Forest and Grassland Resources Research >
Study on Driving Factors and Prediction Model of Forest Fire in Guangxi
Received date: 2023-08-14
Revised date: 2023-10-14
Online published: 2023-12-20
Understanding the main driving factors of forest fires and predicting the occurrence of forest fires can provide scientific basis for the prevention and management of local forest fires.Based on the historical fire data sets,meteorology,topography,human activities and combustible load data from 2011 to 2020,we built Logistic regression models and machine learning models to explore the main driving factors of forest fires in Guangxi.Additionally,the optimal model was selected to predict the probability of forest fire occurrences in the research area.The result showed that average monthly rainfall,average monthly relative humidity and the number of forest buildings were the most significant factors affecting the occurrence of forest fires in Guangxi.Both Logistic regression model and machine learning model had excellent prediction accuracy,with AUC values above 0.85.The accuracy of the machine learning model was better than Logistic regression model,and the random forest model had the highest accuracy(SAUC=0.92).The random forest model showed that the risk of forest fires was greatest in northwest,northern and southwestern regions of Guangxi.The overall results of the model were consistent with the actual situation in Guangxi and can provide a reference for forest fire prediction and forecasting in Guangxi.In the future,managers should strengthen the control of wild fire sources and prevention capabilities of extreme weather to reduce the risk of forest fires.
Wenzhen JU , Longbin WEI , Bolin PENG , Changcheng LI , Ting PAN . Study on Driving Factors and Prediction Model of Forest Fire in Guangxi[J]. Forest and Grassland Resources Research, 2023 , 0(5) : 56 -62 . DOI: 10.13466/j.cnki.lczyyj.2023.05.007
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