DecisionSupportSystemsforenvironmentalmanagement_Acasestudyonwastewaterfromagriculture4323.pdf
Decision Support Systems for environmental management:A casestudy on wastewater from agricultureGianluca Masseia,Lucia Rocchia,*,Luisa Paolottia,Salvatore Grecob,c,Antonio BoggiaaaDept.of Agricultural,Environmental and Food Sciences,University of Perugia,Borgo XX Giugno,74,06121 Perugia,ItalybDept.of Economics and Enterprise,Corso Italia,55,95129 Catania CT,ItalycPortsmouth Business School,Operations&Systems Management University of Portsmouth,Portsmouth PO1 3DE,United Kingdoma r t i c l ei n f oArticle history:Received 1 April 2014Received in revised form11 August 2014Accepted 18 August 2014Available online 10 September 2014Keywords:GISMulticriteria analysisGIS-MCDA integrationSpatial Decision Support SystemsModular packagea b s t r a c tDealing with spatial decision problems means combining and transforming geographical data(input)into a resultant decision(output),interfacing a Geographical Information System(GIS)with Multi-Criteria Decision Analysis(MCDA)methods.The conventional MCDA approach assumes the spatial ho-mogeneity of alternatives within the case study area,although it is often unrealistic.On the other side,GIS provides excellent data acquisition,storage,manipulation and analysis capabilities,but in the case ofa value structure analysis this capability is lower.For these reasons,several studies in the last twentyyears have given attention to MCDA-GIS integration and to the development of Spatial Decision SupportSystems(SDSS).Hitherto,most of these applications are based only on a formal integration between thetwo approaches.In this paper,we propose a complete MCDA-GIS integration with a plurality of MCDAmethodologies,grouped in a suite.More precisely,we considered an open-source GIS(GRASS GIS 6.4)and a modular package including five MCDA modules based on five different methodologies.Themethodsincludedare:ELECTREI,Fuzzyset,REGIMEanalysis,AnalyticHierarchyProcessandDominance-based Rough Set Approach.Thanks to the modular nature of the package,it is possible to addnew methods without modifying the existing structure.To present the suite,we applied each module tothe same case study,making comparisons.The strong points of the MCDA-GIS integration we developedare its open-source setting and the user friendly interface,both thanks to GRASS GIS,and the use ofraster data.Moreover,our suite is a genuine case of perfect integration,where the spatial nature ofcriteria is always present.2014 Elsevier Ltd.All rights reserved.1.IntroductionSeveral fields of research may benefit from the integrated use ofgeographical information systems(GIS)and Multi-criteria analysis(MCDA):environmental and land management issues,or territorialand urban analysis,just to give a few examples,face spatial multi-criteriadecisionproblems.In a spatial multi-criteriadecisionproblem,geographical data(input)is combined and transformedinto a resultant decision(output)(Laskar,2003;Malczewski,1999,2006).One method of dealing with this matter is to interface aGeographical Information System(GIS)with Multi-Criteria Deci-sionAnalysis(MCDA)methods(DrobneandLisec,2009;Malczewski,2006).MCDA methods are basic tools in the field of environmentalvaluationand management;environmentalmanagementis amultidimensional challenge,and MCDA is able to support decision-making,involving several different aspects to be taken into accountat the same time.But MCDA methods cannot easily take into ac-count the geographical dimension(Laskar,2003).The conventionalMCDA approach assumes the spatial homogeneity of alternativeswithin the case study area(Figueira et al.,2005),although this isoften unrealistic,because evaluation criteria may vary across thespace(Jankowski,1995;Laskar,2003).If alternativeshave ageographical nature,classifying,ordering or choosing operationsalso depends on their spatial arrangement(Laskar,2003),and bothvalue judgmentsand geographicalinformationare neededtodefine them(Laskar,2003).Spatial MCDAproblemsare,forinstance,location choice or land suitability(Geneletti and vanDuren,2008;Goncalves Gomes and Estellita Lins,2002;Joerinet al.,2001;Johnson,2005;Maniezzo et al.,1998;Ruiz et al.,*Correspondingauthor.Tel.:39 0755857140;fax:39 0755857143.E-mailaddresses:(G.Massei),lucia.rocchiunipg.it(L.Rocchi),(L.Paolotti),salgrecounict.it(S.Greco),antonio.boggiaunipg.it(A.Boggia).Contents lists available at ScienceDirectJournal of Environmental Managementjournal homepage:www.elsev e/jenvmanhttp:/dx.doi.org/10.1016/j.jenvman.2014.08.0120301-4797/2014 Elsevier Ltd.All rights reserved.Journal of Environmental Management 146(2014)491e5042012;Sahnoun et al.,2012;Scheibe et al.,2006),as in the presentapplication.In a Spatial MCDA,geographical data(input maps)is combinedand transformed into a decision(output maps)(Drobne and Lisec,2009;Jankowski,1995;Malczewski,1999).Therefore,both theMCDA framework and GIS possibilitiesare required in spatial,multi-criteria evaluation,and their integration has become