首页|Prediction and driving factors of forest fire occurrence in Jilin Province,China
Prediction and driving factors of forest fire occurrence in Jilin Province,China
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NETL
NSTL
万方数据
维普
Forest fires are natural disasters that can occur suddenly and can be very damaging,burning thousands of square kilometers.Prevention is better than suppression and prediction models of forest fire occurrence have devel-oped from the logistic regression model,the geographical weighted logistic regression model,the Lasso regression model,the random forest model,and the support vector machine model based on historical forest fire data from 2000 to 2019 in Jilin Province.The models,along with a distribu-tion map are presented in this paper to provide a theoretical basis for forest fire management in this area.Existing stud-ies show that the prediction accuracies of the two machine learning models are higher than those of the three general-ized linear regression models.The accuracies of the random forest model,the support vector machine model,geographi-cal weighted logistic regression model,the Lasso regression model,and logistic model were 88.7％,87.7％,86.0％,85.0％and 84.6％,respectively.Weather is the main factor affecting forest fires,while the impacts of topography factors,human and social-economic factors on fire occurrence were similar.
Forest fireOccurrence predictionForest fire driving factorsGeneralized linear regression modelsMachine learning models
Bo Gao、Yanlong Shan、Xiangyu Liu、Sainan Yin、Bo Yu、Chenxi Cui、Lili Cao
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Wildland Fire Prevention and Fighting Innovation Center of Beihua University,Forestry College of Beihua University,3999 Binjiang East Road,Jilin City 132013,People's Republic of China
NETL
NSTL
万方数据
维普
