Suitability analysis for siting MSW landfills and its multicriteria spatial decision support system: Method, implementation and case study - Demesouka, O.E.; Vavatsikos, A.P.; Anagnostopoulos, K.P. (Waste management (Elmsford), 2013)
► GIS-based landfill suitability analysis combining AHP and compromise programming methods. ► A prototype ArcGIS add-in that supports Ideal Point Methods has been developed. ► The results of the Manhattan distance metric are close to those estimated by the TOPSIS method. ► Chebyshev distance metric provides significantly different results given that it is a non-compensatory approach. Multicriteria spatial decision support systems (MC-SDSS) have emerged as an integration of geographical information systems (GIS) and multiple criteria decision analysis (MCDA) methods for incorporating conflicting objectives and decision makers’ (DMs’) preferences into spatial decision models. This article presents a raster-based MC-SDSS that combines the analytic hierarchy process (AHP) and compromise programming methods, such as TOPSIS (technique for order preference by similarity to the ideal solution) and Ideal Point Methods. To the best of our knowledge it is the first time that a synergy of AHP and compromise programming methods is implemented in raster-driven GIS-based landfill suitability analysis. This procedure is supported by a spatial decision support system (SDSS) that was developed within a widely used commercial GIS software package. A real case study in the Thrace region in northeast Greece serves as a guide on how to conduct a suitability analysis for a MSW landfill site with the proposed MC-SDSS. Moreover, the procedure for identifying MSW disposal sites is accomplished by performing four computational models for synthesizing the DMs per criterion preferential system. Based on the case study results, a comparison analysis is performed according to suitability index estimations. According to them Euclidean distance metric and TOPSIS present strong similarities. When compared with Euclidean distance metric, TOPSIS seems to generate results closer to that derived by Manhattan distance metric. The comparison of Chebychev distance metric with all the other approaches revealed the greatest deviations.