Municipal Landfill Locating using Fuzzy-TOPSIS and Fuzzy-AHP models in GIS: A Case Study of Pakdasht City in Tehran Province

Document Type : Original Article

Authors

1 MSc, Department of Remote Sensing and GIS, Faculty of Earth Sciences, Shahid Chamran University of Ahvaz, Ahvaz, Iran

2 PhD, Department of Remote Sensing and GIS, Faculty of Earth Sciences, University of Shahid Chamran, Ahvaz, Iran

3 PhD, Department of Geography and Urban Planning, Faculty of Literature, University of Shahid Chamran, Ahvaz, Iran

4 Lecturer, Department of GIS and Remote Sensing, Faculty of Earth Sciences, University of Shahid Chamran, Ahvaz, Iran

Abstract

Background: One of the main dilemmas of the human community has been municipal waste landfills. Various methods have been developed to manage municipal wastes in which landfills are the final destination apart from the used technologies. Given the sensitivity of this issue, identifing areas approriate for landfilling requires the application of comprehensive techniques. The objective of the present research is to select the most appropriate place for municipal wastes landfilling using geographical information systems, fuzzy analytic hierarchy process and fuzzy TOPSIS. Methods: Initially the effective factors in choosing landfill location was inquired from various institution and were standardized and analysed using geographical information systems. Then every criterion was weighted according to expert opinion and FAHP (Fuzzy Analytic Hierarchy Process). Then, FTOPSIS (Fuzzy Technique for Order Preference by Similarity to Ideal Solution) method was used to prioritize the options. Each method was programmed in MATLAB. Results: Thirty six suitable options were calculated through running the above mentioned methods. Then, two options that were located in the south east of the current landfilling (in Kulik) and Pakdasht city were selected as the best location for landfilling. Conclusion: Results showed that the selected landfills are located at a better position compared to the current one. And this integrated approach, showed a better performance than others due to taking into account the inexact nature of the phenomena both at weighting and ranking stages.

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