Computational Analysis of Environmental Risk Conditioners
- 1 UFJF Federal University of Juiz de Fora, Brazil
Copyright: © 2020 Barbara Carla Coelho Batista, João Victor Dutra Balboa, Caio Vincenzo Reis Dima, Jordan Henrique de Souza, Marcelo Caniato Renhe, Gislaine dos Santos and Luciana Conceição Dias Campos. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
The intense urbanization process since the 1970s, coupled with the lack of adequate housing and social policies, has led large urban centers to disordered occupations and situations of geotechnical risk. These occupations were not implemented in a technically correct manner from the point of view of civil engineering, considering landscaping, drainage and paving, as well as edification. Areas at risk are regions where it is not recommended to build houses or facilities because they are very exposed to natural disasters, such as landslides and floods. In Brazil, the main institution responsible for monitoring areas at risk is the Civil Defense. There is a large database with history of occurrences of risk areas served by the Municipal Civil Defense, in Juiz de Fora city, Minas Gerais state - Brazil, from 1996 to 2017. Some important information contained in this database are the physical aspects of the soil, such as slope, geolocation, amplitude, curvature and accumulated flow, as well as processed data from the sliding risk susceptibility methodologies. The objective of this work is to apply machine learning techniques to identify, from the mentioned database, the susceptibility to the risk of environmental disasters in regions that have not yet participated in events attended by the municipal civil defense. This database is large and unbalanced, thus it is necessary to apply data analysis methodologies so that the machine learning model can correctly identify the standards with the least human intervention. In this study, areas were classified according to risk susceptibility. After the whole process, it was possible to analyze the performance of the algorithms and select some of them, which obtained the best results, with an accuracy of around 80%.
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- Machine Learning
- Environmental Risk
- Data Analysis