Studying the occurrence and burnt area of wildfires using zero-one-inflated structured additive beta regression. (December 2018)

• Record Type: Journal Article
• Authors: Ríos-Pena, Laura; Kneib, Thomas; Cadarso-Suárez, Carmen; Klein, Nadja; Marey-Pérez, Manuel
• Is Part Of: Environmental modelling & software. Volume 110(2018)
• Page Start: 107
• Page End: 118
• Publication Date: 2018-12
• Subjects: Beta regression -- Burnt area -- Deviance information criterion -- Markov chain Monte Carlo simulations -- Temporal and spatial effect -- Zero-one-inflated beta distribution; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2018.03.008
• Languages: English
• ISSNs: 1364-8152

Abstract

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.