A spatial bayesian-network approach as a decision-making tool for ecological-risk prevention in land ecosystems

Ecological Modelling 419 (2020) 108929

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A spatial bayesian-network approach as a decision-making tool for ecological-risk prevention in land ecosystems

T

Kai Guo, Xinchang Zhang*, Xi Kuai, Zhifeng Wu, Yiyun Chen, Yi Liu Guangzhou University, 230 Wai Huan Xi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, China

A R T I C LE I N FO

A B S T R A C T

Keywords: Regional-Scale ecological-risks prevention Decision-Making tool Spatial bayesian network Cross-Validation Land ecosystem

Prevention of ecological risks in land ecosystems is crucial for environmental protection and sustainable land use. With increasingly severe land degradation, new and effective methods must be developed for the management of ecological risks. In this study, a conceptual decision-making model in ecological risk prevention was developed using the Bayesian belief network with a geographic information system (GIS) for the regional-scale land ecosystem in the traditional mining city of Daye in Central China. Based on the results of a sensitivity analysis, the variable of eco-resilience reduction was identified as the most sensitive to habitat removal with the highest mutual information at 0.71. The two variables of soil pollution and water-quality deterioration were selected for a cross-validation analysis, and the changes in both the calibration and validation performance were very small. The scenarios we considered based on the interests of various stakeholders presented the spatial distribution of the following regulative effects of various management measures on a regional scale: (1) the variable of urbanisation showed that the probability of 11.5 % of all the grids decreased at a high state over an area of 177 km2; (2) the variable of mining showed that the probability of 35.5 % of the all the grids at a high state decreased, over an area of 554 km2; (3) the variable of habitat removal showed that the probability of 6.7 % of all the grids at a high state decreased, over an area of 87 km2; and (4) the variable of health threats showed that the probability of 8.4 % of all the grids at a high state decreased, over an area of 135 km2. The Bayesiannetwork-GIS based tools can support the decision-making process used for ecological-risk prevention in land ecosystems.

1. Introduction The functions of a land ecosystem such as supporting vegetative growth, producing and maintaining biodiversity, providing habitat for humans and other organisms, purifying the environment, and protecting soil and water, and modifying climate are important (Ferretti and Pomarico, 2013; Turner et al., 2016; Huang et al., 2019). The reduction of the ecological functions of land causes damage to habitats, biodiversity, air, water, and soil, which limit the regional economic development and human survival (Li et al., 2014a; Bai et al., 2014; Bryan et al., 2018). Thus, the ecological-risk management of land ecosystems is attracting attention in research and administration globally (Li et al., 2014b; Liang et al., 2017; Kang et al., 2018). The decision-making analysis for ecological-risk prevention requires knowledge of the causes and mechanisms of land degradation and exploration of techniques and methods for controlling and restoring degraded land ecosystems (Ferretti and Pomarico, 2013; Comino et al., 2014; Turner et al., 2016).

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Studies on the prevention of ecological risks in a land ecosystem are generally focused on the environment, economy, and social culture (Li et al., 2014; Turner et al., 2016; Guo et al., 2017). The prevention of environmental degradation involves combating erosion, salinization and desertification of soil, damage to the natural landscape (Li et al., 2014c; Ochoa-Cueva et al., 2015; Price et al., 2015; Kosmas et al., 2017), soil contamination, water-quality deterioration, and soil depletion (Guo et al., 2014; Kibblewhite, 2012; Pries et al., 2008; Xu et al., 2016). The development of an agricultural economy requires the preservation of arable land, reclamation of waste land, and maintenance and improvement of land productivity (ELD-Initiative, 2013; Jackson et al., 2013). A stable society relies on its land ecosystem for supporting sustainable human well-being (Costanza et al., 2013; Iniesta-Arandia et al., 2014). However, previous studies have primarily been focused on the individual aspects of the land ecosystem with discreet results that did not provide effective guidance for realising the comprehensive protection of a complex ecosystem. Therefore, an integrative approach to research

Corresponding author. E-mail address: [email protected] (X. Zhang).

https://doi.org/10.1016/j.ecolmodel.2019.108929 Received 26 May 2019; Received in revised form 27 December 2019; Accepted 30 December 2019 0304-3800/ © 2020 Elsevier B.V. All rights reserved.

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statistical data (Smith et al., 2007; Celio et al., 2014; Gonzalez-Redin et al., 2016) such that researchers can locate the uncertainty and observe the uncertainty level. This solution also helps other people related to the research to observed the consequences of ecosystem degradation on a map, which then encourages them to accept proposals for ecological-risk prevention (Johnson et al., 2011; Chee et al., 2016). In addition, for the BN–GIS approach, further research is required for developing large-scale methods for optimising the models used by land ecologists and managers: these include transparent modelling processes, the application of different types of data, and communication platform construction. However, information regarding the utilisation of the spatial BN model in the study of land degradation is limited. In the present study, the spatial BN model was used to test a decision-making tool for the prevention of ecological risks of the land ecosystem in the mining city of Daye, China. Netica (Norsys Software Corporation, 1998;2010), a BN software package frequently used in ecosystem service modelling research (Chen and Pollino, 2012; Landuyt et al., 2013; Forio et al., 2015), was used to evaluate the performance of the BN model. The scenario simulation was designed to identify prior decision-making options for stakeholders in land management, and cross-validation was performed to confirm the validity of our assessment model.

