Optimizing landfill site selection using fuzzy-AHP and GIS for sustainable urban planning

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Careful landfill selection with minimal environmental impact is vital for urban planners. This study aims to identify suitable sites for controlled landfills using Fuzzy-AHP integrated with Remote Sensing and GIS, considering a 20-year projection of population and solid waste generation. Initially,...

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Detalles Bibliográficos
Autores: Zabaleta Santisteban, Jhon Antony, Salas López, Rolando, Rojas Briceño, Nilton B., Gómez Fernández, Darwin, Medina Medina, Angel J., Tuesta Trauco, Katerin M., Rivera Fernandez, Abner S., Lévano Crisóstomo, José, Oliva Cruz, Manuel, Silva López, Jhonsy O.
Formato: artículo
Fecha de Publicación:2024
Institución:Instituto Nacional de Innovación Agraria
Repositorio:INIA-Institucional
Lenguaje:inglés
OAI Identifier:oai:null:20.500.12955/2626
Enlace del recurso:http://hdl.handle.net/20.500.12955/2626
https://doi.org/10.28991/cej-2024-010-06-01
Nivel de acceso:acceso abierto
Materia:Landfill locations
F-AHP
Remote sensing
Geographic Information systems
Suitability model
https://purl.org/pe-repo/ocde/ford#4.05.00
Landfills
Descripción
Sumario:Careful landfill selection with minimal environmental impact is vital for urban planners. This study aims to identify suitable sites for controlled landfills using Fuzzy-AHP integrated with Remote Sensing and GIS, considering a 20-year projection of population and solid waste generation. Initially, twelve sub-criteria were identified, grouped into environmental, socio-economic, and physical categories, and then weighted using paired comparison matrices involving nine experts. The sub-criteria were rasterized and classified into four suitability levels. The weighted overlay of sub-criteria maps generated a territorial suitability model. Within the Alto Utcubamba Commonwealth (Amazonas, Peru), 0.069%, 41.70%, 66.934%, 0.20%, and 12.4% of the territory are suitable, moderately suitable, less suitable, unsuitable, and restricted, respectively, for landfill establishment. Subsequently, 16 highly suitable sites were selected based on the required area (S4 polygons ≥ 0.505 ha) in line with the projected solid waste generation over 20 years. Of the 16 selected areas, only 15 met the shape index. The model showed high accuracy (AUC = 0.784) during validation. Furthermore, this study provides a comprehensive framework for making decisions about waste management in developing countries, enhancing understanding of key factors in selecting landfill sites. It also offers a deeper insight into global and local factors that determine the suitability of landfill sites.
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