A GIS-based novel approach for suitable sanitary landfill site selection using integrated fuzzy analytic hierarchy process and machine learning algorithms.

Abstract

Disposal of waste without treatment is the least preferable way of sustainable solid waste management (SWM). But most cities in developing nations still use open dumps, causing negative impacts on the environment and human health. This study offered a novel approach for selecting landfill sites and sustainable SWM in Aligarh city, India. This was done through data collection, selecting models for criterion weighting, and validation. In order to prepare a landfill site suitability map, a geographic information system (GIS)-based ensemble fuzzy analytic hierarchy process-support vector machine (FAHP-SVM) and fuzzy analytic hierarchy process-random forest (FAHP-RF) models were implemented. Considering the previous studies and the study area characteristics, eighteen thematic layers were selected. The result revealed that land value; distance from residential roads, hospitals and clinics, and waste bins; and normalized difference built-up index (NDBI) have a fuzzy weight greater than 0.10, indicating significant factors. In contrast, land elevation, land slope, surface temperature, soil moisture index, normalized difference vegetation index (NDVI), and urban classification have a zero fuzzy weight, indicating these criteria have no importance. The result further revealed that FAHP-RF with an area under curve (AUC) value of 0.91 is the more accurate model than FAHP-SVM. According to the final weight-based overlay result, seven potential landfill sites were identified, out of which three were determined as most suitable by considering current land cover, public opinions, and environmental and economic concerns. This research proposed a zonal division model based on landfill sites location for sustainable SWM in Aligarh city. However, the findings may provide a guideline to the decision-makers and planners for optimal landfill site selection in other cities of developing countries.