Multitemporal Modelling of Socio-Economic Wildfire Drivers in Central Spain between the 1980s and the 2000s: Comparing Generalized Linear Models to Machine Learning Algorithms

PLoS One. 2016 Aug 24;11(8):e0161344. doi: 10.1371/journal.pone.0161344. eCollection 2016.

Abstract

The socio-economic factors are of key importance during all phases of wildfire management that include prevention, suppression and restoration. However, modeling these factors, at the proper spatial and temporal scale to understand fire regimes is still challenging. This study analyses socio-economic drivers of wildfire occurrence in central Spain. This site represents a good example of how human activities play a key role over wildfires in the European Mediterranean basin. Generalized Linear Models (GLM) and machine learning Maximum Entropy models (Maxent) predicted wildfire occurrence in the 1980s and also in the 2000s to identify changes between each period in the socio-economic drivers affecting wildfire occurrence. GLM base their estimation on wildfire presence-absence observations whereas Maxent on wildfire presence-only. According to indicators like sensitivity or commission error Maxent outperformed GLM in both periods. It achieved a sensitivity of 38.9% and a commission error of 43.9% for the 1980s, and 67.3% and 17.9% for the 2000s. Instead, GLM obtained 23.33, 64.97, 9.41 and 18.34%, respectively. However GLM performed steadier than Maxent in terms of the overall fit. Both models explained wildfires from predictors such as population density and Wildland Urban Interface (WUI), but differed in their relative contribution. As a result of the urban sprawl and an abandonment of rural areas, predictors like WUI and distance to roads increased their contribution to both models in the 2000s, whereas Forest-Grassland Interface (FGI) influence decreased. This study demonstrates that human component can be modelled with a spatio-temporal dimension to integrate it into wildfire risk assessment.

Publication types

  • Historical Article

MeSH terms

  • Algorithms
  • Fires* / history
  • Geography
  • History, 20th Century
  • History, 21st Century
  • Humans
  • Linear Models
  • Machine Learning
  • Models, Theoretical*
  • Probability
  • Risk Assessment
  • Socioeconomic Factors
  • Spain

Grant support

This research received funding from the European Union’s Seventh Framework Programme (FP7/2007-2013) under grant agreement 243888 (FUME Project).