Multi-Factor GIS Modeling for Solid Waste Dumpsite Selection in Lilongwe, Malawi

Stephen Mandiza Kalisha () and Kondwani Godwin Munthali
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Stephen Mandiza Kalisha: Department of Computing, University of Malawi, Zomba P.O Box 280, Malawi
Kondwani Godwin Munthali: Department of Computing, University of Malawi, Zomba P.O Box 280, Malawi

Sustainability, 2024, vol. 16, issue 3, 1-17

Abstract: Solid waste disposal is an essential component of any waste management system, but finding suitable solid waste disposal sites is seen as the main challenge. The goal of this study was to locate solid waste disposal facilities in Lilongwe that would not present logistical or operational difficulties. We used a hybrid approach involving qualitative and quantitative methods. Primary and secondary data were collected, through interviews, questionnaires, and GPS for coordinates. The study considered 10 suitability factors of slopes, rivers, soil types, built-up areas, airports, forests, wetlands, current dumpsites, roads, and railways weighted using the analytic hierarchy process (AHP). We analyzed site selection techniques, evaluated the current dumpsite, and created a multi-factor geographic information system (GIS) model. This study found that the current dumpsite is dangerous for human health and is close to homes. Additionally, this research reveals that the most popular techniques for choosing the location of a solid waste disposal facility are the AHP and weighted linear combination (WLC) coupled with GIS. Out of 46,283 Ha in Lilongwe, 84.07% are unsuitable, 14.97% are suitable, and 0.96% are highly suitable for solid waste disposal sites. Six sites with capacities ranging from 28 Ha to 94 Ha were identified as optimal to reduce operational costs in areas 55, 44, 58, and 61. Further waste recycling initiatives are recommended to reduce solid waste volume and promote sustainable waste management practices.

Keywords: multi-factor; modeling; solid waste; dumpsites; site selection; sustainability; GIS; AHP; WLC (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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