Applying neural network technology to human-caused wildfire occurrence prediction

Human-caused forest fires are a serious problem throughout the world. Believing that there are predictable characteristics common to all fires, we analyzed the historical human caused fire occurrence data for the Whitecourt Provincial Forest of Alberta using artificial neural network and geographic information system (ARC/INFO) technology. These data were also analyzed using logistic regression analysis (the binary legit model), which served as the ''domain expert'' to identify the important input variables. A 314 fire and no-fire data set for the period 1986-1990 was used for training. The observations were whether at least one fire occurred, on a certain day, in one of the eight geographic zones defined within the study area. The models developed were tested using data from the 1991-1992 fire seasons, which had 58 fire observations. Using as input variables the Canadian Fire Weather Index for the day, area in km(2) of the geographic zone, and district (a 0/1 dummy variable from the logistic regression model, which accounts for observations within a forest district where human use is higher), the resultant model had four input nodes and two output nodes, and correctly predicted 85 percent of the no-fire observations and 78 percent of the fire observations.