Hongrong Wang1,2, Hang Geng1,*, Jing Yuan1, Wen Zhang2, Hanmin Sheng1, Qiuhua Wang3, Xinjian Li4,5
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.3, pp. 3701-3721, 2025, DOI:10.32604/cmes.2025.073513
- 23 December 2025
Abstract Machine learning has emerged as a key approach in wildfire risk prediction research. However, in practical applications, the scarcity of data for specific regions often hinders model performance, with models trained on region-specific data struggling to generalize due to differences in data distributions. While traditional methods based on expert knowledge tend to generalize better across regions, they are limited in leveraging multi-source data effectively, resulting in suboptimal predictive accuracy. This paper addresses this challenge by exploring how accumulated domain expertise in wildfire prediction can reduce model reliance on large volumes of high-quality data. An active More >
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