Citation

Gao C, Lin HL, Hu HQ, Song H. Ying Yong Sheng Tai Xue Bao 2020; 31(9): 3227-3240.

Copyright

(Copyright © 2020, Ying yong sheng tai xue bao bian ji wei yuan hui)

DOI

10.13287/j.1001-9332.202009.014

PMID

33345524

Abstract

We summarized research progress of forest fire occurrence prediction model in China based on the literature review, from the prospects of forest fire drivers, models of forest fire occurrence probability, models of forest fire occurrence frequency and model validation methods. The main conclusions are: 1) Meteorology, terrain, vegetation, fuel and human activities were the main driving factors of forest fire occurrence and model prediction accuracy. 2) In the models of forest fire occurrence probability, the geographically weighted logistic regression model considered the spatial correlation between model variables, the Gompit regression model could fit the asymmetric structure fire data. The random forest algorithm had a high prediction accuracy without the requirement of multicollinearity test and excessive fitting, which made it as one of the optimal methods of forest fire occurrence probability prediction. 3) Among all the forest fire occurrence frequency models, the negative binomial regression model was suitable for fitting the over discrete data, the zero-inflated model and hurdle model could deal with fire data that contained a large number of zeros. 4) ROC test, AIC test, likelihood ratio test, and Wald test were the most common methods for evaluating the accuracy of fire occurrence probability and frequency models. The study of forest fire occurrence prediction model should be the main focus of the forest fire management. Model selection should base on fire data structure of different forests. More influencing factors should be taken into account to improve the prediction accuracy of model. In addition, it was necessary to further explore the application of other mathematical methods in forest fire prediction, to improve the accuracy of the models.

Language: zh

Keywords

Humans; Logistic Models; China; Models, Statistical; forest fire driving factor; forest fire occurrence frequency; forest fire occurrence probability; Meteorology; model test; regression model; Wildfires