Abstract: Aims Much research has been carried out on the relationship between forest fire occurrence and weather factors by use of modeling in recent years. However, the data organization used in past research can not satisfy the requirements of models well. Our aims are to determine the regression model that best fits the forest fire data set and provides a new model theory for research on forest fire and its influencing factors in order to forecast lighting fire occurrence.
Methods We used negative binomial (NB) and zero-inflated negative binomial (ZINB) models to describe the relationship between lighting fire occurrence and weather factors in the Daxing’an Mountains for 1980–2005 using SAS 9.1 version and R-Project statistic software and comparing results from these models by use of AIC and Vuong methods.
Important findings Both NB and ZINB models produced results with high significance for each weather factor. Comparison of the two models according to AIC, Vuong and other methods showed that the fitting ability and predictive power of ZINB model are better than those of the NB model. The advantage was also found when we compared the modeling results with Ordinary Least Squares. Then we obtained the best model for the relationship between lighting fire and weather factors.