Studying the occurrence and burnt area of wildfires using zero-one-inflated structured additive beta regression. (December 2018) - Ríos-Pena, Laura; Kneib, Thomas; Cadarso-Suárez, Carmen; Klein, Nadja; Marey-Pérez, Manuel
Abstract: 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. Highlights: Zero-One-Inflated structured additive beta regression is proposed to study wildfire occurrence and burnt area simultaneously. Structured additive regression allows one to include a variety of covariates, while simultaneously exploring spatial and temporal correlations. The principal advantage of the distributional regression is that allows one to evaluate effects of covariates linked to the response distribution and not just effects on the mean. Proposed methodology showed provided essential information for improved wildfire management.