Modelo de inteligência artificial para estimativa do desmatamento considerando a rede de transporte rodoviário do estado do Pará

Abstract

Since the decade of 1950s the Amazonian and Brazilian transportation complex prioritized the model of road transport. Past studies point that the regular roadway system that is integrated to a clandestine roadway complex is strongly related to the Amazon forest deforestation. Thus, in this work we performed a quantitative analysis of the variables related to the process of deforestation of the Amazon forest, a natural resource of great environment and economic significance, and the socioeconomic development of the region in the period between 1988 and 2018. The geographical study area is the state of Pará, located in the Oriental Amazon, the second largest state of Brazil in territorial extension and the most devastated. We used machine learning in the modeling of the quantitative variables related to the transportation infrastructure, social variables and economic variables, e.g., the devastated area. The random forest model presented the best performance with the generated function (using least squares method). It was estimated the devastated area for the years of 2020, 2030, 2040 and 2050. Sensitivity analysis was used to evaluate the devastated area after the implementation of the roads BR-163 and BR-210 in the north of Pará. The results show that given the current scenario the devastation tends to continue intensively in the next three decades, with a 25.77% increase over the current region albeit with decreasing ten-year rates of forestation loss, and the estimation of the deforested area caused by the implementation of federal roadway networks goes from 4,703.43 km2 to 6,567.48 km2 .