GIS-Based Multi-Criteria Decision Analysis (MCDA) For Optimal Solid Waste Disposal Site Selection In Jalingo Metropolis, Taraba State, Nigeria
Keywords:
Analytical Hierarchy Process, GIS, Jalingo Metropolis, Landfill Siting, Multi-Criteria Decision Analysis, Solid Waste ManagementAbstract
Rapid population growth and urban expansion in Jalingo Metropolis, Taraba State, Nigeria, have led to a significant increase in municipal solid waste generation, creating serious environmental and public health challenges. The absence of scientifically selected landfill sites has contributed to indiscriminate waste disposal, land degradation, and potential contamination of nearby water bodies. This study therefore aimed to identify environmentally suitable locations for solid waste disposal using a Geographic Information System (GIS)-based Multi-Criteria Decision Analysis (MCDA) framework. Four key criteria—road accessibility, proximity to drainage networks, buffers around built-up areas, and topographic conditions—were selected in accordance with environmental guidelines and international best practices for landfill siting. Spatial datasets, including QuickBird satellite imagery, Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and municipal geospatial datasets, were processed using ILWIS, ArcGIS, and IDRISI Taiga. The Analytical Hierarchy Process (AHP) was applied to determine the relative importance of the criteria, while Boolean overlay techniques were used to define constraint layers. A Weighted Linear Combination (WLC) model was subsequently employed to integrate the criteria and generate a landfill suitability map for the study area. The suitability analysis classified the study area into three categories: highly suitable, moderately suitable, and unsuitable zones for landfill development. Results indicate that 28% of the study area is highly suitable, 17% moderately suitable, and 55% unsuitable for landfill siting. Five potential sites were identified, with Site 3 ranked most suitable due to its accessibility, minimal environmental impact, and land availability. Sensitivity analysis confirmed the robustness of the model. Findings provide a scientific basis for evidence-based urban solid waste management, offering a practical decision-support tool for local authorities.
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