6, 4, 2

Extremely preferred Very strongly preferred Strongly preferred Moderately referred Equally preferred Intervals between strong preferences

extracted using the Extract Multi Values to Point tool. The decision matrix was then made up, with 12 columns (criteria) and 500 rows (alternatives). In the next step, the TOPSIS model and VIKOR model were applied in SPSS and the weight of each point was obtained. In the last step, the FHSM was obtained using the Kriging method of interpolation in ArcGIS10.5. 2.7. Validation of models The ROC values express the model's ability to correctly distinguish between positive and negative observations in the validation sample. High sensitivity reflects a large number of correct predictions (true positives), while high 1 − specificity values indicate a high number of false positives. In the AUC, the false positive rate (1 − specificity) is shown on the x axis (Eq. (8)) and the true positive rate (sensitivity) on the y axis (Eq. (9)):  x ¼ 1−specificity ¼ 1−

TN ðTN þ FPÞ

ð8Þ

 y ¼ sensitivity ¼

TN ðTP þ FNÞ

ð9Þ

where TN is true negative, FP is false positive, TP is true positive and FN is false negative (Swets, 1988). The quantitative-qualitative relationship between AUC and prediction accuracy, which ranges from 0 to 1, is as follows: Excellent (0.9–1), very good (0.8–0.9), good (0.7–0.8), moderate (0.6–0.7) and weak (0.5–0.6) (Yesilnacar and Topal, 2005). FR and SCAI are two common indicators for analysis of the classification accuracy of models (Ilinca and Gheuca, 2011). Values of these indicators are inverse. With increasing susceptibility of classes, an increase in the value of FR and a decrease in the value of SCAI indicate increasing accuracy classification of models.

Table 2 Overview of factors used for flood hazard susceptibility mapping. Factor

Range Min

Elevation Slope Aspect Plan curvature TWI SPI Distance to stream Drainage density NDVI Soil type

Classes

Method

Max

−41 0.00 −1

3725 1. (b405 m), 2. (405–907 m), 3. (907–1431 m), 5. (1431–2131), 5. (N2131 m) 72.45 1. (b6.8°), 2. (6.8–15°), 3. (15–23.2°), 4. (23.2–33.2°), 5. (N33.2°) 360 1. Flat (−1°), 2. North (337.5–360°, 0–22.5°), 3. Northeast (22.5–67.5°), 4. East (67.5–112.5°), 5. Southeast (112.5–157.5°), 6. South (157.5–202.5°), 7. Southwest (202.5–247.5°), 8. West (247.4–292.5°), and 9. Northwest (292.5–337.5°) −14.77 13.6 1. Concave (b−0.05), 2. Flat (−0.05–0.05), 3. Convex (N0.5) 1.19 6.33 0

22.67 25.57 3360

1. (b4.9), 2. (4.9–7.3), 3. (7.3–11.2), 4. (N11.2) 1. (b9.2), 2. (9.2–11.2), 3. (11.2–13), 4. (13–15.8) 5. (N15.8) 1. (b237 m), 2. (237–487.5 m), 3. (487.5–751 m), 4. (751–1080.5 m) 5. (N1080.5 m) 2

2

2

2

Natural break Natural break Equal interval Natural break Natural break Natural break

0

1.820

1. b0.58 (km/km ), 2. 0.58–0.79 (km/km ), 3. 0.79–1.07 (km/km ) 4. N1.07 (km/km )

Natural break

−0.18 –

0.63 –

1. (b0.26), 2. (0.26–0.42), 3. (N0.42) 1. (Coastal sands), 2. (Rock outcrops/entisols), 3. (Alfisols), 4. (Inceptisols), 5. (Mollisols)

Lithology

–

–

1. (Group A), 2. (Group B), 3. (Group C), 4. (Group D), 5. (Group E), 6. (Group F), 7. (Group G), 8. (Group H), 9. (Group I).

LU/LC

–

–

1. (Agriculture), 2. (Airport), 3. (Orchard), 4. (Dense forest), 5. (Good range), 6. (Low forest), 7. (Agri-Orchard), 8. (Agri-Dry farming), 9. (Dry farming), 10. (Mod-forest), 11. (Mod-range), 12. (Urban), 13. (Water).

Natural break Supervised classification Lithological units Supervised classification

450

A. Arabameri et al. / Science of the Total Environment 660 (2019) 443–458

Fig. 5. The 500 random points for extraction of 12 flood conditioning factors.

3. Results The results of the collinearity test (Table 4) showed that there was no collinearity among the parameters, with values of TOL and VIF of 0.212–0.915 and 1.04–4.17, respectively. Therefore, all the parameters were used for modelling. 3.1. Applying the EBF and FR statistical models The statistical relationship between FRCFs and floods that have occurred in the past (FIM) can be analysed using four indicators (Belief,

Table 4 Multi-collinearity of flood conditioning factors. Factors

Collinearity statistics Tolerance

Elevation Slope degree Slope aspect Drainage density Distance to stream Lithology LU/LC NDVI Plan curvature Soil type SPI TWI