involving multiple factors that function simultaneously and interactively in the entire land ecosystem has been proposed (Guo et al., 2017). Integrative research on ecological-risk management often depends on the use of appropriate tools or models, and it has been increasingly attracting the attention of researchers. These researchers in this field have made valuable contributions such as multi-agent systems for the decision-making processes involved in forest management (Xu et al., 2015; Ahlqvist et al., 2018), artificial intelligence models for simulating and predicting the risks of a land ecosystem (Liu et al., 2018; Tsai et al., 2018), multi-criteria decision-making models for risk-zoning and management of large areas (Li et al., 2014b; Gallego et al., 2019; Souissi et al., 2019), designs for an integrated risk index that can serve as a warning for implementing the appropriate regional development strategies (Partl et al., 2017; Wang et al., 2019), and environmental risk mapping as guidance for risk minimisation in risk management and decision making (Ma et al., 2013; Maldonado et al., 2016; Petus et al., 2016). The aforementioned tools and models, however, can still be further improved when applied to research on land ecosystems. Firstly, they can be applied primarily in mono-factor stud ies, as has been mentioned in this paper, and they cannot provide motivation for the comprehensive risk prevention for a land ecosystem. Secondly, they represent linear relationships between the factors of the land ecosystem and are unfit for research on nonlinear relationships in a land ecosystem (Guo et al., 2015; W.Q. Zhou et al., 2019). Thirdly, they cannot be used to simultaneously process multiple data. Fourthly, they have been established based on single isolated issues and do not encourage researchers to formulate extensive and comprehensive hypotheses. Finally, they have primarily been designed by researchers without involving the related stakeholders, and the obtained results cannot be easily understood and accepted by the public. To address the above drawbacks, in this study, we used the Bayesian belief network (BN) model with a geographic information system (GIS), which has been believed to be more scientific and powerful. The BN model is a semi-quantitative model that combines ecological models with expert knowledge, and it has shown its effectiveness in studies on ecological issues (Marcot et al., 2006; 2017; Chen and Pollino, 2012). The model reveals the complex ecological mechanisms underlying the results of previous research (Fienen et al., 2013; McDonald et al., 2015; Franco et al., 2016) and helps researchers to present various valuable hypotheses (Landuyt et al., 2013; Chee et al., 2016; Marcot and Penman, 2019) that contribute to realising comprehensive and feasible research designs that then provide convincing results. For the current work of land protection, the emphasis is on the participation of different stakeholders (Voinov and Bousquet, 2010; Luyet et al., 2012), and the BN may provide such convenience; various related stakeholders are involved in the research to help in identifying realistic and practical research objectives (Krueger et al., 2012; Dick et al., 2018). The BN can process multi-type data, operate in a poor data-collection environment (Uusitalo, 2007; Chen and Pollino, 2012), handle uncertainty (Aguilera et al., 2013), be updated using follow-up data, and be combined with other algorithms for obtaining accurate calculations (Aitkenhead and Aalders, 2009; Johnson et al., 2011; Marcot and Penman, 2019). Furthermore, with the advantages of its combination with other software programs, the application of the BN also presents convenience in testing and validating (Marcot, 2012; Fienen et al., 2013). For example, the technique of cross validation that is successfully used in BN modelling prevents the occurrence of the overfitting resulting from overly complex BNs (Marcot, 2012; Beuzen and Simmons, 2019). However, the results obtained on using the BN model comprise abstract statistical data that research participants cannot understand the explicit benefits of and based on which they cannot immediately and confidently make decisions. Therefore, it has been recommended that the BN model be used in combination with a GIS for visualising the

2. Materials and methods 2.1. Study area Daye City (114° 31′–115° 20′ E, 29° 40′–30° 15′ N; Fig. 1) is located in the south-eastern part of the Hubei Province in Central China. This area is rich in mineral deposits and has well-developed mining and metallurgy industries. The ecosystem in and around the city is comprehensive, and it consists of lakes, rivers, forests, mines, arable lands, gardens, and urban and rural residential areas. The entire study area is 1566.3 km2. At present, the ever-increasing industries have been harming the ecosystem in the entire area, which has resulted in dysfunctions of the ecosystem services, including soil contamination, water pollution, and loss of arable land. 2.2. Framework of risk prevention of land ecosystem on a regional scale The ecological risk assessment concept of the United States Environmental Protection Agency (USEPA) with respect to land-ecosystem degradation (Suter, 1993; USEPA, 1998) is introduced in the present study. Several key factors, including sources, stressors, and endpoints, were identified and selected as research variables. Previous papers have been reviewed to obtain existing knowledge regarding model construction. On the basis of the BN model, a conceptual model was developed to determine the cause–effect relationships among the variables to describe the land degradation in Daye City. The various types of data obtained were spatialized in the GIS. The relationships among the variables were quantified by defining the conditional probability table (CPT) of each node. The spatial BN model was combined with the GIS for the model operation. Scenarios were simulated to assist decision makers in identifying prior options for risk prevention in the future. A sensitivity analysis was used to evaluate the ability of our model, and cross-validation was performed for validating the model. Data imperfections and knowledge gaps were identified in the evaluation of the model operation to guide future research. These procedures are similar to those of previous studies comprising the use of BN (Fig. 2). 2.3. Building of bayesian networks 2.3.1. Identification of variables for BN On the basis of previous BNs (Cain, 2001; Marcot et al., 2006; Castelletti and Soncini-Sessa, 2007; Chen and Pollion, 2012), we 2

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Fig. 1. Map showing location of Daye City and its mining distribution.

