Fire Prediction Based on CatBoost Algorithm

Fangrong Zhou, Hao Pan, Zhenyu Gao, Xuyong Huang, Guochao Qian, Yu Zhu and Feng Xiao

Mathematical Problems in Engineering, 2021, vol. 2021, 1-9

Abstract:

In recent years, increasingly severe wildfires have posed a significant threat to the safe and stable operation of transmission lines. Wildfire risk assessment and early warning have become an important research topic in power grid risk assessment. This study proposes a fire prediction model on the basis of the CatBoost algorithm to effectively predict the fire point. Five wildfire risk factors, including vegetation factors, meteorological factors, human factors, terrain factors, and land surface temperature, were combined using the feature selection method on the basis of the gradient boosting decision tree model and principal component analysis to achieve dimensionality reduction of redundant data and create a fire prediction model. The MODIS fire point product is used as the model evaluation data. The verification result uses the AUC value as the evaluation factor. The accuracy of the model is 0.82, and the AUC value is 0.83. The obtained fire point evaluation results are in good agreement with the actual fire points. Results show that this model can effectively predict the risk of wildfires.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:1929137

DOI: 10.1155/2021/1929137

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