a spatial bayesian-network approach as a decision-making

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Ecological Modelling

journal homepage: www.elsevier.com/locate/ecolmodel

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

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

A R T I C L E I N F O

Keywords:Regional-Scale ecological-risks preventionDecision-Making toolSpatial bayesian networkCross-ValidationLand ecosystem

A B S T R A C T

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

1. Introduction

The functions of a land ecosystem such as supporting vegetativegrowth, producing and maintaining biodiversity, providing habitat forhumans and other organisms, purifying the environment, and pro-tecting soil and water, and modifying climate are important (Ferrettiand Pomarico, 2013; Turner et al., 2016; Huang et al., 2019). The re-duction of the ecological functions of land causes damage to habitats,biodiversity, air, water, and soil, which limit the regional economicdevelopment and human survival (Li et al., 2014a; Bai et al., 2014;Bryan et al., 2018). Thus, the ecological-risk management of landecosystems is attracting attention in research and administrationglobally (Li et al., 2014b; Liang et al., 2017; Kang et al., 2018). Thedecision-making analysis for ecological-risk prevention requiresknowledge of the causes and mechanisms of land degradation and ex-ploration of techniques and methods for controlling and restoring de-graded land ecosystems (Ferretti and Pomarico, 2013; Comino et al.,2014; Turner et al., 2016).

Studies on the prevention of ecological risks in a land ecosystem aregenerally focused on the environment, economy, and social culture (Liet al., 2014; Turner et al., 2016; Guo et al., 2017). The prevention ofenvironmental degradation involves combating erosion, salinizationand 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 deple-tion (Guo et al., 2014; Kibblewhite, 2012; Pries et al., 2008; Xu et al.,2016). The development of an agricultural economy requires the pre-servation of arable land, reclamation of waste land, and maintenanceand improvement of land productivity (ELD-Initiative, 2013; Jacksonet al., 2013). A stable society relies on its land ecosystem for supportingsustainable human well-being (Costanza et al., 2013; Iniesta-Arandiaet al., 2014).

However, previous studies have primarily been focused on the in-dividual aspects of the land ecosystem with discreet results that did notprovide effective guidance for realising the comprehensive protectionof a complex ecosystem. Therefore, an integrative approach to research

https://doi.org/10.1016/j.ecolmodel.2019.108929Received 26 May 2019; Received in revised form 27 December 2019; Accepted 30 December 2019

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

Ecological Modelling 419 (2020) 108929

0304-3800/ © 2020 Elsevier B.V. All rights reserved.

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involving multiple factors that function simultaneously and inter-actively in the entire land ecosystem has been proposed (Guo et al.,2017).

Integrative research on ecological-risk management often dependson the use of appropriate tools or models, and it has been increasinglyattracting the attention of researchers. These researchers in this fieldhave made valuable contributions such as multi-agent systems for thedecision-making processes involved in forest management (Xu et al.,2015; Ahlqvist et al., 2018), artificial intelligence models for simulatingand predicting the risks of a land ecosystem (Liu et al., 2018; Tsai et al.,2018), multi-criteria decision-making models for risk-zoning andmanagement of large areas (Li et al., 2014b; Gallego et al., 2019; Souissiet al., 2019), designs for an integrated risk index that can serve as awarning for implementing the appropriate regional development stra-tegies (Partl et al., 2017; Wang et al., 2019), and environmental riskmapping as guidance for risk minimisation in risk management anddecision making (Ma et al., 2013; Maldonado et al., 2016; Petus et al.,2016).

The aforementioned tools and models, however, can still be furtherimproved when applied to research on land ecosystems. Firstly, theycan be applied primarily in mono-factor stud ies, as has been mentionedin this paper, and they cannot provide motivation for the comprehen-sive risk prevention for a land ecosystem. Secondly, they representlinear relationships between the factors of the land ecosystem and areunfit for research on nonlinear relationships in a land ecosystem (Guoet al., 2015; W.Q. Zhou et al., 2019). Thirdly, they cannot be used tosimultaneously process multiple data. Fourthly, they have been estab-lished based on single isolated issues and do not encourage researchersto formulate extensive and comprehensive hypotheses. Finally, theyhave primarily been designed by researchers without involving therelated stakeholders, and the obtained results cannot be easily under-stood and accepted by the public. To address the above drawbacks, inthis study, we used the Bayesian belief network (BN) model with ageographic information system (GIS), which has been believed to bemore scientific and powerful.