shortage, (4) water-quality deterioration, (5) threats to public safety and health, (6) biodiversity decrease, (7) eco-resilience reduction (ecoresilience is the ability of a complex eco-system to recover quickly after suffering severe disruptions), (8) dysfunction in landscape aesthetics, (9) decline in land productivity, (10) shortage of usable land, and (11) population overload (see Appendix 1 for the detailed description of these problems). The candidate stressors were determined according to the aforementioned problems by a panel of experts (i.e., professors and local officials) from different but related fields (e.g., ecology, agronomy, geology, land resources, and urban development). These stressors have a physical, chemical, and biological effect on the land ecosystem in our study area. Fourteen stressors were identified as follows: (1) atmospheric deposition, (2) alteration of the earth-surface runoff, (3) alteration of underground runoff, (4) destruction of the earth surface, (5) desertification, (6) removal of habitat, (7) space occupation, (8) accumulation of heavy metals, (9) organic pollutants, (10) pathogens, (11) soil erosion, (12) soil salinization, (13) nutrient runoff, and (14)

initially identified all the possible components as variables, including the problem formulation and candidate stressors and their possible sources. We formed a research group before the present study was started. The group members performed field investigations and conducted a literature review by reading related papers in Web of Science. The problem formulation is a critical step in the determination of the evaluation range and target of ecological-risk prevention in a land ecosystem (Suter, 1993; Guo et al., 2017). The problems identified in this research reflected the current condition of the land degradation in Daye City. Our problem formulation was based on a comprehensive literature review, which aided in conducting related field investigations and surveys (McCloskey et al., 2011; Chen and Pollion, 2012; Franco et al., 2016). In addition, the problem formulation was supported by subsequent laboratory examinations and by the historical data provided by related local government departments. Seminars were then conducted to refine the identified problems in order to focus on the major problems. In the present study, 11 problems were identified as follows: (1) soil-quality deterioration, (2) soil contamination, (3) water

Fig. 2. Flowchart of regional-scale risk prevention in the land ecosystem in Daye City. 3

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includes all the important variables and cause–effect interactions among these variables, and (2) a quantitative component that consists of conditional probabilities, which quantify the aforementioned cause–effect relationships (Marcot et al., 2006; Landuyt et al., 2013). BNs are often referred to as directed acyclic graphs; a BN consists of nodes that visually represent the key variables and arrows that represent the causal relationships among the variables. Each variable is assigned multiple states (Aguilera et al., 2013; Landuyt et al., 2013; Franco et al., 2016). The entire catalogue of these correlations comprises the CPTs. The strength of the relationships among the variables is defined in the CPT attached to each node. The CPTs specify the degree of belief that a node will be in a particular state as determined by the state of the parent nodes. The Bayesian theorem encapsulates the mathematical rules governing the propagation of probabilities based on the conditional dependency of variables combined with data to produce posterior probabilities. Thus, the BN is generally used to perform various simulations based on the specified node corresponding to the input values and by noting the changes in the probability distributions of the output nodes (Kininmonth et al., 2010; Grêt-Regamey et al., 2013). The Bayesian theorem is mathematically expressed as follows.

eutrophication (see Appendix 2). Candidate risk sources for the land degradation in Daye City were identified under the following conditions: (i) familiarity with the local economic development pattern, (ii) concern with major pollutantemitting enterprises, (iii) interview conducted with local residents suffering from past hazardous events, and (iv) potential disturbances identified according to our knowledge (USEPA, 2000; Yu et al., 2010; Teng et al., 2014). Twenty sources of stressors were determined as follows: (1) storms and floods, (2) geological disasters, (3) extreme weather, (4) acid rain, (5) urbanisation, (6) population growth, (7) intensive land reclamation, (8) mining, (9) transportation, (10) water projects, (11) lake-area reclamation, (12) industrial point-source pollution, (13) solid-waste piles, (14) application of pesticides, (15) application of fertilizers, (16) application of mulch plastic films, (17) aquaculture, (18) irrigation, (19) lumbering, and (20) grazing (see Appendix 3). Generally, in terms of the interactions among the aforementioned problems, stressors, and sources, the sources released stressors, and the stressors caused problems. These interactions damaged the biotic and abiotic feedback mechanisms of the land ecosystem (Suter, 1993; Guo et al., 2017). Therefore, these interactions should be represented by several hypotheses in the form of a conceptual model (Platt, 1994; Huelsenbeck et al., 2001; Jerald and Omland, 2004). A conceptual model (Fig. 3) was constructed to represent the complex interactions among the sources, stressors, and endpoints. The nodes representing different spatial scales and complexities could be nested with each other, and their linkages presented the cause–effect relationships through the abovementioned causal hypotheses (Jakeman et al., 2006; Chen and Pollion, 2012). The model eventually characterised the process of land degradation (i.e., risk formation) by describing several ecological mechanisms. Our model comprised 46 nodes and 64 arrows, the majority of which were included in our model to represent the general status of land degradation in the study area. Not all the factors mentioned in the model were dealt with in the BN model, but they do represent the complete profile of the land degradation in the study area. Future research may take into consideration all the factors that form the general knowledge of ecology (Chen and Pollion, 2012).

P (Ai / B ) =

P (Aj ) × P (B / Aj ) n

∑i =1 P (Ai ) × P (B / Ai )

(1)

where B is an event, Ai refers to all the possible causes of event B, P(Ai) refers to the prior probabilities derived from a priori data, and i represents a particular variable (Castelletti and Soncini-Sessa, 2007; Johnson et al., 2010a; Fienen et al., 2013). The use of probabilities enables a BN to handle uncertain input variables and uncertain relationships among all the nodes in the model. These uncertainties propagate through the network and result in model predictions that explicitly account for uncertainties (Kjaerulff and Madsen, 2008; Pearl et al. (2010); Landuyt et al., 2015). In the current study, the BN modelling software Netica (Norsys Software Corporation, 2010) was used to solve the above equation (Uusitalo, 2007; Voinov and Bousquet, 2010). For a detailed description of BNs, readers may refer to the studies of Chen and Pollino (2012) and Marcot and Penman (2019).

