Combining multicriteria decision analysis with GIS for suitably siting landfills in a Nigerian state - Fidelis O. Ajibade; Olabanji O. Olajire; Temitope F. Ajibade; Nathaniel A. Nwogwu; Kayode H. Lasisi; Ayopo B. Alo; Titilayo A. Owolabi; James R. Adewumi (Environmental and Sustainability Indicators, 2019)
Population explosion coupled with poor governance and land use planning is responsible for indiscriminate dumping of solid waste in unsuitable sites. Despite the vast availability of modern methods of siting landfills, many waste disposal methods in Akure are piled up in open sites and unsafe. Owing to this lack of effectiveness of these common methods in Akure, a better approach is adopted to enable the analysis of a number of required qualitative and quantitative issues for landfill site selection. This study is aimed at identifying suitable sites for solid waste disposal and management while considering all essential factors and rating criteria by integrating GIS with multi-criteria decision analysis (MCDA) in Akure, Ondo State. Standards for siting landfill formulated by the Environment Protection Agency (EPA) were employed in this study. The criteria that are considered herein are land use, slope, distance to drainage, distance to linear features, soil, geology, distance to the residence and road accessibility. These criteria were assigned fuzzy membership classes based on their importance in siting landfill. The fuzzy members of all criteria were overlaid to generate the final landfill site suitability map which was classified into five: not suitable (34.1%), less suitable (50.4%), moderately suitable (0.3%), suitable (0.02%) and highly suitable (15.5%). The Analytical Hierarchy Process (AHP) technique was employed in the selection of the landfill site with reverence to multiple criteria and the fuzzy membership classes in accordance with the standards of the EPA. The result of this study serves as guide for further field survey. Keywords: Landfills, Geographic information systems (GIS), Multiple criteria decision analysis (MCDA), Fuzzy logic