Citation

Zhu X, Li J, Guo W, Bi S, Wu Y. China Saf. Sci. J. 2022; 32(9): 152-157.

Copyright

(Copyright © 2022, China Occupational Safety and Health Association, Publisher Gai Xue bao)

DOI

10.16265/j.cnki.issn1003-3033.2022.09.2729

PMID

unavailable

Abstract

In order to reduce the damage caused by forest fires, the forest fire risk was modeled and predicted through a literature review. This paper summarizes the research status of forest fire risk prediction based on the machine learning method, and analyzes and expounds on the selection of forest fire impact factors, the selection of appropriate fire risk prediction models and the model test methods. The results show that the main influencing factors of forest fire risk include the characteristics of combustibles, meteorological factors, terrain, human activities, etc. In the forest fire risk prediction model, the back propagation(BP) neural network method needs to be improved and applied. The SVM method requires high data, the random forest (RF) method has strong universality and high accuracy, and the deep learning method has less research, but the accuracy is high. The commonly used test methods of the model are accuracy, receiver operating characteristic(ROC) curve and area under curve(AUC) value. © 2022 China Safety Science Journal. All rights reserved.

Language: zh