Author(s)

J. Mateu, F. Saura, P. Gregori & P. Juan

Abstract

Fire departments all over the world often use numerical indices to aid in wild-fire management and hazard assessment. These indices are designed to summarize local meteorological and fuel information and provide an estimate of the current risk of fire. In this paper we evaluate the effectiveness of the Burning Index (BI) (a collec-tion of numerical indices designed to be used for fire planning and management) for predicting wildfire occurrences in the Valencian Community (Spain) using space-time point process models. The models are based on a particular decom-position of the conditional intensity, with separate terms to describe spatial and seasonal variability as well as contributions from the BI. Keywords: burning index, conditional intensity function, spatio-temporal point processes, wildfires. 1 Introduction A spatio-temporal point process is a random collection of points, where each point represents the time and location of an event. Examples of events include incidence of disease, sightings or births of a species, or the occurrences of fires, earthquakes, lightning strikes, tsunamis, or volcanic eruptions. Typically the spatial locations are recorded in three spatial coordinates, e.g. longitude, latitude, and height or depth, though sometimes only one or two spatial coordinates are available or of interest. Figure 1 displays some point process data consisting of microearthquake origin times and epicenters in Parkfield, CA, between 1988 and 1995, recorded

Keywords

burning index, conditional intensity function, spatio-temporal point processes, wildfires.