2.3.2. Spatial BN implementation 2.3.2.1. Bayesian networks. A BN is a graphical, multivariate, statistical model that comprises two structural components, namely, (1) a qualitative component that consists of a causal network, which

2.3.2.2. Embedding BN into GIS. Our spatially explicit approach was used in the decision-making processes by combining BN with GIS while following the process outlined by Smith et al. (2007). All the data

Fig. 3. Conceptual model of the land degradation in Daye City. The nodes were assigned to three groups: risk sources (orange), stressors (yellow), and endpoints (blue) (Costanza et al., 1997; Rapport et al., 1998; Turner et al., 2016) (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article). 4

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are the main land-use types in Daye City. They also represented the opinions of the research group on field investigation. The soil degradation was investigated on the basis of the following indicators: soil degeneration and soil erosion. Heavy-metal pollution caused by mining and mineral processing has attracted the attention of several local researchers (Wu et al., 2009; Wei and Yang (2010); Wu et al., 2010; He et al., 2013; Ma et al., 2013; Teng et al., 2014). In view of funding constraints and substandard laboratory conditions, the research group selected Cu, Pb, and Cd and determined their content levels in the 225 soil samples. Atomic absorption spectrophotometry was performed in the laboratory to determine the content levels of Cu, Pb, and Cd (Kemper and Sommer, 2002; Viscarra Rossel et al., 2006; Ferrier et al., 2009; Ren et al., 2009; Pandit et al., 2010; Liu et al., 2011). Barium chromate spectrophotometry was performed to determine the content level of As (Ren et al., 2009; Zheng et al., 2011). The Nemerow index (Yang et al., 2011; Hu et al., 2013; Jiang et al., 2014) was applied to calculate the individual values of the heavy metals (i.e., Cu, Pb, Cd, and As) and thus obtain the Nemerow composite index. The aforementioned soil-component measurements and heavy-metal content data based on geographical coordinates (i.e., global positioning system data) of the soil samples, as well as the remote-sensing image and elevation data of the study area were processed as spatial data and integrated in a GIS. Based on interviews and a survey conducted by the Daye Environmental Protection Bureau, the research group obtained the quarterly monitoring data of the water quality in water bodies located at 37 monitoring points, including the main rivers, lakes, and sensitive waters, from 2013 to 2016. The group selected ammonia–nitrogen content, eutrophication, and heavy-metal pollution as the three criteria for water-quality deterioration. The group then determined the level of the water-quality degradation (i.e., heavy, moderate, or light pollution) based on the average level calculated from the data collected in the four-year period from 2013–2016. GIS spatial processing was performed for the selected indices based on the geographical information of the sampling points. The research group along with the Daye City Land Resources Bureau frequently organised investigations on ecological-environment damage caused by mining in Daye City from 2013 to 2016. The investigation sites included copper, iron, coal, gold, and silver mines, as well as related smelting sites, ore dressing sites, quarries, tailing reservoirs, coal gangue dumps, and open metal mines. The problems included over 800 damages to the earth surface and vegetation, 349 geological hazards

Table 1 CPT of the node of heavy-metal accumulation as an example. Parent nodes Mining

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 …

Application of pesticides 0 0 0 0 0–3 0–3 0–3 0–3 3–6 3–6 3–6 3–6 6–9 6–9 6–9 6–9 …

Intermediate nodes Industrial point sources pollution 0 0-3 3-6 6-9 0 0-3 3-6 6-9 0 0–3 3–6 6–9 0 0–3 3–6 6–9 …

　

Heavy-metal accumulation 0–5 5–10 10–20

　

0.99 0.77 0.65 0.51 0.84 0.67 0.56 0.38 0.79 0.69 0.50 0.24 0.68 0.56 0.36 0.17 …

0.01 0.19 0.23 0.28 0.15 0.24 0.28 0.35 0.13 0.16 0.3 0.42 0.21 0.23 0.36 0.44 …

0 0.04 0.12 0.21 0.01 0.09 0.16 0.27 0.08 0.15 0.2 0.34 0.11 0.21 0.28 0.39 …

present in the network nodes were determined as a set of GIS variables in ArcGIS 10.2. This data set was processed by the BN model as a case and was simulated in Netica. The CPTs were used to quantify the relationships of the related nodes with their parent nodes. These relationships depend on the state of each node. The tool used spatially explicit input data as raster grids and then executed the BN inference calculations for each raster grid cell of the input data (Smith et al., 2007; Johnson et al., 2011). The outputs were expressed as raster maps, wherein the probability distribution for the target variables was calculated by BN inference. The spatially explicit results were acceptable to various land-resource stakeholders (Landuyt et al. (2014); Gonzalez-Redin et al., 2016). 2.4. Data and parameter design 2.4.1. Data collection and processing Field soil from the study area was sampled in 2013. A total of 225 valid samples were obtained from the entire study area. The samples were obtained from rural settlements, farmlands, benchlands, and irrigation districts surrounding the industrial and mining areas, which

Fig. 4. Current distribution of probabilities for all the nodes. 5

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Table 2 MI between the target nodes of the training model. Target node

