Abstract: The increasing frequency of extreme weather events raises the likelihood of forest wildfires. Therefore, establishing an effective fire prediction model is vital for protecting human life and property, and the environment. This study aims to build a prediction model to understand the spatial characteristics and piecewise effects of forest fire drivers. Using monthly grid data from 2006 to 2020, a modeling study analyzed fire occurrences during the September to April fire season in Fujian Province, China. We compared the fitting performance of the logistic regression model (LRM), the generalized additive logistic model (GALM), and the spatial generalized additive logistic model (SGALM). The results indicate that SGALMs had the best fitting results and the highest prediction accuracy. Meteorological factors significantly impacted forest fires in Fujian Province. Areas with high fire incidence were mainly concentrated in the northwest and southeast. SGALMs improved the fitting effect of fire prediction models by considering spatial effects and the flexible fitting ability of nonlinear interpretation. This model provides piecewise interpretations of forest wildfire occurrences, which can be valuable for relevant departments and will assist forest managers in refining prevention measures based on temporal and spatial differences.

Key words: Forest fire prediction, Logistic regression, Spatial generalized additive model, Spline functions, Piecewise effects