1. Poisson mixed models for predicting number of fires.
- Author
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Boubeta, Miguel, Lombardía, María José, Marey-Pérez, Manuel, and Morales, Domingo
- Subjects
FOREST fires ,WILDFIRES ,RANDOM effects model ,FIRES - 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. Wildfires are one of the main causes of forest destruction and ecological disasters. By using auxiliary variables and past data, this work introduces temporal regression models for explaining and predicting the number of fires in forest areas during a given time period. The new methodology is an interesting tool to implement preventive policies and to support the design of more effective measures against fires. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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