RT Journal Article
T1 Burned area prediction with semiparametric models
A1 Boubeta Martínez, Miguel
A1 Lombardía Cortiña, María José
A1 González Manteiga, Wenceslao
A1 Marey Pérez, Manuel
K1 Bootstrap
K1 Forest fires
K1 Time series
AB Wildfires are one of the main causes of forest destruction, especially in Galicia (north-west Spain), where the area burned by forest fires in spring and summer is quite high. This work uses two semiparametric time-series models to describe and predict the weekly burned area in a year: autoregressive moving average (ARMA) modelling after smoothing, and smoothing after ARMA modelling. These models can be described as a sum of a parametric component modelled by an autoregressive moving average process and a non-parametric one. To estimate the non-parametric component, local linear and kernel regression, B-splines and P-splines were considered. The methodology and software were applied to a real dataset of burned area in Galicia for the period 1999–2008. The burned area in Galicia increases strongly during summer periods. Forest managers are interested in predicting the burned area to manage resources more efficiently. The two semiparametric models are analysed and compared with a purely parametric model. In terms of error, the most successful results are provided by the first semiparametric time-series model
PB Csiro Publishing
SN 1049-8001
YR 2015
FD 2015
LK http://hdl.handle.net/10347/18547
UL http://hdl.handle.net/10347/18547
LA eng
NO Boubeta, M., Lombardía, M., González-Manteiga, W., & Marey-Pérez, M. (2016). Burned area prediction with semiparametric models. International Journal Of Wildland Fire, 25(6), 669. doi: 10.1071/wf15125
NO This work was supported by grants MTM2014–52876-R, MTM2011–22392 and MTM2013–41383-P of the Spanish Ministerio de Economía y Competitividad, by Xunta de Galicia CN2012/130 and 07MRU035291PR, by Ministerio del Medio Ambiente, Rural y Marino PSE-310000–2009–4 and by COST Action/UE COST-OC-2008–1-2124
DS Minerva
RD 27 abr 2026