Prediction and driving factors of forest fire occurrence in Jilin Province, China

Abstract

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 developed 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 distribution map are presented in this paper to provide a theoretical basis for forest fire management in this area. Existing studies show that the prediction accuracies of the two machine learning models are higher than those of the three generalized linear regression models. The accuracies of the random forest model, the support vector machine model, geographical 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.

Keywords

Forest fire / Occurrence prediction / Forest fire driving factors / Generalized linear regression models / Machine learning models

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Funding

This research was funded by the National Natural Science Foundation of China(32271881)