Abstract
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.
Highlights
In the process of maintaining the environment, efficient waste management is essential
This paper presents the Mathematical and Computational Modeling Process (PM2C) applied in the determination of control variables related to the selection of areas for the construction of landfills
The results reveal that despite the 97% accuracy of the K-nearest Neighbors Algorithm (KNN) method, the 99% maximum accuracy of the Decision Tree (DT) is much more reliable when applied together with the Supervised Machine Learning method to predict the presence of cancer
Summary
In the process of maintaining the environment, efficient waste management is essential. One of the main items to be defined is the location of landfills. The methodologies that determine the location must focus, fundamentally, on the prevention of risk and threats to the environment caused by short-term pollution. It is well known that the waste disposal technique is based on collection, processing, recycling, and final disposal. Each country has its particularities in relation to waste production, for Khorram et al (2015), in large parts of cities, waste disposal is done in a basic form of collection and deposited in landfills. Priya et al (2019), state in their paper that, environmental departments have not devoted the necessary attention to the mathematization of this problem, in order to find a sui generis area for the disposal of waste
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