The BN model is a semi-quantitative model that combines ecologicalmodels with expert knowledge, and it has shown its effectiveness instudies on ecological issues (Marcot et al., 2006; 2017; Chen andPollino, 2012). The model reveals the complex ecological mechanismsunderlying the results of previous research (Fienen et al., 2013;McDonald et al., 2015; Franco et al., 2016) and helps researchers topresent various valuable hypotheses (Landuyt et al., 2013; Chee et al.,2016; Marcot and Penman, 2019) that contribute to realising compre-hensive and feasible research designs that then provide convincing re-sults. For the current work of land protection, the emphasis is on theparticipation of different stakeholders (Voinov and Bousquet, 2010;Luyet et al., 2012), and the BN may provide such convenience; variousrelated stakeholders are involved in the research to help in identifyingrealistic and practical research objectives (Krueger et al., 2012; Dicket al., 2018). The BN can process multi-type data, operate in a poordata-collection environment (Uusitalo, 2007; Chen and Pollino, 2012),handle uncertainty (Aguilera et al., 2013), be updated using follow-updata, and be combined with other algorithms for obtaining accuratecalculations (Aitkenhead and Aalders, 2009; Johnson et al., 2011;Marcot and Penman, 2019). Furthermore, with the advantages of itscombination with other software programs, the application of the BNalso presents convenience in testing and validating (Marcot, 2012;Fienen et al., 2013). For example, the technique of cross validation thatis successfully used in BN modelling prevents the occurrence of theoverfitting resulting from overly complex BNs (Marcot, 2012; Beuzenand Simmons, 2019).

However, the results obtained on using the BN model compriseabstract statistical data that research participants cannot understandthe explicit benefits of and based on which they cannot immediatelyand confidently make decisions. Therefore, it has been recommendedthat the BN model be used in combination with a GIS for visualising the

statistical data (Smith et al., 2007; Celio et al., 2014; Gonzalez-Redinet al., 2016) such that researchers can locate the uncertainty and ob-serve the uncertainty level. This solution also helps other people relatedto the research to observed the consequences of ecosystem degradationon a map, which then encourages them to accept proposals for ecolo-gical-risk prevention (Johnson et al., 2011; Chee et al., 2016). In ad-dition, for the BN–GIS approach, further research is required for de-veloping large-scale methods for optimising the models used by landecologists and managers: these include transparent modelling pro-cesses, the application of different types of data, and communicationplatform construction. However, information regarding the utilisationof 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 deci-sion-making tool for the prevention of ecological risks of the landecosystem in the mining city of Daye, China. Netica (Norsys SoftwareCorporation, 1998;2010), a BN software package frequently used inecosystem service modelling research (Chen and Pollino, 2012; Landuytet al., 2013; Forio et al., 2015), was used to evaluate the performance ofthe BN model. The scenario simulation was designed to identify priordecision-making options for stakeholders in land management, andcross-validation was performed to confirm the validity of our assess-ment model.

2. Materials and methods

2.1. Study area

Daye City (114° 31′–115° 20′ E, 29° 40′–30° 15′ N; Fig. 1) is locatedin the south-eastern part of the Hubei Province in Central China. Thisarea is rich in mineral deposits and has well-developed mining andmetallurgy industries. The ecosystem in and around the city is com-prehensive, and it consists of lakes, rivers, forests, mines, arable lands,gardens, and urban and rural residential areas. The entire study area is1566.3 km2. At present, the ever-increasing industries have beenharming the ecosystem in the entire area, which has resulted in dys-functions of the ecosystem services, including soil contamination, waterpollution, 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 StatesEnvironmental Protection Agency (USEPA) with respect to land-eco-system degradation (Suter, 1993; USEPA, 1998) is introduced in thepresent study. Several key factors, including sources, stressors, andendpoints, were identified and selected as research variables. Previouspapers have been reviewed to obtain existing knowledge regardingmodel construction. On the basis of the BN model, a conceptual modelwas developed to determine the cause–effect relationships among thevariables to describe the land degradation in Daye City. The varioustypes of data obtained were spatialized in the GIS. The relationshipsamong the variables were quantified by defining the conditionalprobability table (CPT) of each node. The spatial BN model was com-bined with the GIS for the model operation. Scenarios were simulated toassist decision makers in identifying prior options for risk prevention inthe future. A sensitivity analysis was used to evaluate the ability of ourmodel, and cross-validation was performed for validating the model.Data imperfections and knowledge gaps were identified in the evalua-tion of the model operation to guide future research. These proceduresare 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 BNOn the basis of previous BNs (Cain, 2001; Marcot et al., 2006;

Castelletti and Soncini-Sessa, 2007; Chen and Pollion, 2012), we

K. Guo, et al. Ecological Modelling 419 (2020) 108929

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initially identified all the possible components as variables, includingthe problem formulation and candidate stressors and their possiblesources. We formed a research group before the present study wasstarted. The group members performed field investigations and con-ducted a literature review by reading related papers in Web of Science.

