Geographically Weighted Negative Binomial Regression Model Predicts Wildfire Occurrence in the Great Xing’an Mountains Better Than Negative Binomial Model

Abstract

Wildfire is a major disturbance that affects large area globally every year. Thus, a better prediction of the likelihood of wildfire occurrence is essential to develop appropriate fire prevention measures. We applied a global negative Binomial (NB) and a geographically weighted negative Binomial regression (GWNBR) models to determine the relationship between wildfire occurrence and its drivers factors in the boreal forests of the Great Xing’an Mountains, northeast China. Using geo-weighted techniques to consider the geospatial information of meteorological, topographic, vegetation type and human factors, we aimed to verify whether the performance of the NB model can be improved. Our results confirmed that the model fitting and predictions of GWNBR model were better than the global NB model, produced more precise and stable model parameter estimation, yielded a more realistic spatial distribution of model predictions, and provided the detection of the impact hotpots of these predictor variables. We found slope, vegetation cover, average precipitation, average temperature, and average relative humidity as important predictors of wildfire occurrence in the Great Xing’an Mountains. Thus, spatially differing relations improves the explanatory power of the global NB model, which does not explain sufficiently the relationship between wildfire occurrence and its drivers. Thus, the GWNBR model can complement the global NB model in overcoming the issue of nonstationary variables, thereby enabling a better prediction of the occurrence of wildfires in large geographical areas and improving management practices of wildfire.

Keywords

• boreal forests
• wildfire drivers
• geographically weighted regression
• geospatial analysis