Wildfire prediction using zero-inflated negative binomial mixed models: Application to Spain.

Wildfires have changed in recent decades. The catastrophic wildfires make it necessary to have accurate predictive models on a country scale to organize firefighting resources. In Mediterranean countries, the number of wildfires is quite high but they are mainly concentrated around summer months. Because of seasonality, there are territories where the number of fires is zero in some months and is overdispersed in others. Zero-inflated negative binomial mixed models are adapted to this type of data because they can describe patterns that explain both number of fires and their non-occurrence and also provide useful prediction tools. In addition to model-based predictions, a parametric bootstrap method is applied for estimating mean squared errors and constructing prediction intervals. The statistical methodology and developed software are applied to model and to predict number of wildfires in Spain between 2002 and 2015 by provinces and months.

Prediction of forest fires occurrences with area-level Poisson mixed models.

The number of fires in forest areas of Galicia (north-west of Spain) during the summer period is quite high. Local authorities are interested in analyzing the factors that explain this phenomenon. Poisson regression models are good tools for describing and predicting the number of fires per forest areas. This work employs area-level Poisson mixed models for treating real data about fires in forest areas. A parametric bootstrap method is applied for estimating the mean squared errors of fires predictors. The developed methodology and software are applied to a real data set of fires in forest areas of Galicia.

Spatiotemporal analysis of vehicle collisions involving wild boar and roe deer in NW Spain.

Ungulate-vehicle collisions pose a serious traffic safety hazard in the North of Spain. The understanding of underlying temporal and spatial structure of these non-random events is imperative to develop appropriate mitigation measures. This study analyses the temporal, spatial and spatiotemporal patterns of car crashes involving wild boar and roe deer in the province of Lugo (NW Spain) in the period 2006-2010 using geographic information systems (GIS) and spatial statistics. The temporal analysis--conducted at three scales: daily, weekly and seasonal--revealed that accidents are related to specific animal's life cycles and to interactions with human activities. The localization of collision points with GIS discovered the sections of the autonomic road network where accidents with the two studied species concentrate. Besides, the spatial arrangement of significant hotspots was mapped through kernel density estimation over two time scales (daily and seasonal), distinguishing among 41 sets, sequentially arranged to facilitate clustering comparison and determination of spatiotemporal risky areas. This work is of valuable help for road managers to design the appropriate mitigation measures that will improve traffic safety and animal welfare.