The problem formulation is a critical step in the determination ofthe evaluation range and target of ecological-risk prevention in a landecosystem (Suter, 1993; Guo et al., 2017). The problems identified inthis research reflected the current condition of the land degradation inDaye City. Our problem formulation was based on a comprehensiveliterature review, which aided in conducting related field investigationsand surveys (McCloskey et al., 2011; Chen and Pollion, 2012; Francoet al., 2016). In addition, the problem formulation was supported bysubsequent laboratory examinations and by the historical data providedby related local government departments. Seminars were then con-ducted to refine the identified problems in order to focus on the majorproblems. In the present study, 11 problems were identified as follows:(1) soil-quality deterioration, (2) soil contamination, (3) water

shortage, (4) water-quality deterioration, (5) threats to public safetyand health, (6) biodiversity decrease, (7) eco-resilience reduction (eco-resilience is the ability of a complex eco-system to recover quickly aftersuffering 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 ofthese problems).

The candidate stressors were determined according to the afore-mentioned problems by a panel of experts (i.e., professors and localofficials) from different but related fields (e.g., ecology, agronomy,geology, land resources, and urban development). These stressors havea physical, chemical, and biological effect on the land ecosystem in ourstudy area. Fourteen stressors were identified as follows: (1) atmo-spheric deposition, (2) alteration of the earth-surface runoff, (3) al-teration of underground runoff, (4) destruction of the earth surface, (5)desertification, (6) removal of habitat, (7) space occupation, (8) accu-mulation of heavy metals, (9) organic pollutants, (10) pathogens, (11)soil erosion, (12) soil salinization, (13) nutrient runoff, and (14)

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

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


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eutrophication (see Appendix 2).Candidate risk sources for the land degradation in Daye City were

identified under the following conditions: (i) familiarity with the localeconomic development pattern, (ii) concern with major pollutant-emitting enterprises, (iii) interview conducted with local residentssuffering from past hazardous events, and (iv) potential disturbancesidentified according to our knowledge (USEPA, 2000; Yu et al., 2010;Teng et al., 2014). Twenty sources of stressors were determined asfollows: (1) storms and floods, (2) geological disasters, (3) extremeweather, (4) acid rain, (5) urbanisation, (6) population growth, (7)intensive land reclamation, (8) mining, (9) transportation, (10) waterprojects, (11) lake-area reclamation, (12) industrial point-source pol-lution, (13) solid-waste piles, (14) application of pesticides, (15) ap-plication of fertilizers, (16) application of mulch plastic films, (17)aquaculture, (18) irrigation, (19) lumbering, and (20) grazing (seeAppendix 3).

Generally, in terms of the interactions among the aforementionedproblems, stressors, and sources, the sources released stressors, and thestressors caused problems. These interactions damaged the biotic andabiotic feedback mechanisms of the land ecosystem (Suter, 1993; Guoet al., 2017). Therefore, these interactions should be represented byseveral 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 com-plex interactions among the sources, stressors, and endpoints. Thenodes representing different spatial scales and complexities could benested with each other, and their linkages presented the cause–effectrelationships through the abovementioned causal hypotheses (Jakemanet al., 2006; Chen and Pollion, 2012). The model eventually char-acterised the process of land degradation (i.e., risk formation) by de-scribing several ecological mechanisms. Our model comprised 46 nodesand 64 arrows, the majority of which were included in our model torepresent the general status of land degradation in the study area. Notall the factors mentioned in the model were dealt with in the BN model,but they do represent the complete profile of the land degradation inthe study area. Future research may take into consideration all thefactors that form the general knowledge of ecology (Chen and Pollion,2012).

2.3.2. Spatial BN implementation2.3.2.1. Bayesian networks. A BN is a graphical, multivariate, statisticalmodel that comprises two structural components, namely, (1) aqualitative component that consists of a causal network, which

includes all the important variables and cause–effect interactionsamong these variables, and (2) a quantitative component that consistsof conditional probabilities, which quantify the aforementionedcause–effect relationships (Marcot et al., 2006; Landuyt et al., 2013).BNs are often referred to as directed acyclic graphs; a BN consists ofnodes that visually represent the key variables and arrows thatrepresent the causal relationships among the variables. Each variableis assigned multiple states (Aguilera et al., 2013; Landuyt et al., 2013;Franco et al., 2016). The entire catalogue of these correlationscomprises the CPTs. The strength of the relationships among thevariables is defined in the CPT attached to each node. The CPTsspecify the degree of belief that a node will be in a particular state asdetermined by the state of the parent nodes. The Bayesian theoremencapsulates the mathematical rules governing the propagation ofprobabilities based on the conditional dependency of variablescombined with data to produce posterior probabilities. Thus, the BNis generally used to perform various simulations based on the specifiednode corresponding to the input values and by noting the changes in theprobability distributions of the output nodes (Kininmonth et al., 2010;Grêt-Regamey et al., 2013). The Bayesian theorem is mathematicallyexpressed as follows.