0.333 0.212 0.878 0.764 0.735 0.423 0.423 0.586 0.789 0.712 0. 915 0.609

VIF 3.001 4.712 1.140 1.308 1.361 2.363 2.363 1.706 1.267 1.404 1.040 1.646

Disbelief, Uncertainty and plausibility) of the EBF model and FR model. The results are shown in Table 5. If the values of FR for a class are equal to 1, it represents the average probability of a flood occurring in that class, while values N1 represent a strong probability and values lower than 1 represent a weak probability. In this study, the Belief (Bel) indicator of EBF was used for flood occurrence analysis. High values of Bel can indicate a higher probability of flood occurrence. Based on elevation factor, probability of flood occurrence in class b405 m (Bel = 0.86 and FR = 3.61) was higher than other classes, while areas with elevation higher than 1431 m had Bel and FR values = 0. These results indicate that the probability of flooding occurring decreases with increasing elevation. The results of the analysis of the slope parameter classes using EBF and FR showed that areas with low slopes had the highest sensitivity to floods occurring and sensitivity decreased with increasing slope. Thus the class b6.8° (Bel 0.83 and FR 3.63) had the highest sensitivity and the class N33.2° has the lowest susceptibility to flood occurrence. Based on the aspect parameter, aspects facing west, northwest and flat (Bel = 0.21, 0.19 and 0.14; FR = 1.97, 1.78 and 1.33, respectively) had the greatest susceptibility to flood occurrence. In the case of plan curvature, flat areas (Bel = 0.75 and FR = 4.55) had a stronger correlation with floods in the study area compared with concave (Bel = 0.14 and FR = 0.84) and convex areas (Bel = 0.11 and FR = 0.66). For TWI, the areas with the highest rate of this indicator were more sensitive than the rest of the area to floods occurring. For SPI, areas with the highest values had the lowest sensitivity to flooding and the probability of occurrence in these areas was very low. The TWI class N11.2 (Bel = 0.67 and FR = 7.76) and the SPI class b9.2 (Bel = 0.54 and FR = 5.61) had the highest sensitivity to flood occurrence. In the case of distance to stream, areas with the least distance from streams were more sensitive to flooding and the sensitivity decreased with increasing distance from the streams, which indicates the importance of this parameter in the occurrence of flooding. The class b237 m (Bel = 0.78, FR = 2.66) had a strong connection with flood occurrence. The results for the drainage parameter showed that it had a direct relation with the occurrence of flooding, with the sensitivity to flooding increasing with increases in this parameter, and vice versa. The classes b0.58 km/km2 and N 1.07 km/km2 (Bel = 0.11 and 0.37; FR = 0.49 and 1.43, respectively) showed the lowest and highest sensitivity, respectively, to flood occurrence. Results for the NDVI factor showed that the class 0.26–0.42 (Bel = 0.66 and FR = 3.14) had a strong relationship with flood occurrence in the study area. The results of the FR and EBF models for classes of soil type showed that Mollisols and Alfisols (Bel = 0.57 and 0.33; FR = 1.65 and 0.95, respectively) had higher sensitivity to flooding than other classes. Based on the lithology factor, class H including Qm (Swamp and marsh), Qft1 (High-level piedmont fan and valley terrace deposits) and Qft2 (Low-level piedmont fan and valley terrace deposits) has the highest Bel (0.46) and FR (3.32) and thus the strongest relationship with flood occurrence. Finally for the LU/LC factor, water and urban areas (Bel = 0.33 and 0.25; FR = 9.7 and 7.15, respectively) had the greatest impact on flood events. Construction in urban areas reduces the permeability of the surface and, as a result, the amount of runoff increases and thus the probability of flood events increases. The values obtained from the four functions of the EBF model in FHSM are shown in Fig. 6. As can be seen from the diagram, the Bel function values ranged from 0.41 to 6.8 and the Dis function values from 1.3 to 3.3. The values of Bel and Dis are inverse, so that areas where values of Bel are high have low values of Dis. In this study, the Bel function was used for FHSM and values of Bel were divided into five classes based on the natural break method (Fig. 7a): 0.41–1.59 (very low), 1.59–2.39 (low), 2.39–3.37 (moderate), 3.37–4.48 (high) and 4.48–6.81 (very high). The FR values obtained for FHSM ranged from 2.56 to 45.72. These values were divided into five classes using the natural break method (Fig. 7b): 2.56–8.31 (very low), 8.31–12.54 (low), 12.54–19.15 (moderate), 19.15–26.43 (high) and 26.43–45.72 (very high). The results for

A. Arabameri et al. / Science of the Total Environment 660 (2019) 443–458

451

Table 5 Spatial relation between flood conditioning factors and flood locations using evidential belief function and frequency ratio models. Factors

Elevation (m)

Slope (°)

Aspect

Curvature (100/m)

TWI (100/m)

SPI (100/m)

Dis to stream (m)

Drainage density (km/km2)

NDVI

Soil type

Lithology

LU/LC

Value

b405 405–907 907–1431 1431–2131 N2131 b6.8 6.8–15 15–23.2 23.2–33.2 N33.2 F N NE E SE S SW W NW Concave Flat Convex b4.9 4.9–7.3 7.3–11.2 N11.2 b9.2 9.2–11.2 11.2–13 13–15.8 N15.8 b237 237–487.5 487.5–751 751–1080.5 N1080.5 b0.58 0.58–0.79 0.79–1.07 N1.07 b0.26 0.26–0.42 N0.42 Coastal sands Rock outcrops/entisols Alfisols Inceptisols Mollisols A B C D E F J H I Agriculture (A) Airport (B) Orchard (C) Dense forest (D) Good range (E) Low forest (F) Agri-orchard (J) Agri-dryfarming (H) Dryfarming (I) Mod-forest (J) Mod-range (K) Urban (L) Water (M)

Pixels in domain

Flooding pixels

No.

%

No.

%

928,645 1,250,779 1,067,605 562,117 244,549 898,251 1,142,614 996,869 718,958 297,002 458,075 927,893 385,930 331,135 346,463 397,631 375,257 376,231 416,739 1,839,689 271,058 1,942,947 2,091,128 1,334,130 526,254 102,183 451,301 1,471,257 1,407,707 597,710 125,720 1,343,570 1,065,296 856,850 604,634 183,344 812,216 1,712,256 1,166,577 362,646 769,813 786,533 2,497,348 1631 354,024 2,363,563 290,189 1,044,254 38,947 127,273 70,750 270,227 554,624 1,259,743 631,617 557,941 542,573 377,603 714 100,868 2,604,689 155,312 53,807 160,715 36,590 376,860 117,576 39,760 24,649 4544

22.91 30.86 26.34 13.87 6.03 22.16 28.19 24.59 17.74 7.33 11.41 23.11 9.61 8.25 8.63 9.90 9.35 9.37 10.38 45.38 6.69 47.93 51.59 32.91 12.98 2.52 11.13 36.29 34.73 14.74 3.10 33.14 26.28 21.14 14.92 4.52 20.04 42.24 28.78 8.95 18.99 19.40 61.61 0.04 8.73 58.31 7.16 25.76 0.96 3.14 1.75 6.67 13.68 31.08 15.58 13.76 13.38 9.32 0.02 2.49 64.25 3.83 1.33 3.96 0.90 9.30 2.90 0.98 0.61 0.11

76 13 3 0 0 74 11 6 1 0 14 15 10 4 3 3 9 17 17 35 28 29 7 39 28 18 34 30 9 3 16 81 5 3 2 1 9 34 36 13 25 56 11 0 0 51 2 39 0 1 0 7 9 19 12 42 2 31 0 3 24 0 0 16 0 9 4 0 4 1

82.61 14.13 3.26 0.00 0.00 80.43 11.96 6.52 1.09 0.00 15.22 16.30 10.87 4.35 3.26 3.26 9.78 18.48 18.48 38.04 30.43 31.52 7.61 42.39 30.43 19.57 36.96 32.61 9.78 3.26 17.39 88.04 5.43 3.26 2.17 1.09 9.78 36.96 39.13 14.13 27.17 60.87 11.96 0.00 0.00 55.43 2.17 42.39 0.00 1.09 0.00 7.61 9.78 20.65 13.04 45.65 2.17 33.70 0.00 3.26 26.09 0.00 0.00 17.39 0.00 9.78 4.35 0.00 4.35 1.09

FR

3.61 0.46 0.12 0.00 0.00 3.63 0.42 0.27 0.06 0.00 1.33 0.71 1.13 0.53 0.38 0.33 1.05 1.97 1.78 0.84 4.55 0.66 0.15 1.29 7.76 3.32 0.90 0.28 0.22 5.61 2.66 0.21 0.15 0.15 0.24 0.49 0.87 1.36 1.58 1.43 3.14 0.19 0.00 0.00 0.95 0.30 1.65 0.00 0.35 0.00 1.14 0.72 0.66 0.84 3.32 0.16 3.62 0.00 1.31 0.41 0.00 0.00 4.39 0.00 1.05 1.50 0.00 7.15 9.70

