Improvement of fire danger modelling with geographically weighted logistic model

Abstract

Global models dominate historical documents on fire danger modelling. However, local variations may exist in the relationships between fire presence and fire-influencing factors. In this study, 50 fire danger models (10 global logistic models and 40 geographically weighted logistic models, i.e. local models), were developed to model daily fire danger in Heilongjiang province in north-east China and cross-validation was performed to evaluate the predictive performance of the various developed models. In modelling, multi-temporal spatial sampling and repeated random sub-sampling were applied to obtain 10 groups of training sub-samples and inner testing sub-samples. For each of the 10 groups of training sub-samples, principal component analysis, in which muticollinearity among variables can be removed, was used to create nine principal components that were then employed as covariates to develop one global logistic model and four geographically weighted logistic models. Compared to global models, all local models showed better model fitting, less spatial autocorrelation of residuals and more desirable modelling of fire presence. In particular, not only was local spatial variation in fire–environment relationships accounted for in the adaptive Gaussian geographically weighted logistic models, but spatial autocorrelation of residuals was significantly reduced to acceptable levels, indicating strong inferential performance.