Sensitive node

MI

Soil-quality deterioration

Nutrient runoff Intensive reclaim Irrigation Mining Mining Heavy-metal accumulation Solid-waste pile Aquaculture Industrial point-sources pollution Lake-area reclamation Irrigation Alteration of underground runoff Soil contamination Earth-surface destruction Heavy-metal accumulation Mining Habitat removal Urbanisation Solid-waste pile Habitat removal Lake-area reclamation Urbanisation Habitat removal Urbanisation Lake-area reclamation Lumbering Habitat removal Lake-area reclamation Water shortage Solid-waste pile Habitat removal

0.31 0.24 0.21 0.17 0.29 0.23 0.12 0.19 0.08 0.13 0.18 0.05 0.22 0.13 0.08 0.05 0.62 0.51 0.38 0.71 0.16 0.47 0.55 0.46 0.24 0.31 0.32 0.28 0.10 0.23 0.18

Soil contamination

Water quality deterioration Water shortage

Threats to public safety and health

Shortage in usable land

Eco-resilience reduction

Biodiversity decrease

Decline in land productivity

Dysfunction in landscape aesthetics

Fig. 5. Calibration and validation of skill values for various arrangements of bins on the land degradation BNs. Sets are the set identifiers for the divided folders. Skillmean represents the mean value in the dataset. The approximation between the values of calibration and verification verifies the accuracy of the method.

Table 3 Validation results for ten-fold cross validation. Current_Fold

cal_s

val s

cal_w

val_w

1 2 3 4 5 6 7 8 9 10

0.6505 0.6470 0.6285 0.6311 0.6378 0.6325 0.6281 0.6328 0.6383 0.6443

0.6565 0.6170 0.6518 0.6179 0.5887 0.6446 0.6977 0.6471 0.6073 0.5650

0.6150 0.6070 0.6150 0.6018 0.5728 0.5868 0.5831 0.5696 0.5737 0.5805

0.7070 0.5875 0.5497 0.5093 0.5646 0.5327 0.4967 0.4643 0.4452 0.4273

Fig. 6. Calibration and validation of skill values for various arrangements of bins on the land degradation BNs. Sets are the set identifiers for the divided folders. Skillmean represents the mean value in the dataset. The approximation between the values of calibration and verification verifies the accuracy of the method.

(e.g., collapse, gob areas, excavation, landslide, and water depletion), 550 land damages caused by solid-waste dumping, and 1294 land plaques covering an area of 6943.58 ha. In terms of actual harm, the research group conducted a field investigation, plotting, and performed scene photography to obtain the information of each plot. An expert group performed intra-industry interpretation, on-site verification, and hazard-level classification. Finally, the research group performed spatial processing based on the statistical information of each plot. The data for waste-water discharge and solid-waste piling were regional and could not be spatially processed. Therefore, we used “industrial point-source pollution” as a variable to present the discharge and piling. This variable was characterised by the kernel density distribution in the GIS. Threats to public safety and health are an important ecological function indicator (Cheng and Nathanail, 2009; Pinedo et al., 2014; Huang et al., 2016). In view of the characteristics of the traditional mining city of Daye, the main threat is posed by mines that cause heavy-metal pollution and water pollution and result in geological hazards in the surrounding environment. The health risk was evaluated via an exposure-risk assessment (Korre et al., 2002; Gay and Korre,

2006; Li et al., 2014d). The extent of the impact of heavy-metal pollution was considered, and the severity of the threat to human health was determined based on the Euclidean distance in the GIS (Bien et al., 2004; Hooker and Nathanail, 2006; Morra et al., 2006; Poggio and Vrščaj, 2009). The data application of the land-use category was based on the landuse maps of Daye City of 2013. A large amount of data was obtained from the Daye Statistical Yearbook and from social surveys, such as data collected by the Daye Environmental Protection Bureau, and from the historical information of Daye (1949–2010). The data regarding the variables affecting the land degradation in Daye City were too complex for general models to process; nevertheless, the BN model could manage these data (Landuyt et al., 2013). All the above processes were performed in ArcGIS 10.2.

2.4.2. Parameter design The strength of the relationships between the nodes was quantified 6

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Fig. 7. Distribution of probabilities for all the nodes in Scenario 1.

Fig. 8. Scenario simulation distribution of urban nodes.

Pollino et al., 2007b). The ranges of 0–5, 5–10, and 10–20 represent variables under low, medium, and high conditions, respectively. For example, the CPT for heavy-metal accumulation (Table 1) comprised three states (i.e., high, medium, and low), which were determined by three parent nodes (i.e., application of pesticides, industrial point sources, and mining).

in the CPTs attached to each node. The relationships between the states of parent and child nodes were quantified within the CPTs that presented the probability of a child node taking on each discrete state, which is determined by the state of each parent node (Marcot et al., 2001; Pollino et al., 2007b; Chen and Pollino, 2012). The states of marginal nodes were determined based on their own probability distributions (Marcot et al., 2006; Chen and Pollino, 2012). To ensure that the states of the parent and child nodes interacted logically and to avoid bias, a combination of empirical data, laboratory data, past experiences, expert opinions, and a literature review was used to estimate the conditional probabilities (Smith et al., 2007;

2.5. Model application 2.5.1. BN sensitivity A sensitivity analysis was performed to measure the sensitivity of 7

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Fig. 9. Distribution of probabilities for all the nodes for scenario 2.

and a higher score of sk indicated the better performance of the models; sk was calculated as follows:

the changes in the probabilities of query nodes when the parameters and inputs changed (Marcot et al., 2006). Two types of sensitivity analyses were performed to identify the relative influence of the variables in the network Kjaerulff and Madsen, 2008; Norsys (1998). Entropy H(X) is often used to evaluate the uncertainty or randomness of a variable X and is characterised by a probability distribution P(x), as follows (Korb and Nicholson, 2004; Pollino et al., 2007a):