EBF Bel

Dis

Unc

PLS

0.86 0.11 0.03 0.00 0.00 0.83 0.10 0.06 0.01 0.00 0.14 0.08 0.12 0.06 0.04 0.04 0.11 0.21 0.19 0.14 0.75 0.11 0.01 0.11 0.20 0.67 0.32 0.09 0.03 0.02 0.54 0.78 0.06 0.05 0.04 0.07 0.11 0.20 0.32 0.37 0.30 0.66 0.04 0.00 0.00 0.33 0.10 0.57 0.00 0.05 0.00 0.16 0.10 0.09 0.12 0.46 0.02 0.12 0.00 0.04 0.01 0.00 0.00 0.15 0.00 0.04 0.05 0.00 0.25 0.33

0.05 0.25 0.26 0.23 0.21 0.05 0.25 0.25 0.24 0.22 0.11 0.12 0.11 0.12 0.12 0.12 0.11 0.10 0.10 0.36 0.23 0.41 0.43 0.20 0.18 0.19 0.14 0.21 0.27 0.22 0.17 0.04 0.26 0.25 0.24 0.21 0.28 0.27 0.21 0.23 0.24 0.13 0.62 0.20 0.22 0.21 0.21 0.16 0.11 0.11 0.11 0.11 0.12 0.13 0.11 0.07 0.13 0.05 0.07 0.07 0.15 0.08 0.07 0.06 0.07 0.07 0.07 0.07 0.07 0.07

0.09 0.64 0.71 0.77 0.79 0.12 0.66 0.69 0.75 0.78 0.75 0.80 0.77 0.83 0.84 0.85 0.78 0.69 0.71 0.51 0.01 0.48 0.55 0.69 0.61 0.14 0.54 0.71 0.70 0.76 0.29 0.18 0.68 0.70 0.72 0.72 0.61 0.52 0.47 0.40 0.46 0.21 0.34 0.80 0.78 0.46 0.68 0.28 0.89 0.84 0.89 0.73 0.78 0.78 0.77 0.47 0.85 0.82 0.93 0.88 0.83 0.92 0.93 0.79 0.93 0.89 0.88 0.93 0.68 0.59

0.95 0.75 0.74 0.77 0.79 0.95 0.75 0.75 0.76 0.78 0.89 0.88 0.89 0.88 0.88 0.88 0.89 0.90 0.90 0.64 0.77 0.59 0.57 0.80 0.82 0.81 0.86 0.79 0.73 0.78 0.83 0.96 0.74 0.75 0.76 0.79 0.72 0.73 0.79 0.77 0.76 0.87 0.38 0.80 0.78 0.79 0.79 0.84 0.89 0.89 0.89 0.89 0.88 0.87 0.89 0.93 0.87 0.95 0.93 0.93 0.85 0.92 0.93 0.94 0.93 0.93 0.93 0.93 0.93 0.93

452

A. Arabameri et al. / Science of the Total Environment 660 (2019) 443–458

Fig. 6. Function results of evidential belief function model in flood susceptibility mapping.

the EBF model (Table 6) indicated that much (41.43%) of the study area was located in the very low susceptibility class and the smallest area (7.31%) was located in the very high susceptibility class, followed by high (8.76%), moderate (13.87%) and low (28.63%). Similarly to the EBF model, the results for the FR model indicated that

increasing class sensitivity was associated with smaller area, so that the very low sensitivity class was associated with the largest area (41.89%), and the very sensitive class had the smallest area (6.72%), followed by high (28.96%), moderate (14.18%) and low (8.24%).

A. Arabameri et al. / Science of the Total Environment 660 (2019) 443–458

453

Fig. 7. Flood susceptibility mapping. a) evidential belief function (BEL), b) frequency ratio (FR), c) TOPSIS, d) VIKOR.

3.2. Applying the TOPSIS and VIKOR MCDM models The results of determining the weight of FRCFs using the AHP method are shown in Fig. 8. Based on these results, the parameters

slope (0.253), distance to stream (0.163) and LU/LC (0.115) had the greatest impact on the occurrence of floods in the study area. The parameters slope aspect (0.022), TWI (0.027) and SPI (0.032) had the least effect. The parameters lithology (0.095), drainage density

454

A. Arabameri et al. / Science of the Total Environment 660 (2019) 443–458

Table 6 Areas of susceptibility classes along with FR and SCAI indicators in models. Model

Classes

EBF

Very low Low Moderate High Very high Very low Low Moderate High Very high Very low Low Moderate High Very high Very low Low Moderate High Very high

FR

TOPSIS

VIKOR

Pixels in domain

Flood

No

%

No

%

1,679,399 1,160,739 562,136 355,112 296,270 1,698,027 1,174,068 574,970 334,127 272,467 801,912 821,395 801,055 851,917 777,415 781,585 812,920 819,072 824,572 815,545

41.43 28.63 13.87 8.76 7.31 41.89 28.96 14.18 8.24 6.72 19.78 20.26 19.76 21.02 19.18 19.28 20.05 20.21 20.34 20.12

1 1 8 43 79 1 1 15 50 65 7 11 13 21 80 28 15 23 12 54

0.76 0.76 6.06 32.58 59.85 0.76 0.76 11.36 37.88 49.24 5.30 8.33 9.85 15.91 60.61 21.21 11.36 17.42 9.09 40.91

Table 7 Calculation of the weight of sample points using TOPSIS and VIKOR model.

FR

SCAI

0.02 0.03 0.44 3.72 8.19 0.02 0.03 0.80 4.60 7.33 0.27 0.41 0.50 0.76 3.16 1.10 0.57 0.86 0.45 2.03

54.69 37.80 2.29 0.27 0.12 55.29 38.23 1.25 0.22 0.14 3.73 2.43 2.01 1.32 0.32 0.91 1.76 1.16 2.24 0.49

(0.074), elevation (0.063), soil type (0.054), NDVI (0.051), and plan curvature (0.046) were intermediate. The results of determining the weight of sample points from 500 random points using the TOPSIS and VIKOR models are shown in Table 7. After interpolation and FHSM using TOPSIS and VIKOR, the flood map was divided into five classes, from very low to very high, using the natural break method. In TOPSIS (Fig. 7c) and VIKOR (Fig. 7d), the susceptibility classes were defined as very low, low, moderate, high and very high. With the TOPSIS model, 19.79%, 20.26%, 19.76%, 21.02% and 19.18% of the study area was located in very low, low, moderate, high, and very high susceptibility classes, while with the VIKOR model, 19.28%, 20.05%, 20.21%, 20.34%, and 20.12% of the study area fell into these respective classes (Table 6).

