title: Predicting the occurrence of wildfires with binary structured additive regression models.
abstract: Wildfires are one of the main environmental problems facing societies today, and in the case of Galicia (north-west Spain), they are the main cause of forest destruction. This paper used binary structured additive regression (STAR) for modelling the occurrence of wildfires in Galicia. Binary STAR models are a recent contribution to the classical logistic regression and binary generalized additive models. Their main advantage lies in their flexibility for modelling non-linear effects, while simultaneously incorporating spatial and temporal variables directly, thereby making it possible to reveal possible relationships among the variables considered. The results showed that the occurrence of wildfires depends on many covariates which display variable behaviour across space and time, and which largely determine the likelihood of ignition of a fire. The joint possibility of working on spatial scales with a resolution of 1 × 1 km cells and mapping predictions in a colour range makes STAR models a useful tool for plotting and predicting wildfire occurrence. Lastly, it will facilitate the development of fire behaviour models, which can be invaluable when it comes to drawing up fire-prevention and firefighting plans.
authors.affiliation: Conservation Biology Department, Doñana Biological Station-CSIC, C/Américo Vespucio S/n, 41092 Seville, Spain. Electronic address: laura.rios@ebd.csic.es.
authors.forename: Laura
authors.initials: L
authors.lastname: Ríos-Pena
authors.affiliation: Chair of Statistics, Georg-August-University Göttingen, Humboldtalle 3, 37073 Göttingen, Germany.
authors.forename: Thomas
authors.initials: T
authors.lastname: Kneib
authors.affiliation: Unit of Biostatistics, Department of Statistics and Operations Research, School of Medicine, University of Santiago de Compostela, C/San Francisco S/n, 15782 Santiago de Compostela, Spain.
authors.forename: Carmen
authors.initials: C
authors.lastname: Cadarso-Suárez
authors.affiliation: PROEPLA Research Group, Polytechnic Institute, University of Santiago de Compostela, Campus Universitario S/n, 27002 Lugo, Spain.
authors.forename: Manuel
authors.initials: M
authors.lastname: Marey-Pérez
mesh_terms: D004190:Disasters; D005390:Fires / Q000517:prevention & control; D006801:Humans; D016015:Logistic Models; D008390:Markov Chains; D008962:Models, Theoretical; D011336:Probability; D013030:Spain; D062211:Spatio-Temporal Analysis
keywords: Covariates; Markov random fields; Penalized splines; Structured additive regression models; Voxel; Wildfires