A0178
Title: Bayesian spatio-temporal modeling of the Brazilian wildfires: The influence of human and meteorological variables
Authors: Paulo Canas Rodrigues - Federal University of Bahia (Brazil) [presenting]
Abstract: Wildfires are among the most common natural disasters in many world regions and actively impact life quality. These events have become frequent due to climate change, other local policies, and human behavior. The historical data with the geographical locations of all the fire spots detected by the reference satellites covering the Brazilian territory between January 2011 and December 2022 are considered, comprising more than 2.2 million fire spots. This data were modeled with a spatio-temporal generalized linear model for areal unit data, whose inferences about its parameters are made in a Bayesian approach and use meteorological variables (precipitation, air temperature, humidity, and wind speed) and a human variable (land-use transition and occupation) as covariates. The change in land use from the forest and green areas to farming significantly impacts the number of fire spots for all six Brazilian biomes. (Joint work with Jonatha Pimentel and Rodrigo Bulhoes)