TOPSIS

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38

VIKOR

di+

di−

cli+

S

R

Q

0.0017 0.0016 0.0019 0.0021 0.0018 0.0019 0.0017 0.0018 0.0016 0.0021 0.0016 0.0018 0.0018 0.0017 0.0010 0.0015 0.0018 0.0021 0.0019 0.0018 0.0016 0.0018 0.0019 0.0017 0.0021 0.0014 0.0019 0.0017 0.0018 0.0016 0.0019 0.0016 0.0020 0.0010 0.0018 0.0017 0.0016 0.0019

0.0030 0.0032 0.0025 0.0023 0.0029 0.0023 0.0028 0.0026 0.0028 0.0025 0.0031 0.0028 0.0026 0.0030 0.0032 0.0028 0.0024 0.0027 0.0026 0.0027 0.0028 0.0026 0.0026 0.0031 0.0021 0.0027 0.0027 0.0030 0.0026 0.0029 0.0025 0.0026 0.0024 0.0033 0.0028 0.0030 0.0029 0.0028

0.6369 0.6619 0.5674 0.5200 0.6109 0.5445 0.6160 0.5922 0.6295 0.5402 0.6679 0.6034 0.5852 0.6400 0.7615 0.6498 0.5673 0.5632 0.5764 0.6018 0.6338 0.5909 0.5755 0.6487 0.5012 0.6582 0.5913 0.6480 0.5985 0.6390 0.5595 0.6156 0.5452 0.7614 0.6127 0.6334 0.6433 0.5970

0.3827 0.3671 0.5517 0.5920 0.4982 0.5629 0.4663 0.4575 0.4898 0.6021 0.3750 0.4748 0.4696 0.4215 0.2476 0.3263 0.5153 0.5632 0.4465 0.4507 0.4131 0.4786 0.5574 0.4133 0.6457 0.4378 0.4474 0.2725 0.4980 0.3797 0.5119 0.4234 0.5742 0.2209 0.4520 0.4446 0.3678 0.5213

0.3827 0.3671 0.5517 0.5920 0.4982 0.5629 0.4663 0.4575 0.4898 0.6021 0.3750 0.4748 0.4696 0.4215 0.2476 0.3263 0.5153 0.5632 0.4465 0.4507 0.4131 0.4786 0.5574 0.4133 0.6457 0.4378 0.4474 0.2725 0.4980 0.3797 0.5119 0.4234 0.5742 0.2209 0.4520 0.4446 0.3678 0.5213

0.6344 0.6608 0.3479 0.2795 0.4386 0.3289 0.4926 0.5076 0.4528 0.2624 0.6475 0.4782 0.4871 0.5687 0.8635 0.7300 0.4096 0.3284 0.5263 0.5192 0.5829 0.4719 0.3382 0.5825 0.1884 0.5410 0.5248 0.8213 0.4389 0.6394 0.4153 0.5654 0.3098 0.9088 0.5169 0.5294 0.6596 0.3994

3.3. Validation of models The AUC results (Fig. 9) showed that the EBF model, with prediction rate 0.987 and success rate 0.946, gave higher prediction accuracy that the FR, TOPSIS and VIKOR models. Based on the FR and SCAI indicators

Lithology 0.3 TWI

NDVI 0.25 0.2 Aspect

Elevation

0.15 0.1 0.05

SPI

Dis to stream

0

Soil type

LU/LC

Drainage density

Slope Plan curvature

Fig. 8. Weight of flood conditioning factors using AHP model.

(Table 6), all models except VIKOR showed acceptable accuracy of classification. 4. Discussion Predictions of geomorphological phenomena by applied researches can be used for management of these phenomena and decrease the financial and human damage they cause. Restoration and rehabilitation is more expensive and less sustainable (Keesstra et al., 2018b). One such geomorphological hazard is watershed flooding. Flood hazard susceptibility mapping (FHSM) is the first step in preventing and managing future floods (Wu et al., 2010). In general, FHSM involves determination of flooding-susceptible areas according to flood-related conditioning factors (FRCFs) (Liu et al., 2008; Pradhan and Youssef, 2011). Identification and zonation of watershed flooding can be effective in flood management, in controlling floods and thus reducing their damages. So far, various methodologies for FHSM have been developed by various researchers worldwide (Haghizadeh et al., 2017; Samanta et al., 2018; Zhao et al., 2018; Shafapour Tehrany and Kumar, 2018), each with disadvantages and advantages. It should be noted that the proposed methodology should be simple and at the same time highly accurate. The results obtained by applied models are influenced by the local hydrological, geological, hydrogeological and morphological conditions, and researchers tend to adapt and calibrate them to obtain the optimal solution in a specific study area (Shafapour Tehrany et al., 2017b). RS and GIS are very useful and powerful tools for examining multidimensional events such as floods in which several factors are influential (Pradhan et al., 2014; Karlsson et al., 2017; Tien Bui and Hoang,

A. Arabameri et al. / Science of the Total Environment 660 (2019) 443–458

Fig. 9. AUC values of models. a) Prediction rate (validation dataset), b) success rate (training dataset).

2017; Ahlmer et al., 2018). In this study, two different methodologies using statistical and MCDM models, along with GIS and RS, were used in FHSM in the north of Iran with the mountainous environment. The need for the implementation of such models and the development of an effective flood hazard and risk assessment approach arises from the fact that floods are responsible for serious economic losses and severe damages in human infrastructure and natural ecosystem (Li et al., 2015; Chapi et al., 2017; Hong et al., 2018a, 2018b). The first methodology involved the use of FR and EBF statistical methods for FHSM. These two methods were chosen because of their efficiency and popularity in terms of environmental hazard assessment and their limited use in the field of FHSM. For the test catchment, 12 FRCFs and 132 flood locations were used. The FR, a simple model, and the EBF, a complex method, were used for flood hazard susceptibility