=×

∑ = ×P A B

P A P B AP A P B A

( / )( ) ( / )

( ) ( / )ij j

in

i i1 (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 irepresents 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 inputvariables and uncertain relationships among all the nodes in the model.These uncertainties propagate through the network and result in modelpredictions that explicitly account for uncertainties (Kjaerulff andMadsen, 2008; Pearl et al. (2010); Landuyt et al., 2015). In the currentstudy, the BN modelling software Netica (Norsys Software Corporation,2010) was used to solve the above equation (Uusitalo, 2007; Voinovand Bousquet, 2010). For a detailed description of BNs, readers mayrefer to the studies of Chen and Pollino (2012) and Marcot and Penman(2019).

2.3.2.2. Embedding BN into GIS. Our spatially explicit approach wasused in the decision-making processes by combining BN with GIS whilefollowing 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 theweb version of this article).


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present in the network nodes were determined as a set of GIS variablesin ArcGIS 10.2. This data set was processed by the BN model as a caseand was simulated in Netica. The CPTs were used to quantify therelationships of the related nodes with their parent nodes. Theserelationships depend on the state of each node. The tool usedspatially explicit input data as raster grids and then executed the BNinference calculations for each raster grid cell of the input data (Smithet al., 2007; Johnson et al., 2011). The outputs were expressed as rastermaps, wherein the probability distribution for the target variables wascalculated by BN inference. The spatially explicit results wereacceptable 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 processingField soil from the study area was sampled in 2013. A total of 225

valid samples were obtained from the entire study area. The sampleswere obtained from rural settlements, farmlands, benchlands, and ir-rigation districts surrounding the industrial and mining areas, which

are the main land-use types in Daye City. They also represented theopinions of the research group on field investigation. The soil de-gradation was investigated on the basis of the following indicators: soildegeneration and soil erosion.

Heavy-metal pollution caused by mining and mineral processing hasattracted the attention of several local researchers (Wu et al., 2009; Weiand Yang (2010); Wu et al., 2010; He et al., 2013; Ma et al., 2013; Tenget al., 2014). In view of funding constraints and substandard laboratoryconditions, the research group selected Cu, Pb, and Cd and determinedtheir content levels in the 225 soil samples. Atomic absorption spec-trophotometry was performed in the laboratory to determine the con-tent levels of Cu, Pb, and Cd (Kemper and Sommer, 2002; ViscarraRossel et al., 2006; Ferrier et al., 2009; Ren et al., 2009; Pandit et al.,2010; Liu et al., 2011). Barium chromate spectrophotometry was per-formed to determine the content level of As (Ren et al., 2009; Zhenget 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 theheavy metals (i.e., Cu, Pb, Cd, and As) and thus obtain the Nemerowcomposite index. The aforementioned soil-component measurementsand heavy-metal content data based on geographical coordinates (i.e.,global positioning system data) of the soil samples, as well as the re-mote-sensing image and elevation data of the study area were processedas spatial data and integrated in a GIS.

Based on interviews and a survey conducted by the DayeEnvironmental Protection Bureau, the research group obtained thequarterly monitoring data of the water quality in water bodies locatedat 37 monitoring points, including the main rivers, lakes, and sensitivewaters, from 2013 to 2016. The group selected ammonia–nitrogencontent, eutrophication, and heavy-metal pollution as the three criteriafor water-quality deterioration. The group then determined the level ofthe water-quality degradation (i.e., heavy, moderate, or light pollution)based on the average level calculated from the data collected in thefour-year period from 2013–2016. GIS spatial processing was per-formed for the selected indices based on the geographical informationof the sampling points.

The research group along with the Daye City Land Resources Bureaufrequently organised investigations on ecological-environment damagecaused by mining in Daye City from 2013 to 2016. The investigationsites included copper, iron, coal, gold, and silver mines, as well as re-lated smelting sites, ore dressing sites, quarries, tailing reservoirs, coalgangue dumps, and open metal mines. The problems included over 800damages to the earth surface and vegetation, 349 geological hazards

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

Parent nodes Intermediate nodes

Mining Application ofpesticides

Industrial pointsourcespollution

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

0 0 0 0.99 0.01 00 0 0-3 0.77 0.19 0.040 0 3-6 0.65 0.23 0.120 0 6-9 0.51 0.28 0.210 0–3 0 0.84 0.15 0.010 0–3 0-3 0.67 0.24 0.090 0–3 3-6 0.56 0.28 0.160 0–3 6-9 0.38 0.35 0.270 3–6 0 0.79 0.13 0.080 3–6 0–3 0.69 0.16 0.150 3–6 3–6 0.50 0.3 0.20 3–6 6–9 0.24 0.42 0.340 6–9 0 0.68 0.21 0.110 6–9 0–3 0.56 0.23 0.210 6–9 3–6 0.36 0.36 0.280 6–9 6–9 0.17 0.44 0.39… … … 　 … … …

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


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(e.g., collapse, gob areas, excavation, landslide, and water depletion),550 land damages caused by solid-waste dumping, and 1294 landplaques covering an area of 6943.58 ha. In terms of actual harm, theresearch group conducted a field investigation, plotting, and performedscene photography to obtain the information of each plot. An expertgroup performed intra-industry interpretation, on-site verification, andhazard-level classification. Finally, the research group performed spa-tial processing based on the statistical information of each plot.