σ2 sk = ⎡1 − e2 ⎤ ⎢ σ0 ⎥ ⎣ ⎦

where σe2 was the mean squared error between the observations and σ02 predictions, and was the variance of the observationsWe selected two variables for the validation skill analysis in the BN simulation, namely, soil pollution and water-quality deterioration.

n

H (X ) = H (x i , ..., x n ) = − ∑ P (x i ) log (x i ) i=1

(2)

where X and x represent a variable; i represents a particular variable, and n denotes the number of all the variables. The entropy measures were used to assess the average information required in addition to the current knowledge to specify a particular alternative. The most uncertain variables were identified according to the ranked probabilities (Pollino et al., 2007a). Mutual information (MI) was used to measure the effect of one variable X on another Y (Korb and Nicholson, 2004) as follows:

I (X , Y ) = H (X ) − H (X / Y )

(4)

2.5.3. Scenario design An important finding of this research was that the BN model could be directly used as a management tool by simply setting the state of an endpoint to a desired level and, thereby, essentially solving the model “backwards” (Ayre and Landis, 2012; Landuyt et al., 2013). Subtle changes in environment management may result in large changes in the simulation for sensitive endpoints. This inherent flexibility of use makes the model a powerful tool for resource management because alternative management scenarios can be easily evaluated for the desired objectives. In the current study, four different scenarios were designed while taking into consideration the interests of various stakeholders to ensure that the simulation results could be referred to by stakeholders for favourable decision making. Scenario 1: The Land Resources Department was the most concerned regarding the amount of land resource supply, which is closely related to regional economic development. The node of usable-land shortage was thus set at an extremely low state to identify changes in the probability distribution of the relevant variables. Scenario 2: The Environmental Protection Department was primarily concerned with soil pollution and water pollution. The nodes of soil contamination and water-quality deterioration were thus set at an extremely low state. Scenario 3: The Agricultural Production Department was most interested in land productivity. The node of land productivity was thus set at an extremely high state. Scenario 4: On the basis of the sustainable development of resourceexhausted cities, the local government seemed to value mining. The nodes of mining were thus set at an extremely low state.

(3)

where I(X,Y) was the MI between the variables. This measure reported the expected degree of divergence of the joint probability of X and Y from what it would be if X was independent of Y (Korb and Nicholson, 2004). If I(X,Y) was equal to zero, X and Y were mutually independent (Pearl, 1988; Johnson et al., 2010b). To determine the degree of independence between the variables in a pair in the BN, we performed a sensitivity analysis. 2.5.2. Model validation The validation is performed to assess the predictive performance of BN models (Fienen and Plant, 2015; Forio et al., 2015). We applied kfold cross validation (KFCV), which can handle the problem of overfitting and complexity in BN models. In the KFCV, firstly, the dataset was randomly split into k groups. Each group was considered as the validation set for model building, and the remaining groups were treated as a training dataset for evaluating the built models. This process was repeated k times, and the performances of the BN models were then summarised. k was considered as 10, which is a value that was widely adopted in previous studies (Marcot, 2012; Beuzen and Simmons, 2019). The performance of the BN models was assessed based on a useful statistic, namely, the skill (sk). The value of sk was between 0 and 1, 8

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Fig. 10. Scenario simulation distribution of mining node.

3. Results

the experiment, the expected sources of soil-quality degradation comprised the application of pesticides and fertilizers, the MI of which were 0.07 and 0.11, respectively. The node of water-quality deterioration was the most sensitive to aquaculture instead of the industrial point source, and the corresponding MI was rather high at 0.19. The node of water shortage was the most sensitive to irrigation instead of lake-area reclamation (MI: 0.18 and 0.13, respectively). The nodes of the soil contamination, earth-surface destruction, and heavy-metal accumulation were sensitive to threats to public safety and health (MI: 0.22, 0.13, and 0.08 respectively). The majority of the other nodes were sensitive to lake-area reclamation and mining. For example, the nodes of water shortage, eco-resilience reduction, biodiversity decrease, and landproductivity reduction were all sensitive to the lake-area reclamation (MI: 0.13, 0.16, 0.24, and 0.28, respectively; Table 2).

3.1. Baseline training The base scenario of the BN model showed that the nodes of soil contamination, threats to public safety and health, and water-quality deterioration had high probability distributions (i.e., 42.2 %, 38.5 %, and 17.5 %, respectively). Meanwhile, the nodes of eco-resilience reduction, water shortage, and decline in land productivity had moderate probability distributions (32.4 %, 36.7 %, and 29.3 %, respectively). Furthermore, the nodes of soil-quality deterioration, population overload, and biodiversity decrease had low probability distributions (54 %, 43 %, and 41.5 %, respectively) (Fig. 4).

3.2. Sensitivity analysis 3.3. Model performance The node of soil-quality degradation was the most sensitive concerning nutrient runoff, intensive land reclamation, irrigation, and mining (MI: 0.31, 0.24, 0.21, and 0.17, respectively). However, prior to

Table 3 shows the mean for both the variables of soil contamination and water-quality deterioration for obtaining the skill mean over bins 9

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Fig. 11. Distribution of probabilities for all the nodes for scenario 3.