455

mapping. In the FR model, the input, computation and output processes are simple and understandable (Lee and Min, 2001). With the FR method, the higher the FR for a class, the greater the probability of occurrence of the flood in that class (Rahmati et al., 2016a). Although the EBF model has complex computational processes, it has many advantages, including the ability to combine certainty from the sources of various evidence and relative flexibility to accept uncertainty (Tahmassebipoor et al., 2016; Cui et al., 2017). The second methodology involved the use of the TOPSIS and VIKOR MCDM methods that had not been used previously in FHSM. The main advantage of this methodology is that it does not need flood inventory maps and is very useful and efficient in areas where data about flood events in the past are not available. The use of quantitative and qualitative criteria and the ability to convert quality criteria to quantitative, and vice versa, express the priorities in a quantitative manner, ability to consider matches and mismatches between the alternatives, ability to determine the weight of criteria using different models, simpleness and not time-consuming and ability to change the input data and analysis the system response to these changes are the most important advantages of these two models (Olson, 2004). In MCDM models, AHP are used to determine the weight of criteria (FRCFs). The advantages of this method include i) the possibility of formulating issues in a hierarchical manner, ii) paired comparison and use of expert knowledge, iii) the possibility of using quantitative and qualitative criteria, and iv) the possibility of examining the degree of compatibility and incompatibility of the decision (Saaty, 1980). The results of the AHP method showed that the parameters slope, distance to stream and LU/LC had the strongest effect on the occurrence of flooding in the study area, which is consistent with previous findings (Adiat et al., 2012; Glenn et al., 2012; Zou et al., 2013; Youssef et al., 2016; Shafapour Tehrany and Kumar, 2018). The degree of slope controls the rate of penetration of surface runoff and its volume and velocity, thus having a great impact on flooding (Adiat et al., 2012). The distance from river affects flood spread and magnitude and, as a result, is a key factor in flooding (Glenn et al., 2012; Rahmati et al., 2016a). Type of land use directly or indirectly affects many of the components of hydrological processes, such as infiltration, evapotranspiration and runoff (Di Prima et al., 2018; Rodrigo-Comino et al., 2018; Keesstra et al., 2019), and therefore plays an influential role in flood events (Kalantari et al., 2017b). The model validation using prediction and success rates showed that the first methodology (statistical models), based on floods occurring in the past, had a higher predictive accuracy in FHSM than the second methodology (MCDM models). The EBF method had higher predictive accuracy than the FR method when comparing the statistical models. Similarly, Shafapour Tehrany et al. (2017a, 2017b) compared the efficiency of the EBF and FR models when preparing a soil erosion sensitivity map for Southern Luzon in the Philippines at different time periods and concluded that the EBF model (AUC = 83.1%) had higher prediction accuracy than the FR model (AUC = 70.6%). W. Chen et al. (2017) and Z. Chen et al. (2017) evaluated the performance of three models (EBF, FR and certainty factor (CF)) in landslide susceptibility mapping in northwest China using nine landslide conditioning factors (elevation, slope, aspect, gully density, cutting depth, relief amplitude, lithology, NDVI and distance to road) and concluded that the EBF model (success rates 0.803 and prediction rate 0.805) had higher efficiency and accuracy than the other two models. Shafapour Tehrany and Kumar (2018) compared three methods (EBF, FR and LR) in the field of flood susceptibility mapping in Brisbane, Australia, using 12 environmental parameters (TWI, SPI, LU/LC, elevation, slope, aspect, plan curvature, soil, distance to river, distance to road, geology and rainfall) and also concluded that the EBF model (prediction rate 82.60%) had higher predictive accuracy than the other models. Although the second methodology developed in the present study does not use FIM in FHSM, nevertheless it can achieve very good predictive accuracy. The precise prediction of high susceptible areas by using novel methodological framework introduced

456

A. Arabameri et al. / Science of the Total Environment 660 (2019) 443–458

in this research can be used by decision makers for flood hazard management and reducing its cost and damages. Also, this methodology is adaptable and can be successfully used in other places. 5. Conclusion Flood hazard susceptibility maps are widely used in flood management studies. Today, these maps are one of the most important resources in the study of development projects world-wide and are studied by relevant bodies before investment or implementation of any development plans. In this study, two different methodological frameworks, based on statistical or MCDM approaches along with GIS and RS techniques, were tested for FHSM. Based on literature and environmental conditions in the study area, 12 flood-related conditioning factors (FRCFs) from 92 flood locations were used for modelling. The AHP model results showed that slope, distance to stream and land use play a key role in the occurrence of floods in the study catchment. Overall, the results of the study show that a combination of statistical and MCDM methods with remote sensing data and the GIS technique can be a powerful tool for flood zoning. Due to the excellent and very good accuracy of the two methodologies presented here, both can be recommended for use in areas with similar environmental conditions, especially areas where there is a shortage of data. Acknowledgement We are grateful to the Editor, Prof. Ralf Ludwig, and four anonymous referees for their constructive comments which were valuable to improve our manuscript. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2019.01.021. References Adiat, K.A.N., Nawawi, M.N.M., Abdullah, K., 2012. Assessing the accuracy of GIS-based elementary multi criteria decision analysis as a spatial prediction tool – a case of predicting potential zones of sustainable groundwater resources. J. Hydrol. 440–441, 75–89. Ahlmer, A.-K., Cavalli, M., Hansson, K., Koutsouris, A.J., Crema, S., Kalantari, Z., 2018. Soil moisture remote-sensing applications for identification of flood-prone areas along transport infrastructure. Environ. Earth Sci. 77 (14), 533. Alilou, H., Rahmati, O., Singh, V.P., Choubin, B., Pradhan, B., Keesstra, S., Sadeghi, S.H., 2019. Evaluation of watershed health using Fuzzy-ANP approach considering geoenvironmental and topo-hydrological criteria. J. Environ. Manag. 232, 22–36. Alvarado-Aguilar, D., Jiménez, J.A., Nicholls, R.J., 2012. Flood hazard and damage assessment in the Ebro Delta (NW Mediterranean) to relative sea level rise. Nat. Hazards 62, 1301–1321. Arabameri, A., Pourghasemi, H.R., 2019. Spatial modeling of gully erosion using linear and quadratic discriminant analyses in GIS and R. In: Pourghasemi, H.R., Gokceoglu, C. (Eds.), Spatial Modeling in GIS and R for Earth and Environmental Sciences, First edition Elsevier publication (796 p.). Arabameri, A., Pourghasemi, H.R., Cerda, A., 2017a. Erodibility prioritization of subwatersheds using morphometric parameters analysis and its mapping: a comparison among TOPSIS, VIKOR, SAW, and CF multi-criteria decision making models. Sci. Total Environ. 613–614, 1385–1400. Arabameri, A., Pourghasemi, H.R., Yamani, M., 2017b. Applying different scenarios for landslide spatial modeling using computational intelligence methods. Environ. Earth Sci. 76, 832. Arabameri, A., Pradhan, B., Pourghasemi, H.R., Rezaei, K., 2018a. Identification of erosionprone areas using different multi-criteria decision-making techniques and GIS. Geomatics, Natural Hazards and Risk. 9, 1129–1155. Arabameri, A., Pradhan, B., Pourghasemi, H.R., Rezaei, K., Kerle, N., 2018b. Spatial modelling of gully erosion using GIS and R programing: a comparison among three data mining algorithms. Appl. Sci. 8 (8), 1369. Arabameri, A., Rezaei, K., Pourghasemi, H.R., Lee, S., Yamani, M., 2018c. GIS-based gully erosion susceptibility mapping: a comparison among three data-driven models and AHP knowledge-based technique. Environ. Earth Sci. 77, 628. Arabameri, A., Pradhan, B., Rezaei, K., Yamani, M., Pourghasemi, H.R., Lombardo, L., 2018d. Spatial modelling of gully erosion using evidential belief function, logistic regression and a new ensemble EBF–LR algorithm. Land Degrad. Dev. 29, 4035–4049.

