Forest fire risk assessment model optimized by stochastic average gradient descent
Forest fire is a serious global natural disaster that occurs frequently and is characterized by its suddenness, destructiveness, and difficulty in emergency response. Therefore, it’s of great importance to research forest fire risk assessment and prediction to protect the ecological environment of f...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Ecological Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X24014638 |
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| author | Zexin Fu Adu Gong Jinhong Wan Wanru Ba Haihan Wang Jiaming Zhang |
| author_facet | Zexin Fu Adu Gong Jinhong Wan Wanru Ba Haihan Wang Jiaming Zhang |
| author_sort | Zexin Fu |
| collection | DOAJ |
| description | Forest fire is a serious global natural disaster that occurs frequently and is characterized by its suddenness, destructiveness, and difficulty in emergency response. Therefore, it’s of great importance to research forest fire risk assessment and prediction to protect the ecological environment of forests, respond to disaster damage in time and mitigate the effect of disasters. Most of the current related research methods rely on a stable operating environment and high raw data accuracy, the number of influencing factors considered is always different from the actual in many ways, and there is no systematic validation, etc., which makes the feasibility insufficient. In this study, a comprehensive method for forest fire risk assessment and prediction is proposed using a back-propagation neural network (BPNN) optimized by the stochastic average gradient descent (SAGD) algorithm. The model is based on the Regional Disaster System Theory (RDST), and incorporates 11 indicators of meteorological, vegetation, and human activity factors from the aspects of hazard-formative factor, hazard-formative environment, and hazard-affected body, and achieves a prediction accuracy of 94.38% and a coefficient of determination of 0.9581 when compared with historical data and the Global Fire Weather Index (FWI). The results demonstrate that the SAGD optimization improves the performance of the BPNN, offers a pragmatic solution for forest fire risk assessment in the Guangxi Zhuang Autonomous Region, and facilitates the enhancement of its disaster preparedness and response capabilities, thereby mitigating the adverse effects of forest fires. |
| format | Article |
| id | doaj-art-441ec1177b71406dbfbc86befd807797 |
| institution | Kabale University |
| issn | 1470-160X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj-art-441ec1177b71406dbfbc86befd8077972025-01-31T05:10:27ZengElsevierEcological Indicators1470-160X2025-01-01170113006Forest fire risk assessment model optimized by stochastic average gradient descentZexin Fu0Adu Gong1Jinhong Wan2Wanru Ba3Haihan Wang4Jiaming Zhang5Key Laboratory of Environmental Change and Natural Disasters of Ministry of Education, Beijing Normal University, Beijing 100875, PR China; State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, PR ChinaKey Laboratory of Environmental Change and Natural Disasters of Ministry of Education, Beijing Normal University, Beijing 100875, PR China; State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, PR China; Corresponding author.China Institute of Water Resources and Hydropower Research, Beijing 100038, PR ChinaState Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, PR ChinaState Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, PR ChinaState Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, PR ChinaForest fire is a serious global natural disaster that occurs frequently and is characterized by its suddenness, destructiveness, and difficulty in emergency response. Therefore, it’s of great importance to research forest fire risk assessment and prediction to protect the ecological environment of forests, respond to disaster damage in time and mitigate the effect of disasters. Most of the current related research methods rely on a stable operating environment and high raw data accuracy, the number of influencing factors considered is always different from the actual in many ways, and there is no systematic validation, etc., which makes the feasibility insufficient. In this study, a comprehensive method for forest fire risk assessment and prediction is proposed using a back-propagation neural network (BPNN) optimized by the stochastic average gradient descent (SAGD) algorithm. The model is based on the Regional Disaster System Theory (RDST), and incorporates 11 indicators of meteorological, vegetation, and human activity factors from the aspects of hazard-formative factor, hazard-formative environment, and hazard-affected body, and achieves a prediction accuracy of 94.38% and a coefficient of determination of 0.9581 when compared with historical data and the Global Fire Weather Index (FWI). The results demonstrate that the SAGD optimization improves the performance of the BPNN, offers a pragmatic solution for forest fire risk assessment in the Guangxi Zhuang Autonomous Region, and facilitates the enhancement of its disaster preparedness and response capabilities, thereby mitigating the adverse effects of forest fires.http://www.sciencedirect.com/science/article/pii/S1470160X24014638Forest FireDisaster RiskGradient DescentNeural Network |
| spellingShingle | Zexin Fu Adu Gong Jinhong Wan Wanru Ba Haihan Wang Jiaming Zhang Forest fire risk assessment model optimized by stochastic average gradient descent Ecological Indicators Forest Fire Disaster Risk Gradient Descent Neural Network |
| title | Forest fire risk assessment model optimized by stochastic average gradient descent |
| title_full | Forest fire risk assessment model optimized by stochastic average gradient descent |
| title_fullStr | Forest fire risk assessment model optimized by stochastic average gradient descent |
| title_full_unstemmed | Forest fire risk assessment model optimized by stochastic average gradient descent |
| title_short | Forest fire risk assessment model optimized by stochastic average gradient descent |
| title_sort | forest fire risk assessment model optimized by stochastic average gradient descent |
| topic | Forest Fire Disaster Risk Gradient Descent Neural Network |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X24014638 |
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