Fig. 12. Scenario simulation distribution of habitat removal node.

and then decreased (Fig. 5). Although the verification line rose within the range of 4–5 bins, the bins all showed a decline in the performance of water-quality deterioration (Fig. 6).

through a ten-fold cross validation. The small differences in the performance of the test and training sets suggest that our BN overfitting is minimal. For example, the performance of the soil contamination bin evaluated through one-, four-, and six-fold cross-validation was similar. However, the performance of water-quality deterioration was less stable in the nine- and ten-fold cross-validations (Table 3). Figs. 5 and 6 depict the changes in calibration and validation performance in the ten-fold cross validation. In terms of the performance of soil contamination, no significant change was found in the calibration line, and the verification line reached its peak value of 0.6976 at 7 bins

3.4. Scenario analysis The results of Scenario 1 showed that the probabilities of the nodes of urbanisation and decline in land productivity at a high state decreased by 18.14 % and 14.5 %, respectively; furthermore, we also found that the probability of the related nodes of eco-resilience 10

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Fig. 13. Distribution of probabilities for all the nodes for scenario 4.

that the probability of 6.7 % of all the grids at a high state decreased over an area of 87 km2 in the southern regions; the probability of 7.3 % of the grids at a medium state increased over an area of 114 km2 in the western and central regions; and the probability of 16.7 % of the grids at a low state increased over an area of 262 km2 for very few southern regions (Fig. 12). The results of Scenario 4 showed that the probability of the node of threat to public safety and health at a high state decreased by 4.5 %. We also found that the probabilities of the related nodes of water-quality deterioration and soil contamination at a high state decreased by 6 % and 7.8 %, respectively. The probability of the node of earth-surface destruction at a medium state decreased by 5.7 %, and the probabilities of the influenced nodes of soil-quality deterioration and dysfunction in landscape aesthetics at a high state also decreased by 2 % and 3.7 %, respectively. In addition, the probabilities of the nodes of heavy-metal accumulation and shortage in usable land at a low state increased by 15 % and 5.8 %, respectively (Fig. 13). The spatial simulation based on the node of threats to public safety and health showed that the probability of 8.4 % of all the grids at a high state decreased over an area of 135 km2 in the central regions; the probability of 6.2 % of the grid at a medium state also decreased over an area of 103 km2 in the central regions; and the probability of 14.3 % of the grid at a low state increased over an area of 222 km2 in the northwest and southeast regions (Fig. 14).

reduction at a low state increased by 4.9 %. The probability of the node of soil pollution at the medium state increased by 6.1 %, and the probability of the cause node of industrial point-sources pollution at the none state increased by 14.2 %. In addition, the probabilities of the nodes of space occupation and habitat removal at a low state increased by 17.3 % and 35.2 %, respectively; and the probability of the cause node of the mine at a high state increased by 6.83 % (Fig. 7). The space simulation based on the node of urbanisation showed that the probability of 11.5 % of all the grids decreased at a high state over an area of 177 km2; the probability of 15 % of all the nodes decreased at a medium state over an area of 230 km2; and the probability of 13 % and 17.3 % of the grids increased at a low state with no distribution over an area of 199 km2 and 266 km2, respectively (Fig. 8). The results of Scenario 2 showed that the probability of the node of heavy-metal accumulation at a high state was reduced by 27.25 %. We also found that the probability of the cause node of mining at a high state decreased by 4.1 %. The probability of the node of industrial point-sources pollution at a medium state decreased by 14.95 %. In addition, the probabilities of the nodes of eutrophication and organic pollutants at a low state increased by 19 % and 33.6 %, respectively; and the probabilities of the nodes of mining and aquaculture at a none state increased by 33 % and 26.9 %, respectively (Fig. 9). The spatial simulation based on the node of mining showed that the probability of 35.5 % of all the nodes at a high state decreased over an area of 554 km2 in the central and eastern regions, the probability of 52 % of the grids at a medium state decreased over an area of 811 km2 in the western and northern regions, and the probability of 18.5 % and 37 % of the grids at a low state and no distribution increased over an area of 289 km2 and 577 km2, respectively, in the majority of the northcentral regions (Fig. 10). The results of Scenario 3 showed that the probabilities of the nodes of habitat removal and intensive land reclamation at a high state increased by 21.6 % and 13.3 %, respectively. We also found that the probability of the related nodes of urbanisation, transportation, lake area reclamation, and mining at a high state increased by 42.95 %, 2.2 %, 17.36 %, and 15.62 %, respectively. The probability of the node of soil degradation at a low state decreased by 15.1 %, and the probabilities of the related nodes of earth-surface destruction and nutrient runoff at a low state also decreased by 19 % and 12.7 %, respectively. Furthermore, the probability of the node of population overload at a low state decreased by 19.9 % (Fig. 11). The spatial simulation based on the node of habitat removal showed

4. Discussion The BN–GIS model that we have developed is an explanatory tool for providing visual evidence for decision making for the prevention of ecological risks in the ecosystem and can be observed as a new approach to research on land-resource management. The multi-factor investigation in the present research can be used to realise the comprehensive protection of an entire land ecosystem on a large scale. Meanwhile, characterising the complex interactive relationships is helpful in interpreting the mechanism of land degradation and formulating appropriate management suggestions. The BN was combined with the GIS-data layer and prior knowledge to determine the probability relationship of each node. The probabilities of all the target nodes at different states in our study clearly increased or decreased. Meanwhile, the spatial simulation presented clear variations in distribution under the GIS operation. In particular, the spatial visualisation of the area wherein the probabilities occurred 11

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Fig. 14. cenario simulation distribution of public health threat node.

ecological process at the regional scale. In particular, the visualisation of the results obtained using the BN–GIS model helped the stakeholders in examining the effects of strategies proposed to ensure that prior options were considered” instead. Moreover, problems arising owing to land degradation are closely related to regional economic development, and research in this field exhibits dynamically spatial and temporal characteristics. Furthermore, our proposed BN–GIS framework could fit in with frequently updated land-resource data sets.