Arabameri, A., Pradhan, B., Rezaei, K., 2019. Gully erosion zonation mapping using integrated geographically weighted regression with certainty factor and random forest models in GIS. J. Environ. Manag. 232, 928–942. Bahremand, A., De Smedt, F., Corluy, J., Liu, Y.B., Poorova, J., Velcicka, L., et al., 2007. WetSpa model application for assessing reforestation impacts on floods in Margecany–Hornad watershed, Slovakia. Water Resour. Manag. 21, 1373–1391. Bajabaa, S., Masoud, M., Al-Amri, N., 2014. Flash flood hazard mapping based on quantitative hydrology, geomorphology and gis techniques (case study of Wadi Al-Lith, Saudi Arabia). Arab. J. Geosci. 7, 2469–2481. Billa, L., Shattri, M., Mahmud, A.R., Ghazali, A.H., 2006. Comprehensive planning and the role of SDSS in flood disaster management in Malaysia. Disaster Prev. Manag. 15, 233–240. Bubeck, P., Botzen, W.J., Aerts, J.C., 2012. A review of risk perceptions and other factors that influence flood mitigation behavior. Risk Anal. 32 (9), 1481–1495. Cao, C., Xu, P., Wang, Y., Chen, J., Zheng, L., Niu, C., 2016. Flash flood hazard susceptibility mapping using frequency ratio and statistical index methods in coalmine subsidence areas. Sustainability 8, 948. Caviedes-Voullième, D., García-Navarro, P., Murillo, J., 2012. Influence of mesh structure on 2D full shallow water equations and SCS curve number simulation of rainfall/runoff events. J. Hydrol. 448, 39–59. Chapi, K., Singh, P.V., Shirzadi, A., Shahabi, H., Tien Bui, D., Pham, T.B., Khosravi, K., 2017. A novel hybrid artificial intelligence approach for flood susceptibility assessment. Environ. Model. Softw. 95, 229–245. Chen, Y.R., Yeh, C.H., Yu, B., 2011. Integrated application of the analytic hierarchy process and the geographic information system for flood risk assessment and flood plain management in Taiwan. Nat. Hazards 59 (3), 1261–1276. Chen, W., Xie, X., Wang, J., Pradhan, B., Hong, H., Bui, D.T., et al., 2017. A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. Catena 151, 147–160. Chen, Z., Liang, S., Ke, Y., Yang, Z., Zhao, H., 2017. Landslide susceptibility assessment using evidential belief function, certainty factor and frequency ratio model at Baxie River basin, NW China. Geocarto Int. 1–20. Cloke, H., Pappenberger, F., 2009. Ensemble flood forecasting: a review. J. Hydrol. 375 (3), 613–626. Cui, K., Lu, D., Li, W., 2017. Comparison of landslide susceptibility mapping based on statistical index, certainty factors, weights of evidence and evidential belief function models. Geocarto Int. 32 (9), 935–955. Dang, N.M., Babel, M.S., Luong, H.T., 2011. Evaluation of food risk parameters in the day river flood diversion area, Red River delta, Vietnam. Nat. Hazards 56 (1), 169–194. Dempster, A.P., 1968. Generalization of Bayesian inference. J. R. Stat. Soc. B 30, 205–247. Di Prima, S., Rodrigo-Comino, J., Novara, A., Iovino, M., Pirastru, M., Keesstra, S., Cerdà, A., 2018. Soil physical quality of citrus orchards under tillage, herbicide, and organic managements. Pedosphere 28 (3), 463–477. Ettinger, S., Mounaud, L., Magill, C., Yao-Lafourcade, A.F., Thouret, J.C., Manville, V., et al., 2016. Building vulnerability to hydro-geomorphic hazards: estimating damage probability from qualitative vulnerability assessment using logistic regression. J. Hydrol. 541, 563–581. Geology Survey of Iran (GSI), 1997. http://www.gsi.ir/Main/Lang_en/index.html. Ghosh, S., Carranza, E.J.M., 2010. Spatial analysis of mutual fault/fracture and slope controls on rocksliding in Darjeeling Himalaya, India. Geomorphology 122, 1–24. Glenn, E., Morino, K., Nagler, P., Murray, R., Pearlstein, S., Hultine, K., 2012. Roles of saltcedar (Tamarix spp.) and capillary rise in salinizing a non-flooding terrace on a flow-regulated desert river. J. Arid Environ. 79, 56–65. Haghizadeh, A., Siahkamari, S., Haghiabi, A.H., Rahmati, O., 2017. Forecasting flood-prone areas using Shannon's entropy model. J. Earth Syst. Sci. 126, 39. Hazbavi, Z., Keesstra, S.D., Nunes, J.P., Baartman, J.E., Gholamalifard, M., Sadeghi, S.H., 2018. Health comparative comprehensive assessment of watersheds with different climates. Ecol. Indic. 93, 781–790. Hong, H., Panahi, M., Shirzadi, A., Ma, T., Liu, J., Zhu, A.X., Chen, W., Kougias, L., Kazakis, N., 2018a. Flood susceptibility assessment in Hengfeng area coupling adaptive neurofuzzy inference system with genetic algorithm and differential evolution. Sci. Total Environ. 621, 1124–1141. Hong, H., Tsangaratos, P., Ilia, I., Liu, J., Zhu, A.X., Chen, W., 2018b. Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China. Sci. Total Environ. 625, 575–588. Hwang, C.L., Yoon, K., 1981. Multiple Attribute Decision Making: Methods and Application. Springer, New York. I.R. of Iran Meteorological Organization (IRIMO), 2012. Available online. http://www. mazandaranmet.ir. Ilinca, V., Gheuca, I., 2011. The red lake landslide (Ucigau Mountain, Romania). Carpathian J. Earth Environ. 23, 263–272. Kalantari, Z., Nickman, A., Lyon, S.W., Olofsson, B., Folkeson, L., 2014. A method for mapping flood hazard along roads. J. Environ. Manag. 133, 69–77. Kalantari, Z., Cavalli, M., Cantone, C., Crema, S., Destouni, G., 2017a. Flood probability quantification for road infrastructure: data-driven spatial-statistical approach and case study applications. Sci. Total Environ. 581–582, 386–398. Kalantari, Z., Ferreira, C.S.S., Walsh, R.P.D., Ferreira, A.J.D., Destouni, G., 2017b. Urbanization development under climate change: hydrological responses in a peri-urban Mediterranean catchment. Land Degrad. Dev. 28 (7), 2207–2221. Kalantari, Z., Ferreira, C.S.S., Keesstra, S., Estouni, G., 2018. Ecosystem-based solutions for flood-drought risk mitigation in vulnerable urbanizing parts of East-Africa. Curr. Opin. Environ. Sci. Health 5, 73–78. Karlsson, C.S.J., Kalantari, Z., Mörtberg, U., Olofsson, B., Lyon, S.W., 2017. Natural hazard susceptibility assessment for road planning using spatial multi-criteria analysis. Environ. Manag. 60 (5), 823–851.