one ofthe most useful approaches in environmental management andplanning(Chang et al.,2008;Chen et al.,2010;Papadopoulou-Vrynioti et al.,2013;Rahmanet al.,2012;Zucca et al.,2008).Several studies over the last twenty years have thus focused onMCDA-GIS integration and on the development of Multi-criteriaSpatial Decision Support Systems(MCSDSS)(Chakhar and Martel,2003;Jankowski,1995;Lidouh,2013;Malczewski,2006)as afundamental instrument for managing the environment(Rahmanet al.,2012;Zucca et al.,2008).Web GIS-MCDA applications havealso beendevelopingin very recentyears(BouroushakiandMalczewski,2010;Karnatak et al.,2007).Although several appli-cations and examples of GIS-MCDA integration are found in theliterature,there are fewer studies concerning the development of atheoretical framework(Chakhar and Mousseau,2007,2008).The objective of this study is to present a new MCDA-GIS inte-gration tool and its use in land management problems,as the landapplication of wastewater from agricultural activities.We devel-oped a modular suite(r.mcda)based on different Multi-CriteriaDecision Analysis methodologiesin an open-source GIS(GRASSGIS 6.4)(Massei et al.,2012).The paper is structured as follows:Section 2 describes themethodology;Section 3 presents the case study;Section 4 reportsthe results;Section 5 is the discussion.The paper ends with themain conclusions.2.Methodology:MCDA-GIS integrationThe multi-criteria spatial decision support system(MCSDSS)canbe considered a specific part of the more general group of SpatialDecision Support Systems(SDSS)(Ascough et al.,2002).SDSSs havereceived a great deal of attention from researchers,since theirusefulness in spatial decision problems has been clearly demon-strated(Crossland et al.,1995):SDSSs produce more efficient re-sults in a shorter solution time.An MCSDSS consists of three components(Ascough et al.,2002;Laskar,2003;Malczewski,2010):a geographical database and therelevant management systems,an MCDA model-basedmanage-ment system,and an interface.According to certain authors(Chakhar and Martel,2003;Laskar,2003),it is possible to classify MCDA-GIS integration in three ways.The basic step is MCDA-GIS indirect integration:MCDA and GISmodelsareseparated,andlinkedthroughanintermediateconnection system,handled by the analyst.Each part has its owndatabase and its own interface,which may affect their interaction.This procedure has the advantage of its low development cost,butthe separationof MCDAand GIS partsmakesit difficulttocompletely comprehend the spatial nature of the problem(Lidouh,2013).Moreover,errors may occur during the transfer,due to thehuman element involved(Lidouh,2013).There are some examplesof this type of integration(Cavallo and Norese,2001;Chang et al.,2008;Geneletti,2004),where the complexity of the analysis isnevertheless quite high.The second type of system is representedby MCDA-GIS tools(Laskar,2003),in which the multi-criteriacomponent is integrated into the GIS system,but remains inde-pendent from a logical and functional point of view.In particular,the MCDA part has its own database,whereas the interface is thesame.There is no need for an intermediate system,and the ex-change data and analysis between the two parts are performeddirectly,which is a good step forward compared to indirect inte-gration(Chakhar and Martel,2003).It is a sort of one-directionalintegration(Malczewski,2006),where one of the two softwaresworks as the main software.This type of integration is the one mostsuccessfully applied(Lidouh,2013).It is only at the third level,known as complete,or full MCDA-GISintegration(Greene et al.,2010;Laskar,2003),that the two systemsuse the same interface and the same database.The MCDA model isactivated inside the GIS software in the same way as any otheranalysis function(Chakhar and Martel,2003).In a full-integrationscheme(see Fig.1),the user can access both the MCDA and theGIS tools at any time during analysis,and interaction is complete:itis possible to change the parameters and the methods,visualiseresults or the spatial elements(Lidouh,2013),until the goal of theresearch is achieved.As Lidouh reports(2013),some integration options are alsopossible in well-known commercial software,such as ArcGIS byESRI(Lidouh,2013).The weak point of the applicationsimple-mented in commercial software lies in the very nature of theproducts.In ArcGIS,for instance,the researcher cannot choose thealgorithms he wishes to include,and he cannot improve them sinceArcScripts closed down.Moreover,frontier methods are excluded,since preference is given to the most widely used and most well-known methods.In contrast,open-source options give more pos-sibilities for developing new tools,even though there are few opencomponents(Lidouh,2013).The modular suite r.mcda,based on five different Multi-CriteriaDecisionAnalysismethodologiespresentedinthispaper,isdeveloped in GRASS GIS 6.4 svn(Grass Development Team,2012a,2012b).As all geographical processing in GRASS GIS is carried outby separate modules,we developed