Studying the occurrence and burnt area of wildfires using zero-one-inflated structured additive beta regression

摘要

When studying the empirical phenomenon of wildfires, we can distinguish between the occurrence at a specific location and time and the burnt area measured. This study proposes using structured additive regression models based on zero-one-inflated beta distribution for studying wildfire occurrence and burnt area simultaneously. Beta distribution affords a convenient way of studying the percentage of burnt area in cases where such percentages are bounded away from zero and one. Inflation with zeros and ones enables observations without wildfires or with 100% burnt areas to be treated as special cases. Structured additive regression allows one to include a variety of covariates, while simultaneously exploring spatial and temporal correlations. Our inferences are based on an efficient Markov chain Monte Carlo simulation algorithm utilizing iteratively weighted least squares approximations as proposal densities. Application of the proposed methodology to a large wildfire database covering Galicia (Spain) provides essential information for improved wildfire management. (c) 2018 Elsevier Ltd. All rights reserved.