A. Arabameri et al. / Science of the Total Environment 660 (2019) 443–458 Kazakis, N., Kougias, I., Patsialis, T., 2015. Assessment of flood hazard areas at a regional scale using an index-based approach and analytical hierarchy process: application in Rhodope–Evros region, Greece. Sci. Total Environ. 538, 555–563. Keesstra, S.D., 2007. Impact of natural reforestation on floodplain sedimentation in the Dragonja basin, SW Slovenia. Earth Surf. Proc. Landf. J. Br. Geomorphol. Res. Group 32 (1), 49–65. Keesstra, S.D., Van Dam, O., Verstraeten, G.V., Van Huissteden, J., 2009. Changing sediment dynamics due to natural reforestation in the Dragonja catchment, SW Slovenia. Catena 78 (1), 60–71. Keesstra, S.D., Temme, A.J.A.M., Schoorl, J.M., Visser, S.M., 2014. Evaluating the hydrological component of the new catchment-scale sediment delivery model LAPSUS-D. Geomorphology 212, 97–107. Keesstra, S., Nunes, J.P., Saco, P., Parsons, T., Poeppl, R., Masselink, R., Cerdà, A., 2018a. The way forward: can connectivity be useful to design better measuring and modelling schemes for water and sediment dynamics? Sci. Total Environ. 644, 1557–1572. Keesstra, S., Nunes, J., Novara, A., Finger, D., Avelar, D., Kalantari, Z., Cerdà, A., 2018b. The superior effect of nature based solutions in land management for enhancing ecosystem services. Sci. Total Environ. 610, 997–1009. Keesstra, S.D., Rodrigo-Comino, J., Novara, A., Giménez-Morera, A., Pulido, M., Di Prima, S., Cerdà, A., 2019. Straw mulch as a sustainable solution to decrease runoff and erosion in glyphosate-treated clementine plantations in Eastern Spain. An assessment using rainfall simulation experiments. Catena 174, 95–103. Khosravi, K., Nohani, E., Maroufinia, E., Pourghasemi, H.R., 2016a. A GIS-based flood susceptibility assessment and its mapping in Iran: a comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision-making technique. Nat. Hazards 83, 947–987. Khosravi, K., Pourghasemi, H.R., Chapi, K., Bahri, M., 2016b. Flash flood susceptibility analysis and its mapping using different bivariate models in Iran: a comparison between Shannon's entropy, statistical index, and weighting factor models. Environ. Monit. Assess. 188, 656. Kia, M.B., Pirasteh, S., Pradhan, B., Mahmud, A.R., Sulaiman, W.N.A., Moradi, A., 2012. An artificial neural network model for flood simulation using GIS: Johor River basin, Malaysia. Environ. Earth Sci. 67 (1), 251–264. Kourgialas, N.N., Karatzas, G.P., 2017. A national scale flood hazard mapping methodology: the case of Greece — protection and adaptation policy approaches. Sci. Total Environ. 601-602, 441–452. Lee, S., Min, K., 2001. Statistical analyses of landslide susceptibility at Yongin, Korea. Environ. Geol. 40, 1095–1113. Lee, M.J., Kang, J.E., Jeon, S., 2012. Application of frequency ratio model and validation for predictive flooded area susceptibility mapping using GIS. Paper presented at the 2012 IEEE International Geoscience and Remote Sensing Symposium. Li, X., Zhang, Q., Xu, C., Ye, X., 2015. The changing patterns of floods in Poyang Lake, China: characteristics and explanations. Nat. Hazards 76, 651–666. Liu, J., Li, J., Liu, J., Cao, R., 2008. Integrated GIS/AHP-based flood risk assessment: a case study of Huaihe River basin in China. J. Nat. Disasters 17 (6), 110–114. Liu, R., Chen, Y., Wu, J., Gao, L., Barrett, D., Xu, T., Li, L., Huang, C., Yu, J., 2015. Assessing spatial likelihood of flooding hazard using naïve Bayes and GIS: a case study in Bowen Basin, Australia. Stoch. Stoch. Environ. Res. Risk Assess. 30, 1575–1590. Liu, K., Li, Z., Yao, C., Chen, J., Zhang, K., Saifullah, M., 2016. Coupling the k-nearest neighbour procedure with the Kalman filter for real-time updating of the hydraulic model in flood forecasting. Int. J. Sediment Res. 31, 149–158. Malczewski, J., 2006. GIS-based multicriteria decision analysis: a survey of the literature. Int. J. Geogr. Inf. Sci. 20 (7), 703–726. Manandhar, B., 2010. Flood Plain Analysis and Risk Assessment of Lothar Khola. MSc Thesis. Tribhuvan University, Phokara, Nepal (64p.). Masselink, R., Temme, A.J.A.M., Giménez, R., Casalí, J., Keesstra, S.D., 2017. Assessing hillslope-channel connectivity in an agricultural catchment using rare-earth oxide tracers and random forests models. Cuad. Investig. Geogr. 43 (1), 17–39. Mojaddadi, H., Pradhan, B., Nampak, H., Ahmad, N., Ghazali, A.H.b., 2017. Ensemble machine-learning-based geospatial approach for flood risk assessment using multisensor remote-sensing data and GIS. Geomatic Nat. Hazard Risk 8, 1080–1102. Nampak, H., Pradhan, B., Manap, M.A., 2014. Application of GIS based data driven evidential belief function model to predict groundwater potential zonation. J. Hydrol. 513, 283–300. Nandi, A., Mandal, A., Wilson, M., Smith, D., 2016. Flood hazard mapping in Jamaica using principal component analysis and logistic regression. Environ. Earth Sci. 75, 465. Nikoo, M., Ramezani, F., Hadzima-Nyarko, M., Nyarko, E.K., Nikoo, M., 2016. Flood routing modeling with neural network optimized by social-based algorithm. Nat. Hazards 82 (1), 1–24. Norouzi, G., Taslimi, M., 2012. The impact of flood damages on production of Iran's agricultural sector. Middle East J. Sci. Res. 12, 921–926. Oeurng, C., Sauvage, S., Sánchez-Pérez, J.-M., 2011. Assessment of hydrology, sediment and particulate organic carbon yield in a large agricultural catchment using the SWAT model. J. Hydrol. 401, 145–153. Olson, D.L., 2004. Comparison of weights in TOPSIS models. Math. Comput. Model. 40, 21–727. Opricovic, S., 1998. Multicriteria optimization of civil engineering systems. Fac. Civ. Eng. Belgrade 2, 5–21. Opricovic, S., Tzeng, G.H., 2002. Multicriteria planning of post-earthquake sustainable reconstruction. Comput. Aided Civ. Infrastruct. Eng. 17, 211–220. Paquette, J., Lowry, J., 2013. Flood hazard modelling and risk assessment in the Nadi River basin, Fiji, using GIS and MCDA. S. Pac. J. Nat. Appl. Sci. 30 (1), 33–43. Perucca, L.P., Angilieri, Y.E., 2011. Morphometric characterization of del molle basin applied to the evaluation of flash floods hazard, Iglesia department, San Juan, Argentina. Quat. Int. 233, 81–86.