The data for waste-water discharge and solid-waste piling were re-gional and could not be spatially processed. Therefore, we used “in-dustrial point-source pollution” as a variable to present the dischargeand piling. This variable was characterised by the kernel density dis-tribution in the GIS.

Threats to public safety and health are an important ecologicalfunction indicator (Cheng and Nathanail, 2009; Pinedo et al., 2014;Huang et al., 2016). In view of the characteristics of the traditionalmining city of Daye, the main threat is posed by mines that causeheavy-metal pollution and water pollution and result in geologicalhazards in the surrounding environment. The health risk was evaluatedvia an exposure-risk assessment (Korre et al., 2002; Gay and Korre,

2006; Li et al., 2014d). The extent of the impact of heavy-metal pol-lution was considered, and the severity of the threat to human healthwas determined based on the Euclidean distance in the GIS (Bien et al.,2004; Hooker and Nathanail, 2006; Morra et al., 2006; Poggio andVrščaj, 2009).

The data application of the land-use category was based on the land-use maps of Daye City of 2013. A large amount of data was obtainedfrom the Daye Statistical Yearbook and from social surveys, such asdata collected by the Daye Environmental Protection Bureau, and fromthe historical information of Daye (1949–2010). The data regarding thevariables affecting the land degradation in Daye City were too complexfor general models to process; nevertheless, the BN model couldmanage these data (Landuyt et al., 2013). All the above processes wereperformed in ArcGIS 10.2.

2.4.2. Parameter designThe strength of the relationships between the nodes was quantified

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

Target node Sensitive node MI

Soil-quality deterioration Nutrient runoff 0.31Intensive reclaim 0.24Irrigation 0.21Mining 0.17

Soil contamination Mining 0.29Heavy-metal accumulation 0.23Solid-waste pile 0.12

Water quality deterioration Aquaculture 0.19Industrial point-sources pollution 0.08

Water shortage Lake-area reclamation 0.13Irrigation 0.18Alteration of underground runoff 0.05

Threats to public safety and health Soil contamination 0.22Earth-surface destruction 0.13Heavy-metal accumulation 0.08Mining 0.05

Shortage in usable land Habitat removal 0.62Urbanisation 0.51Solid-waste pile 0.38

Eco-resilience reduction Habitat removal 0.71Lake-area reclamation 0.16Urbanisation 0.47

Biodiversity decrease Habitat removal 0.55Urbanisation 0.46Lake-area reclamation 0.24Lumbering 0.31

Decline in land productivity Habitat removal 0.32Lake-area reclamation 0.28Water shortage 0.10

Dysfunction in landscape aesthetics Solid-waste pile 0.23Habitat removal 0.18

Table 3Validation results for ten-fold cross validation.

Current_Fold cal_s val s cal_w val_w

1 0.6505 0.6565 0.6150 0.70702 0.6470 0.6170 0.6070 0.58753 0.6285 0.6518 0.6150 0.54974 0.6311 0.6179 0.6018 0.50935 0.6378 0.5887 0.5728 0.56466 0.6325 0.6446 0.5868 0.53277 0.6281 0.6977 0.5831 0.49678 0.6328 0.6471 0.5696 0.46439 0.6383 0.6073 0.5737 0.445210 0.6443 0.5650 0.5805 0.4273

Fig. 5. Calibration and validation of skill values for various arrangements ofbins on the land degradation BNs. Sets are the set identifiers for the dividedfolders. Skillmean represents the mean value in the dataset. The approximationbetween the values of calibration and verification verifies the accuracy of themethod.

Fig. 6. Calibration and validation of skill values for various arrangements ofbins on the land degradation BNs. Sets are the set identifiers for the dividedfolders. Skillmean represents the mean value in the dataset. The approximationbetween the values of calibration and verification verifies the accuracy of themethod.


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in the CPTs attached to each node. The relationships between the statesof parent and child nodes were quantified within the CPTs that pre-sented 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 ofmarginal nodes were determined based on their own probability dis-tributions (Marcot et al., 2006; Chen and Pollino, 2012).

