Seasonal forest fire risk and key drivers in Yunnan Province: a machine learning approach
Abstract Forest fires occur frequently in the southwest of China. It is crucial to construct forest fire prediction models and explore the driving factors of fire occurrence for effective fire management. We employed six machine learning models to explore the optimal model and important driving fact...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | npj Natural Hazards |
| Online Access: | https://doi.org/10.1038/s44304-025-00112-4 |
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| author | Heng Zhang Wenxuan Wang Qingyu Ban |
| author_facet | Heng Zhang Wenxuan Wang Qingyu Ban |
| author_sort | Heng Zhang |
| collection | DOAJ |
| description | Abstract Forest fires occur frequently in the southwest of China. It is crucial to construct forest fire prediction models and explore the driving factors of fire occurrence for effective fire management. We employed six machine learning models to explore the optimal model and important driving factors for predicting forest fires in different seasons in Yunnan Province, China. The results indicated that the BRT was the best model for predicting forest fires, and meteorological and human factors were the important driving factors for fire occurrence. The XGBoost was the optimal model for predicting fires in summer and autumn, mainly influenced by meteorological and soil vegetation factors. We also found that the areas with a higher probability of forest fire occurrence were mainly concentrated in the southwest, southeast, and northwest. This study can provide useful reference for the formulation of forest fire prevention strategies in specific seasons in the research area. |
| format | Article |
| id | doaj-art-c77eecff4ab2411e8fca77efa2d12e11 |
| institution | Kabale University |
| issn | 2948-2100 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Natural Hazards |
| spelling | doaj-art-c77eecff4ab2411e8fca77efa2d12e112025-08-20T04:01:43ZengNature Portfolionpj Natural Hazards2948-21002025-07-012111510.1038/s44304-025-00112-4Seasonal forest fire risk and key drivers in Yunnan Province: a machine learning approachHeng Zhang0Wenxuan Wang1Qingyu Ban2College of Forestry, Inner Mongolia Agricultural UniversityCollege of Forestry, Inner Mongolia Agricultural UniversityCollege of Forestry, Inner Mongolia Agricultural UniversityAbstract Forest fires occur frequently in the southwest of China. It is crucial to construct forest fire prediction models and explore the driving factors of fire occurrence for effective fire management. We employed six machine learning models to explore the optimal model and important driving factors for predicting forest fires in different seasons in Yunnan Province, China. The results indicated that the BRT was the best model for predicting forest fires, and meteorological and human factors were the important driving factors for fire occurrence. The XGBoost was the optimal model for predicting fires in summer and autumn, mainly influenced by meteorological and soil vegetation factors. We also found that the areas with a higher probability of forest fire occurrence were mainly concentrated in the southwest, southeast, and northwest. This study can provide useful reference for the formulation of forest fire prevention strategies in specific seasons in the research area.https://doi.org/10.1038/s44304-025-00112-4 |
| spellingShingle | Heng Zhang Wenxuan Wang Qingyu Ban Seasonal forest fire risk and key drivers in Yunnan Province: a machine learning approach npj Natural Hazards |
| title | Seasonal forest fire risk and key drivers in Yunnan Province: a machine learning approach |
| title_full | Seasonal forest fire risk and key drivers in Yunnan Province: a machine learning approach |
| title_fullStr | Seasonal forest fire risk and key drivers in Yunnan Province: a machine learning approach |
| title_full_unstemmed | Seasonal forest fire risk and key drivers in Yunnan Province: a machine learning approach |
| title_short | Seasonal forest fire risk and key drivers in Yunnan Province: a machine learning approach |
| title_sort | seasonal forest fire risk and key drivers in yunnan province a machine learning approach |
| url | https://doi.org/10.1038/s44304-025-00112-4 |
| work_keys_str_mv | AT hengzhang seasonalforestfireriskandkeydriversinyunnanprovinceamachinelearningapproach AT wenxuanwang seasonalforestfireriskandkeydriversinyunnanprovinceamachinelearningapproach AT qingyuban seasonalforestfireriskandkeydriversinyunnanprovinceamachinelearningapproach |