was helpful in understanding the actual ecological processes. The sensitivity analysis revealed the relationships among the variables and indicated the complex causes of land degradation at the regional scale. For example, the serious degradation of the soil quality is caused by mining, intensive land reclamation, and irrigation because mining produces waste water, solid-waste piles damage the nutrient profile of soil, intensive land reclamation exhausts soil fertility, and abusive irrigation results in soil salinization (Li et al., 2014c; Singh, 2015). In addition, our model was able to identify the key factors for effectively proving our hypotheses, identify knowledge gaps, and provide a direction for future research. Cross-validation greatly enhances the validation of the BN application in various ecosystems (Chen and Pollino, 2012; Marcot, 2012). In our study, the status of calibration and validation (soil contamination: 0.006 ± 0.0793; water-quality deterioration: 0.019 ± 0.153) indicated that the performance was reliable and stable. Our modelling approach may thus be applied to other complicated land ecosystems. The scenario simulation was widely accepted by the land-resource management stakeholders, who were able to express their concerns regarding their respective interests (Ticehurst et al., 2011; Celio et al., 2014). The obvious spatial heterogeneity reflected the close relationships between human economic activities and land

5. Conclusion In this study, a BN-GIS model, which is a spatial BN approach, was established as a decision-making tool for the prevention of ecological risks in a land ecosystem at the regional scale. The model revealed the mechanisms of land-ecosystem degradation by integrating prior knowledge and data collected in the field, which could help policy makers in their work. Meanwhile, our hypothesis regarding the field investigation conducted before the experiment was verified, and it reflects the close relationship between human economic activities and land ecological process. For example, intense irrigation depleted the soil quality (with a relatively high MI of 0.21), thus confirming our 12

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original hypothesis that salinization was responsible. The research involved different stakeholders throughout the process in order to significantly reduce disputes over the measures to be implemented for the prevention of ecological risks. Moreover, this not only ensures the transparency of policy implementation, but also promotes the establishment of a platform for discussing land-resource protection in the future. The specific locations identified via visual simulation helped the officials to identify the priorities in land conservation while avoiding costly blind actions. For example, the simulation in which the most severe measures for limit mining were taken into consideration can be used to reduce the threats to human health over an area of 135 km2 in the mining zone, which is widely recognised by the local residents. The conducted cross-validation indicated that the performance of the BN–GIS model was reliable, and it was effective in the present study. The use of this novel method was more convenient for the development of BN modelling in ecological research. However, the policy-makers’ strategies derived from our model are still required to be evaluated and confirmed in practice for land-resource management. In particular, with respect to the factors of safety of human life, we should focus our attention on the investigation of first-line resources and the environment in mining cities according to each specific situation in order to implement timely measures, and we must not be confined to experimental simulations. However, the BN has some limitations in that it cannot represent feedback loops and dynamic relationships (Uusitalo, 2007; Kelly et al., 2013); these limitations may be addressed in future research.

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Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This research was supported by the National Key R&D Program of China (Grant No. 2018YFB2100702), the National Natural Science Foundation of China (Grant No. 41431178), and the Natural Science Foundation of Guangdong Province, China (Grant No. 2016A030311016). The authors would like to thank Dr Yanfang Liu and Dr Xuesong Kong (Wuhan University, China) for offering data related to the land resources of Daye City. We also thank the Land Resources Bureau of Daye City and Dr Yuan Wan of Hubei Normal University for their kind help with the field investigation. References Aguilera, P.A., Fernandez, A., Ropero, R.F., Molina, L., 2013. Groundwater quality assessment using data clustering based on hybrid Bayesian networks. Stoch. Environ. Res. Risk Assess. 27 (2), 435–447. Ahlqvist, O., Khodke, N., Ramnath, R., 2018. GeoGame analytics - A cyber-enabled petri dish for geographic modeling and simulation. Comput. Environ. Urban Syst. 67, 1–8. Aitkenhead, M.J., Aalders, I.H., 2009. Predicting land cover using GIS, Bayesian and evolutionary algorithm methods. J Environ Manag. 90, 236–250. Ayre, K.K., Landis, W.G., 2012. A bayesian approach to landscape ecological risk assessment applied to the upper grande ronde watershed. Oregon. Hum Ecol Risk Assess. 18, 946–970. Bai, X.M., Shi, P.J., Liu, Y.S., 2014. Realizing China’s urban dream. Nature. 509, 158–160. Beuzen, T., Simmons, J., 2019. A variable selection package driving Netica with Python. Environ. Model. Softw. 115, 1–5. Bien, J.D., Meer, J.T., Rulkens, W.H., Rijnaarts, H.M., 2004. A GIS-based approach for the long-term prediction of human health risks at contaminated sites. Environ. Model. Assess. 9, 221–226. Bryan, B.A., Gao, L., Ye, Y.Q., 2018. China’s response to a national land-system sustainability emergency. Nature 559 (7713), 193–204. Castelletti, A., Soncini-Sessa, R., 2007. Bayesian networks and participatory modelling in water resource management. Environ. Model. Softw. 22 (8), 1075–1088. Celio, E., Koellner, T., Grêt-Regamey, A., 2014. Modeling land use decisions with Bayesian networks: spatially explicit analysis of driving forces on land use change. Environ. Model. Softw. 52, 222–233. Chee, Y.E., Wilkinson, L., Nicholson, A.E., Quintana-Ascencio, P.F., Fauth, J.E., Hall, D.,

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