The increasing global concern about wildfires, mostly caused by people, has triggered the development of human-caused fire occurrence models in many countries. The premise is that better knowledge of the underlying factors is critical for many fire management purposes, such as operational decision-making in suppression and strategic prevention planning, or guidance on forest and land-use policies. However, the explanatory and predictive capacity of fire occurrence models is not yet widely applied to the management of forests, fires or emergencies. In this article, we analyse the developments in the field of human-caused fire occurrence modelling with the aim of identifying the most appropriate variables and methods for applications in forest and fire management and civil protection. We stratify our worldwide analysis by temporal dimension (short-term and long-term) and by model output (numeric or binary), and discuss management applications. An attempt to perform a meta-analysis based on published models proved limited because of non-equivalence of the metrics and units of the estimators and outcomes across studies, the diversity of models and the lack of information in published works.

The logit model was used to predict the number of fire-days in the Whitecourt Forest of Alberta. The database used included fire (1) and no-fire (0) observations for fire season days between April and October for the 1986 through 1990 period. There were 8,009 observations during this period of which 157 were fire observations. Using four variables, we were able to predict 79.0% of the fire-days and 81.5% of the no-fire-days. The model, Zi=-8.5171+7.6590xAREAi+0.7367xDISTRICTi+2.0478xBUIi+3.9563xISIi, failed to predict 37 of the fire-days and produced 29 ''false alarms''. When this model was tested on fire occurrence data from the Whitecourt Forest for 1991 and 1992 fire seasons it was correct 74.1% of the time. The management implications and limitations of this study are also discussed in this paper.

Human-caused forest fires are common in Mediterranean countries. Forest fire management agencies customarily estimate daily fire loads by using meteorological fire danger rating indices, based on variables registered daily by weather stations. This paper is focussed on the evaluation of the relative performance of a comprehensive set of commonly used fire weather indices by developing holistic daily fire occurrence models in Spain involving also other topographic, fuel and human-related geographic factors. The data consisted of historical records of daily fire occurrences, daily weather data and geographic characteristics for the peninsular territory of Spain in a 10-km-spatial resolution grid, for the period from 2002 to 2005. The prediction units were 10 × 10-km-grid cells but in order to take into account the spatial variation in relationships between explanatory variables and historical occurrences, Spain was divided into 53 ecoregions and a logistic regression model was developed for each one of these regions. The explanatory variables included in the models illustrated which weather and geographic factors primarily affected daily human-caused fires in the ecoregions. The validation of the estimated ignition probabilities with the fire occurrences registered during 2005, reserved for independently testing the model’s predictive capability, resulted in values of total percentage correctly predicted varying from 47.4 to 82.6%.