Akademik Veri Yönetim Sistemi
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, cilt.26, sa.30, ss.30711-30730, 2019 (SCI-Expanded)
One of the cheapest and proper methods for the ultimate disposal of Municipal Solid Waste (MSW) is landfilling. However, determining the location of landfill sites is a difficult and complex task due to depending on social, environmental, technical, economic, and legal factors. To solve the aforementioned challenges related to the landfill site suitability analysis, the combinations of Geographic Information System (GIS) and Multi-Criteria Decision-Making (MCDM) have been studied by academia and applied by experts over the years. This notice is apparent by the large number of academic papers which have been announced in the near future. To provide a framework of the existing literature, and to guide colleagues, a state-of-the-art of recent papers is crucial. The goal of this study is to review all scientific papers in GIS-based MCDM modeling for landfill site suitability analysis in academic journals. A total of 106 studies published between 2005 and 2019 are recorded and surveyed. The studies are then investigated and classified by a generated taxonomy including following categories: GIS software, application area, uncertainty, MCDM techniques, cell sizes in GIS, and criteria. Based on the review conducted, it is observed that while Analytical Hierarchy Process (AHP) and Weighted Linear Combination (WLC) are the most widely used MCDM methods for weighting the criteria and ranking the alternatives, respectively. On the other hand, while environmental dimension is the most commonly preferred main criteria, surface water comes first in the sub-criteria pool. Criteria analysis shows that surface and ground water, geology, land use, distance to fault zone, distance to urban areas, and distance to road and slope are the most commonly used criteria groups among others. These classifications and observations are helpful for identifying research gaps in the current literature and provide insights for future modeling and research efforts in the field.