457

Pourghasemi, H.R., Pradhan, B., Gokceoglu, C., Mohammadi, M., Moradi, H.R., 2013. Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arab. J. Geosci. 6, 2351–2365. Pradhan, B., 2010. Flood susceptible mapping and risk area estimation using logistic regression, GIS and remote sensing. J. Spat. Hydrol. 9, 1–18. Pradhan, B., 2013. A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput. Geosci. 51, 350–365. Pradhan, B., Youssef, A., 2011. A 100-year maximum flood susceptibility mapping using integrated hydrological and hydrodynamic models: Kelantan River Corridor, Malaysia. J. Flood Risk Manag. 4 (3), 189–202. Pradhan, B., Hagemann, U., Shafapour Tehrany, M., Prechtel, N., 2014. An easy to use arcmap based texture analysis program for extraction of flooded areas from terrasar-x satellite image. Comput. Geosci. 63, 34–43. Rahmati, O., Nazari Samani, A., Mahmoodi, N., Mahdavi, M., 2015. Assessment of the contribution of N-fertilizers to nitrate pollution of groundwater in western Iran (case study: Ghorveh–Dehgelan Arquifer). Water Qual. Expo. Health 7 (13), 143–151. Rahmati, O., Pourghasemi, H.R., 2017. Identification of critical flood prone areas in datascarce and ungauged regions: a comparison of three data mining models. Water Resour. Manag. 31, 1473–1487. Rahmati, O., Pourghasemi, H.R., Zeinivand, H., 2016a. Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran. Geocarto Int. 31, 42–70. Rahmati, O., Zeinivand, H., Besharat, M., 2016b. Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis. Geomatic Nat. Hazard Risk 7, 1000–1017. Rodrigo-Comino, J., Keesstra, S., Cerdà, A., 2018. Soil erosion as an environmental concern in vineyards. The case study of Celler del Roure, Eastern Spain, by means of rainfall simulation experiments. Beverages 4 (2), 31. Saaty, T.L., 1980. The Analytic Hierarchy Process. McGraw-Hill, New York. Saaty, T.L., Vargas, L.G., 1998. Diagnosis with dependent symptoms: Bayes theorem and the analytic hierarchy process. Oper. Res. 46 (4), 491–502. Samanta, S., Koloa, C., Pal, D.K., Palsamanta, B., 2016. Flood risk analysis in lower part of Markham River based on multi-criteria decision approach (MCDA). Hydrology 3 (3), 29. Samanta, S., Kumar Pal, D., Palsamanta, B., 2018. Flood susceptibility analysis through remote sensing, GIS and frequency ratio model. Appl Water Sci 8, 66. Scheuer, S., Haase, D., Meyer, V., 2011. Exploring multicriteria flood vulnerability by integrating economic, social and ecological dimensions of flood risk and coping capacity: from a starting point view towards an end point view of vulnerability. Nat. Hazards 58, 731–751. Shafapour Tehrany, M., Kumar, L., 2018. The application of a Dempster–Shafer-based evidential belief function in flood susceptibility mapping and comparison with frequency ratio and logistic regression methods. Environ. Earth Sci. 77, 490. Shafapour Tehrany, M., Pradhan, B., Jebur, M.N., 2013. Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. J. Hydrol. 504, 69–79. Shafapour Tehrany, M., Pradhan, B., Jebur, M.N., 2015a. Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method. Stoch. Env. Res. Risk A. 29, 1149–1165. Shafapour Tehrany, M., Pradhan, B., Mansor, S., Ahmad, N., 2015b. Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena 125, 91–101. Shafapour Tehrany, M., Shabani, F., Javier, D., Kumar, L., 2017a. Soil erosion susceptibility mapping for current and 2100 climate conditions using evidential belief function and frequency ratio. Geomatics Nat. Hazards Risk 1–21. Shafapour Tehrany, M., Shabani, F., Neamah Jebur, M., Hong, H., Chen, W., Xie, X., 2017b. GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques. Geomatic Nat. Hazard Risk 8, 1538–1561. Shafer, G., 1976. A Mathematical Theory of Evidence. vol. 1. Princeton University, Princeton. Shafique, M., van der Meijde, M., Kerle, N., van der Meer, F., 2011. Impact of DEM source and resolution on topographic seismic amplification. Int. J. Appl. Earth Obs. Geoinf. 13, 420–427. Swets, J.A., 1988. Measuring the accuracy of diagnostic systems. Science 240, 1285–1293. Tahmassebipoor, N., Rahmati, O., Noormohamadi, F., Lee, S., 2016. Spatial analysis of groundwater potential using weights of evidence and evidential belief function models and remote sensing. Arab. J. Geosci. 9, 1–18. Termeh, S.V.R., Kornejady, A., Pourghasemi, H.R., Keesstra, S., 2018. Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms. Sci. Total Environ. 615, 438–451. Tien Bui, D., Hoang, N.D., 2017. A Bayesian framework based on a Gaussian mixture model and radial-basis-function fisher discriminant analysis (BayGmmKda V1.1) for spatial prediction of floods. Geosci. Model Dev. 10, 3391–3409. Tien Bui, D., Pradhan, B., Nampak, H., Bui, Q.T., Tran, Q.A., Nguyen, Q.P., 2016. Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibility modeling in a high-frequency tropical cyclone area using GIS. J. Hydrol. 540, 317–330. Tierney, K.J., Lindell, M.K., Perry, R.W., 2001. Facing the Unexpected: Disaster Preparedness and Response in the United States. Joseph Henry Press. Vaze, J., Teng, J., Spencer, G., 2010. Impact of DEM accuracy and resolution on topographic indices. Environ. Model Softw. 25, 1086–1098. Watershed Department of Iran (WDI), 2002. Statistics of Flooding Area in Iran Report (50pp.).

458

A. Arabameri et al. / Science of the Total Environment 660 (2019) 443–458

Wechsler, S., 2007. Uncertainties associated with digital elevation models for hydrologic applications: a review. Hydrol. Earth Syst. Sci. 11, 1481–1500. World Health Organization, 2003. Disaster Data-key Trends and Statistics in World Disasters Report. WHO, Geneva, Switzerland http://www.ifrc.org/PageFiles/89755/2003/ 43800-WDR20, Accessed date: 5 April 2017. Wu, S.J., Lien, H.C., Chang, C.H., 2010. Modeling risk analysis for forecasting peak discharge during flooding prevention and warning operation. Stoch. Env. Res. Risk A. 24 (8), 1175–1191. Xiao, Y., Yi, S., Tang, Z., 2017. Integrated flood hazard assessment based on spatial ordered weighted averaging method considering spatial heterogeneity of risk preference. Sci. Total Environ. 599, 1034–1046. Yesilnacar, E., Topal, T., 2005. Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Eng. Geol. 79, 251–266.

Yousefi, S., Mirzaee, S., Keesstra, S., Surian, N., Pourghasemi, H.R., Zakizadeh, H.R., Tabibian, S., 2018. Effects of an extreme flood on river morphology (case study: Karoon River, Iran). Geomorphology 304, 30–39. Youssef, A.M., Pradhan, B., Sefry, S.A., 2016. Flash flood susceptibility assessment in Jeddah city (Kingdom of Saudi Arabia) using bivariate and multivariate statistical models. Environ. Earth Sci. 75, 12. Zhang, D.W., Quan, J., Zhang, H.B., Wang, F., Wang, H., He, X.Y., 2015. Flash flood hazard mapping: a pilot case study in Xiapu River basin, China. Water Sci. Eng. 8, 195–204. Zhao, G., Pang, B., Xu, Z., Tu, T., 2018. Mapping flood susceptibility in mountainous areas on a national scale in China. Sci. Total Environ. 615, 1133–1142. Zou, Q., Zhou, J., Zhou, C., Song, L., Guo, J., 2013. Comprehensive flood risk assessment based on set pair analysis-variable fuzzy sets model and fuzzy AHP. Stoch. Env. Res. Risk A. 27 (2), 525–546.