Artificial Intelligence implemented to recognize patterns of sustainable areas by evaluating the database of socioenvironmental safety restrictions




Sustainable Development; Environmental Management; Landfill; Bio-inspired Computing; Decision Tree; Artificial Intelligence; Decision matrix.


The several papers recently published, applied to sustainable development, has been considering new methodologies and techniques in identifying the main criteria, in numeric format, that are useful in formulating possible solutions to the solid waste problem. This paper presents the Mathematical and Computational Modeling Process (PM2C), applied in the determination of control variables related to selection of areas destined to the construction of landfills, in order to benefit from new analyzes and values obtained by methods such as AHP (Analytical Hierarchy Process) and GIS (Geographic Information Systems). The main objective of this paper is the use of Artificial Intelligence (AI), through a Decision Tree strategy, as a selective method and optimal solutions in choosing the best area dedicated to the construction of landfills, with the creation and analysis of new values applied to scenarios defined in the paper of Andrade e Barbosa (2015). The results, expressed in analytical and graphical forms, show the individual values for each criterion and new scenarios involved in the phenomena. This paper highlights the importance of incorporating new conditions and criteria to propose a new decision-making rule, simultaneously, associating qualitative and quantitative characteristics, related to social and economic effects, applied to the environment management system. Based on these principles, it was possible to simulate new scenarios that demonstrate, with very high precision, the best values of useful criteria for decision-making in the selection of the optimal area for implementation of a landfill.


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How to Cite

AZANCORT NETO, J. L.; GONÇALVES, A. L. S. .; CRUZ, B. C. C. da .; GOMES, L. L. .; COSTA , D. C. L. . Artificial Intelligence implemented to recognize patterns of sustainable areas by evaluating the database of socioenvironmental safety restrictions. Research, Society and Development, [S. l.], v. 10, n. 10, p. e212101018841, 2021. DOI: 10.33448/rsd-v10i10.18841. Disponível em: Acesso em: 18 oct. 2021.