To ensure that the states of the parent and child nodes interactedlogically and to avoid bias, a combination of empirical data, laboratorydata, past experiences, expert opinions, and a literature review wasused to estimate the conditional probabilities (Smith et al., 2007;

Pollino et al., 2007b). The ranges of 0–5, 5–10, and 10–20 representvariables under low, medium, and high conditions, respectively. Forexample, the CPT for heavy-metal accumulation (Table 1) comprisedthree states (i.e., high, medium, and low), which were determined bythree parent nodes (i.e., application of pesticides, industrial pointsources, and mining).

2.5. Model application

2.5.1. BN sensitivityA sensitivity analysis was performed to measure the sensitivity of

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

Fig. 8. Scenario simulation distribution of urban nodes.


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the changes in the probabilities of query nodes when the parametersand inputs changed (Marcot et al., 2006). Two types of sensitivityanalyses were performed to identify the relative influence of the vari-ables in the network Kjaerulff and Madsen, 2008; Norsys (1998). En-tropy H(X) is often used to evaluate the uncertainty or randomness of avariable X and is characterised by a probability distribution P(x), asfollows (Korb and Nicholson, 2004; Pollino et al., 2007a):

∑= = −=

H X H x x P x log x( ) ( , ..., ) ( ) ( )i ni

n

i i1 (2)

where X and x represent a variable; i represents a particular vari-able, and n denotes the number of all the variables. The entropy mea-sures were used to assess the average information required in additionto the current knowledge to specify a particular alternative. The mostuncertain variables were identified according to the ranked prob-abilities (Pollino et al., 2007a).

Mutual information (MI) was used to measure the effect of onevariable X on another Y (Korb and Nicholson, 2004) as follows:

= −I X Y H X H X Y( , ) ( ) ( / ) (3)

where I(X,Y) was the MI between the variables. This measure re-ported the expected degree of divergence of the joint probability of Xand Y from what it would be if X was independent of Y (Korb andNicholson, 2004). If I(X,Y) was equal to zero, X and Y were mutuallyindependent (Pearl, 1988; Johnson et al., 2010b). To determine thedegree of independence between the variables in a pair in the BN, weperformed a sensitivity analysis.

2.5.2. Model validationThe validation is performed to assess the predictive performance of

BN models (Fienen and Plant, 2015; Forio et al., 2015). We applied k-fold cross validation (KFCV), which can handle the problem of over-fitting and complexity in BN models. In the KFCV, firstly, the datasetwas randomly split into k groups. Each group was considered as thevalidation set for model building, and the remaining groups weretreated as a training dataset for evaluating the built models. This pro-cess was repeated k times, and the performances of the BN models werethen summarised. k was considered as 10, which is a value that waswidely adopted in previous studies (Marcot, 2012; Beuzen andSimmons, 2019).

The performance of the BN models was assessed based on a usefulstatistic, namely, the skill (sk). The value of sk was between 0 and 1,

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

= ⎡⎣⎢

− ⎤⎦⎥

skσσ

1 e2

02 (4)

where σe2was the mean squared error between the observations and σ0

2

predictions, and was the variance of the observationsWe selected twovariables for the validation skill analysis in the BN simulation, namely,soil pollution and water-quality deterioration.

2.5.3. Scenario designAn important finding of this research was that the BN model could

be directly used as a management tool by simply setting the state of anendpoint to a desired level and, thereby, essentially solving the model“backwards” (Ayre and Landis, 2012; Landuyt et al., 2013). Subtlechanges in environment management may result in large changes in thesimulation for sensitive endpoints. This inherent flexibility of use makesthe model a powerful tool for resource management because alternativemanagement scenarios can be easily evaluated for the desired objec-tives. In the current study, four different scenarios were designed whiletaking into consideration the interests of various stakeholders to ensurethat the simulation results could be referred to by stakeholders for fa-vourable decision making.

Scenario 1: The Land Resources Department was the most concernedregarding the amount of land resource supply, which is closely relatedto regional economic development. The node of usable-land shortagewas thus set at an extremely low state to identify changes in theprobability distribution of the relevant variables.

Scenario 2: The Environmental Protection Department was primarilyconcerned with soil pollution and water pollution. The nodes of soilcontamination and water-quality deterioration were thus set at an ex-tremely low state.

Scenario 3: The Agricultural Production Department was most in-terested in land productivity. The node of land productivity was thus setat an extremely high state.

Scenario 4: On the basis of the sustainable development of resource-exhausted cities, the local government seemed to value mining. Thenodes of mining were thus set at an extremely low state.

