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Predicting the occurrence of wildfires with binary structured additive regression models

AutorRíos-Pena, Laura; Kneib, T.; Cadarso-Suárez, C.; Marey-Pérez, Manuel
Palabras claveWildfires
Structured additive regression models
Covariates
Voxel
Penalized splines
Markov random fields
Fecha de publicación2017
EditorElsevier
CitaciónJournal of Environmental Management, 187: 154-165 (2017)
ResumenWildfires 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
Versión del editorhttp://dx.doi.org/10.1016/j.jenvman.2016.11.044
URIhttp://hdl.handle.net/10261/143707
DOI10.1016/j.jenvman.2016.11.044
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