An Advanced Analytical Approach for Landfill Site Selection in Rajshahi City for Solid Waste Management

Municipal solid waste (MSW) management has become a critical challenge in rapidly urbanizing regions, particularly in developing countries such as Bangladesh, where increasing population density and limited land availability intensify the pressure on existing waste disposal systems. In this context, selecting an environmentally sustainable and operationally efficient landfill site is essential for improving long-term waste management strategies. This study proposes a comprehensive decision-support framework for the strategic selection of landfill sites within the Rajshahi City Corporation (RCC) area. The framework integrates two core methodologies: a Genetic Algorithm (GA) and a Random Forest (RF) regression model. The genetic algorithm is employed to optimize site suitability by evaluating key criteria such as distance from Secondary Transfer Stations (STS), available land capacity, accessibility, and transportation considerations. A specially designed fitness function is incorporated into the GA to identify the most optimal site from a set of potential locations. To enhance reliability and provide cross-validation of the GA results, a Random Forest regression model is used to analyze feature importance and assess the influence of each parameter on the final site selection outcome. The RF results confirm that transport cost, distance from STS, and landfill capacity are among the most influential factors in determining site suitability. Based on the integrated analysis, the Baya-Duary region emerges as the most favorable landfill site, exhibiting strong environmental compatibility, adequate land availability, and high operational feasibility. The proposed framework demonstrates a robust and data-driven approach that can support urban planners and policymakers in making informed and sustainable MSW disposal decisions.