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Bui, D.T., Bui, Q.-T., Nguyen, Q.-P., Pradhan, B., Nampak, H., and Trinh, P.T. (2017) A Hybrid Artificial Intelligence Approach Using GIS-Based Neural-Fuzzy Inference System and Particle Swarm Optimization for Forest Fire Susceptibility Modeling at a Tropical Area. Agricultural and Forest Meteorology, 233, 32-44.
https://doi.org/10.1016/j.agrformet.2016.11.002

has been cited by the following article:

• TITLE: Modeling Environmental Susceptibility of Municipal Solid Waste Disposal Sites: A Case Study in São Paulo State, Brazil

  AUTHORS: Victor Fernandez Nascimento, Anahi Chimini Sobral, Pedro R. Andrade, Jean Pierre Henry Balbaud Ometto, Nazli Yesiller

  KEYWORDS: Modeling, Geographic Information System (GIS), Environmental Impact, Municipal Solid Waste, Landfills, Multi-Criteria Decision Analysis (MCDA), Analytic Hierarchy Process (AHP)

  JOURNAL NAME: Journal of Geographic Information System, Vol.9 No.1, February 21, 2017

  ABSTRACT: The large excess of solid waste generated in cities is a result of population growth and economic development. Properly managing this municipal solid waste (MSW) is a challenge, mainly in underdeveloped and developing countries where financial concerns are an added problem. From the environmental point of view, a major issue is properly disposing MSW taking into consideration a wide range of factors, and working with different spatial data. In this study, we used geographic information system (GIS) to perform multi-criteria decision analysis (MCDA) conducted by analytical hierarchy process (AHP). The development of the environmental impact susceptibility model (EISM) for municipal solid waste disposal sites (MSWDS) applied to the state of Sao Paulo, Brazil considered factors such as geology, pedology, geomorphology, water resources, and climate represented by fifteen associated sub-factors. The results indicated that more than 82% of Sao Paulo’s territory is situated in areas with very low, low, and medium environmental impact susceptibility categories. However, in the remaining 18% of the state land area, 85 landfills are located in areas with high and very high environmental impact susceptibility categories. These results are alarming because these 85 landfills receive approximately 17,886 tons of MSW on a daily basis, which corresponds to 46% of all municipal solid waste disposed in Sao Paulo state. Therefore, decision makers, urban planners and policymakers could use the findings of the EISM towards mitigating the environmental impacts caused by MSWDS.