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


8

3. Results

3.1. Baseline training

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

3.2. Sensitivity analysis

The node of soil-quality degradation was the most sensitive con-cerning nutrient runoff, intensive land reclamation, irrigation, andmining (MI: 0.31, 0.24, 0.21, and 0.17, respectively). However, prior to

the experiment, the expected sources of soil-quality degradation com-prised the application of pesticides and fertilizers, the MI of which were0.07 and 0.11, respectively. The node of water-quality deteriorationwas the most sensitive to aquaculture instead of the industrial pointsource, and the corresponding MI was rather high at 0.19. The node ofwater shortage was the most sensitive to irrigation instead of lake-areareclamation (MI: 0.18 and 0.13, respectively). The nodes of the soilcontamination, earth-surface destruction, and heavy-metal accumula-tion 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 sensitiveto lake-area reclamation and mining. For example, the nodes of watershortage, eco-resilience reduction, biodiversity decrease, and land-productivity reduction were all sensitive to the lake-area reclamation(MI: 0.13, 0.16, 0.24, and 0.28, respectively; Table 2).

3.3. Model performance

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

Fig. 10. Scenario simulation distribution of mining node.


9

through a ten-fold cross validation. The small differences in the per-formance of the test and training sets suggest that our BN overfitting isminimal. For example, the performance of the soil contamination binevaluated through one-, four-, and six-fold cross-validation was similar.However, the performance of water-quality deterioration was lessstable in the nine- and ten-fold cross-validations (Table 3).

Figs. 5 and 6 depict the changes in calibration and validation per-formance in the ten-fold cross validation. In terms of the performance ofsoil contamination, no significant change was found in the calibrationline, and the verification line reached its peak value of 0.6976 at 7 bins

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

3.4. Scenario analysis

The results of Scenario 1 showed that the probabilities of the nodesof urbanisation and decline in land productivity at a high state de-creased by 18.14 % and 14.5 %, respectively; furthermore, we alsofound that the probability of the related nodes of eco-resilience


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


10

reduction at a low state increased by 4.9 %. The probability of the nodeof soil pollution at the medium state increased by 6.1 %, and theprobability of the cause node of industrial point-sources pollution at thenone state increased by 14.2 %. In addition, the probabilities of thenodes of space occupation and habitat removal at a low state increasedby 17.3 % and 35.2 %, respectively; and the probability of the causenode of the mine at a high state increased by 6.83 % (Fig. 7).

The space simulation based on the node of urbanisation showed thatthe probability of 11.5 % of all the grids decreased at a high state overan area of 177 km2; the probability of 15 % of all the nodes decreased ata 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 distributionover an area of 199 km2 and 266 km2, respectively (Fig. 8).

The results of Scenario 2 showed that the probability of the node ofheavy-metal accumulation at a high state was reduced by 27.25 %. Wealso found that the probability of the cause node of mining at a highstate decreased by 4.1 %. The probability of the node of industrialpoint-sources pollution at a medium state decreased by 14.95 %. Inaddition, the probabilities of the nodes of eutrophication and organicpollutants at a low state increased by 19 % and 33.6 %, respectively;and the probabilities of the nodes of mining and aquaculture at a nonestate increased by 33 % and 26.9 %, respectively (Fig. 9).

The spatial simulation based on the node of mining showed that theprobability of 35.5 % of all the nodes at a high state decreased over anarea 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 inthe 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 areaof 289 km2 and 577 km2, respectively, in the majority of the north-central regions (Fig. 10).

The results of Scenario 3 showed that the probabilities of the nodesof habitat removal and intensive land reclamation at a high state in-creased by 21.6 % and 13.3 %, respectively. We also found that theprobability of the related nodes of urbanisation, transportation, lakearea reclamation, and mining at a high state increased by 42.95 %, 2.2%, 17.36 %, and 15.62 %, respectively. The probability of the node ofsoil degradation at a low state decreased by 15.1 %, and the prob-abilities of the related nodes of earth-surface destruction and nutrientrunoff at a low state also decreased by 19 % and 12.7 %, respectively.Furthermore, the probability of the node of population overload at alow state decreased by 19.9 % (Fig. 11).

The spatial simulation based on the node of habitat removal showed

that the probability of 6.7 % of all the grids at a high state decreasedover 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 thewestern and central regions; and the probability of 16.7 % of the gridsat a low state increased over an area of 262 km2 for very few southernregions (Fig. 12).

The results of Scenario 4 showed that the probability of the node ofthreat to public safety and health at a high state decreased by 4.5 %. Wealso found that the probabilities of the related nodes of water-qualitydeterioration and soil contamination at a high state decreased by 6 %and 7.8 %, respectively. The probability of the node of earth-surfacedestruction at a medium state decreased by 5.7 %, and the probabilitiesof the influenced nodes of soil-quality deterioration and dysfunction inlandscape aesthetics at a high state also decreased by 2 % and 3.7 %,respectively. In addition, the probabilities of the nodes of heavy-metalaccumulation 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 safetyand health showed that the probability of 8.4 % of all the grids at a highstate decreased over an area of 135 km2 in the central regions; theprobability of 6.2 % of the grid at a medium state also decreased overan 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 thenorthwest and southeast regions (Fig. 14).

