Multicriteria analysis and logistical grouping method for selecting areas to consortium landfills in Paraiba do Sul river basin, Brazil

Senkiio, Caroline Souza; Ramos, Ana Paula Marques; Simões, Silvio Jorge Coelho; Mendes, Tatiana Sussel Gonçalves

doi:10.1007/s12665-022-10354-4

Cited by 4 publications

(1 citation statement)

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“…Nascimento and Silva [25] highlighted challenges in finding suitable landfill locations in Bauru, a city in the interior of the State. In a broader context, Senkiio et al [26] combined MCDA and logistic analysis to identify suitable areas for consortium landfills in the Paraíba do Sul River basin. Morais et al [27] conducted a statewide assessment of waste disposal in São Paulo city.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Landfill Monitoring and Assessment: A Proposal Combining GIS-Based Analytic Hierarchy Processes and Fuzzy Artificial Intelligence

Loureiro,

Bressane,

Nascimento

et al. 2023

Knowledge

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The global surge in urbanization and population growth has led to a significant increase in municipal solid waste generation, posing a considerable challenge in identifying suitable landfill sites. This study proposes a novel framework that enhances landfill site monitoring and assessment by combining GIS-based hierarchical analytical processes with a fuzzy inference system (FIS). The study employs a systematic approach involving phases such as feature selection, spatial analysis, criteria weighting, FIS building, and a case study conducted in São Paulo State, Brazil. The proposed framework effectively assesses landfill suitability and offers practical recommendations for landfill management and future site selection. This framework provides actionable recommendations for landfill monitoring and assessment, supporting landfill management while minimizing environmental and social impacts. It offers a comprehensive approach to landfill assessment, enhancing the sustainability of waste management practices. Further research can improve the proposed framework by refining feature selection and incorporating real-time data for continuous monitoring. Additionally, exploring the integration of emerging technologies, such as remote sensing and artificial intelligence, can further enhance landfill site monitoring and assessment.

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Section: Related Workmentioning

confidence: 99%