our Multi-Criteria tools asmodules.We chose GRASS GIS for our applicationbecause it is anadvanced and well-known open-source GIS software(Frigeri et al.,2011),used for geospatial data management and analysis,imageFig.1.Represents a Decision flowchart for spatial multicriteria analysis,proposed by40.In the flowchart GIS and MCDA parts are clearly tightly bound.G.Massei et al./Journal of Environmental Management 146(2014)491e504492processing,graphics/maps production,spatial modelling and vis-ualisation.Since its first release in 1982(Frigeri et al.,2011),GRASSGIS has been increasingly used by academic and commercial set-tings all around the world,as well as by many governmentalagencies and environmentalconsultingcompanies,for a widerange of possible applications(Estalrich and Trill,1998;GrassDevelopment Team,2012a,2012b;Li et al.,2010;Massei et al.,2012;Neteler and Mitasova,2008).Moreover,it is written in theC language,and its open libraries and GPL license make it possibleto easily develop new modules(Estalrich and Trill,1998;GrassDevelopment Team,2012a,2012b;Neteler and Mitasova,2008).In GRASS GIS,new modules can be added using the C language,Bash Shell and Python(Grass Development Team,2012a,2012b),and then they are available for all the users on the GRASS GIS re-pository.In our application we used the C and Python languages,and it will be possible to add new MCDA modules using theselanguages.The great advantage of the r.mcda suite is the open nature ofGRASS GIS.This allows a real improvement of the tool,because allGRASS GIS users can potentially modify the existing modules orperfectthe algorithmapplied.The possibilityof addingnewmethods is also another great advantage,because it enables thescientific evolution of MCDA to be followed up.The presence of awide range of methods enables the best algorithm to be applied forthe specific problem.The selection of the right method for eachproblem is still an open question in the field(Guitouni and Martel,1998;Roy and Slowinski,2013;Zopounidis and Doumpos,2002).The methods used in the modules1are ELECTRE I(Roy,1991,1997),Fuzzy set(Yager,1977,1988;1993),REGIME(Hinloopenet al.,1986;Nijkamp and Hinloopen,1990),Analytic HierarchyProcess(Saaty,1977,1992)andDominance-basedRoughSetApproach(DRSA)(Greco et al.,2001).This last module in particularrepresents the first implementation of the DRSA in a geographicalcontext.The name of each module,based on GRASS GIS,is structured asfollows:r.mcda.algorithm.Prefix r refers to raster data,mcda isthe name of the suite,whereas the algorithm has to be substitutedby the name of the MCDA method applied.For instance,the modulecorresponding to the ELECTRE I method is named r.mcda.electre,and so on.The modules use and process raster,and therefore theoutputs are raster.The spatial nature of the data is always presentin the multi-criteria process,because the basic unit of analysis is thesingle cell.This is not possible in the case of indirect integration.Each criterion in all the modules is represented by a raster map(criterion map),which describes how the attribute is distributed inspace.Each cell of the GRASS region stands for an alternative,and isdescribed by means of the value assumed for the same cell theraster used as its criterion.The assignment of weights depends onthe method applied.More details about the methods applied in the case study can befound in Sections 2.1e2.3.2.1.r.mcda.fuzzyThis module is an implementation of the fuzzy multi-criteriaclassicalgorithmproposedby Yager(1977,1988;1993)in aGRASS GIS environment.Judgements cannot be clearly defined in afuzzy model,but they coincide with fuzzy subsets.A fuzzy subset isdefined by non-numeric linguistic variables.The weighting processis expressed by linguistic modifiers,such as“much more”or“littlemore”.In the model,the affiliation degree for each alternative isvalued as the degree of achievement of the goals.Different opera-tors are possible to aggregate the objective.The MIN operatorrepresents the intersection(AND)and requires all criteria to havebeen satisfied.Compensatory effects are not feasible.On the otherhand,the MAX operator,representingthe union(OR),allowscompensation.The previoustwo operatorsare quite rigid:acompromise is the ordered weighted averaging(OWA)operator,which allows a judgement to be made for most of the criteria.Theinputs required by the module are the list of the raster representingthe criteria to be assessed in the multi-criteria evaluation and thevector of linguistic modifiers to be assigned.The outputs are threedifferent maps,which are the results of the intersection(or MIN),the union(or MAX)and the ordered weighted averaging(OWA)operators.Some Ordered Weighted Averaging(OWA)applicationsinspatialanalysishavealreadybeenimplemented(e.g.Bouroushaki and Malczewski,2010;Chang et al.,2008;Rahmanet al.,2012).2.2.r.mcda.ahpThis module represents the implementation of the AnalyticalHierarchy Process(AHP),as introduced by Saaty(1977),in GRASSGIS.The AHP is quite a popular decision tool,especially
