ANALISE COMPUTACIONAL DE CONDICIONANTES DE RISCOS AMBIENTAIS

Resumo

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 in the 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 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 informations contained in this database are the physical aspects of the soil, such as
slope, altitude, 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, so it is necessary to apply data analysis methodologies so as a machine learning model
can correctly identify the standards with the least human intervention.