4. Discussion

The BN–GIS model that we have developed is an explanatory toolfor providing visual evidence for decision making for the prevention ofecological risks in the ecosystem and can be observed as a new ap-proach to research on land-resource management. The multi-factor in-vestigation in the present research can be used to realise the compre-hensive protection of an entire land ecosystem on a large scale.Meanwhile, characterising the complex interactive relationships ishelpful in interpreting the mechanism of land degradation and for-mulating appropriate management suggestions.

The BN was combined with the GIS-data layer and prior knowledgeto determine the probability relationship of each node. The prob-abilities of all the target nodes at different states in our study clearlyincreased or decreased. Meanwhile, the spatial simulation presentedclear variations in distribution under the GIS operation. In particular,the spatial visualisation of the area wherein the probabilities occurred



11

was helpful in understanding the actual ecological processes. The sen-sitivity analysis revealed the relationships among the variables andindicated the complex causes of land degradation at the regional scale.For example, the serious degradation of the soil quality is caused bymining, intensive land reclamation, and irrigation because miningproduces waste water, solid-waste piles damage the nutrient profile ofsoil, intensive land reclamation exhausts soil fertility, and abusive ir-rigation results in soil salinization (Li et al., 2014c; Singh, 2015). Inaddition, our model was able to identify the key factors for effectivelyproving our hypotheses, identify knowledge gaps, and provide a di-rection for future research. Cross-validation greatly enhances the vali-dation of the BN application in various ecosystems (Chen and Pollino,2012; Marcot, 2012). In our study, the status of calibration and vali-dation (soil contamination: 0.006±0.0793; water-quality deteriora-tion: 0.019±0.153) indicated that the performance was reliable andstable. Our modelling approach may thus be applied to other compli-cated land ecosystems. The scenario simulation was widely accepted bythe land-resource management stakeholders, who were able to expresstheir concerns regarding their respective interests (Ticehurst et al.,2011; Celio et al., 2014). The obvious spatial heterogeneity reflectedthe close relationships between human economic activities and land

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

5. Conclusion

In this study, a BN-GIS model, which is a spatial BN approach, wasestablished as a decision-making tool for the prevention of ecologicalrisks in a land ecosystem at the regional scale. The model revealed themechanisms of land-ecosystem degradation by integrating priorknowledge and data collected in the field, which could help policymakers in their work. Meanwhile, our hypothesis regarding the fieldinvestigation conducted before the experiment was verified, and it re-flects the close relationship between human economic activities andland ecological process. For example, intense irrigation depleted thesoil quality (with a relatively high MI of 0.21), thus confirming our

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


12

original hypothesis that salinization was responsible. The research in-volved different stakeholders throughout the process in order to sig-nificantly reduce disputes over the measures to be implemented for theprevention of ecological risks. Moreover, this not only ensures thetransparency of policy implementation, but also promotes the estab-lishment of a platform for discussing land-resource protection in thefuture. The specific locations identified via visual simulation helped theofficials to identify the priorities in land conservation while avoidingcostly blind actions. For example, the simulation in which the mostsevere measures for limit mining were taken into consideration can beused to reduce the threats to human health over an area of 135 km2 inthe mining zone, which is widely recognised by the local residents. Theconducted cross-validation indicated that the performance of theBN–GIS model was reliable, and it was effective in the present study.The use of this novel method was more convenient for the developmentof BN modelling in ecological research. However, the policy-makers’strategies derived from our model are still required to be evaluated andconfirmed in practice for land-resource management. In particular,with respect to the factors of safety of human life, we should focus ourattention on the investigation of first-line resources and the environ-ment in mining cities according to each specific situation in order toimplement timely measures, and we must not be confined to experi-mental simulations. However, the BN has some limitations in that itcannot represent feedback loops and dynamic relationships (Uusitalo,2007; Kelly et al., 2013); these limitations may be addressed in futureresearch.

Declaration of Competing Interest

The authors declare that they have no known competing financialinterests or personal relationships that could have appeared to influ-ence the work reported in this paper.

Acknowledgements

This research was supported by the National Key R&D Program ofChina (Grant No. 2018YFB2100702), the National Natural ScienceFoundation of China (Grant No. 41431178), and the Natural ScienceFoundation of Guangdong Province, China (Grant No.2016A030311016). The authors would like to thank Dr Yanfang Liuand Dr Xuesong Kong (Wuhan University, China) for offering data re-lated to the land resources of Daye City. We also thank the LandResources Bureau of Daye City and Dr Yuan Wan of Hubei NormalUniversity for their kind help with the field investigation.

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