Poisson mixed models for predicting number of fires
Miguel Boubeta A , María José Lombardía A D , Manuel Marey-Pérez B and Domingo Morales C
+ Author Affiliations
- Author Affiliations
A Universidade da Coruña, Facultad de Informática, Campus de Elviña s/n, 15071 A Coruña, Spain.
B Universidad de Santiago de Compostela, Escuela Politécnica Superior de Ingeniería, Campus Terra s/n, 27002 Lugo, Spain.
C Universidad Miguel Hernández de Elche, Centro de Investigación Operativa, Avenida de la Universidad s/n, 03202 Elche, Spain.
D Corresponding author. Email: maria.jose.lombardia@udc.es
International Journal of Wildland Fire 28(3) 237-253 https://doi.org/10.1071/WF17037
Submitted: 23 February 2017 Accepted: 4 January 2019 Published: 7 March 2019
Abstract
Wildfires are considered one of the main causes of forest destruction. In recent years, the number of forest fires and burned area in Mediterranean regions have increased. This problem particularly affects Galicia (north-west of Spain). Conventional modelling of the number of forest fires in small areas may have a high error. For this reason, four area-level Poisson mixed models with time effects are proposed. The first two models contain independent time effects, whereas the random effects of the other models are distributed according to an autoregressive process AR(1). A parametric bootstrap algorithm is given to measure the accuracy of the plug-in predictor of fire number under the temporal models. A significant prediction improvement is observed when using Poisson regression models with random time effects. Analysis of historical data finds significant meteorological and socioeconomic variables explaining the number of forest fires by area and reveals the presence of a temporal correlation structure captured by the area-level Poisson mixed model with AR(1) time effects.
Additional keywords: bootstrap, empirical best predictor, forest fires, mean squared error, method of moments, plug-in predictor, time dependency.
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