{"title":"利用变压器网络和模糊推理系统架起野火预测与决策建模的桥梁","authors":"Parul Dubey , Pushkar Dubey","doi":"10.1016/j.mex.2025.103498","DOIUrl":null,"url":null,"abstract":"<div><div>Wildfires present a growing threat to ecosystems, human settlements, and climate stability, necessitating accurate and interpreted prediction systems. Existing AI-based models often prioritize performance over explainability, limiting their utility in real-time decision-making contexts. Current wildfire forecasting models struggle to incorporate uncertainty and offer transparent response strategies. Moreover, many models fail to integrate domain knowledge in a way that supports actionable interventions. This study utilizes the Canadian Fire Spread Dataset, augmented with Sentinel, ERA5, and SRTM data, encompassing vegetation, meteorological, and topographic variables. The suggested system uses a Transformer-based model to predict fires over time and space, along with a Fuzzy Rule-Based System (FRBS) to create rules for responding to those predictions. This integration allows for both high accuracy and interpretability in decision-making under uncertain environmental conditions. The novelty lies in the use of symbolic fuzzy reasoning layered onto a deep attention-based architecture. Performance was evaluated using metrics such as accuracy, precision, recall, F1-score, and AUC. The model achieved an F1-score of 92.9 % and accuracy of 94.8 %, significantly outperforming baseline and deep learning alternatives.</div><div>• Integrates deep learning with fuzzy logic for both accurate forecasting and interpretable response planning.</div><div>• Enables uncertainty-aware reasoning by translating predictions into actionable fire management rules.</div><div>• Demonstrates superior performance across diverse environmental datasets using multi-source satellite and climate inputs.</div></div>","PeriodicalId":18446,"journal":{"name":"MethodsX","volume":"15 ","pages":"Article 103498"},"PeriodicalIF":1.6000,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Bridging spatiotemporal wildfire prediction and decision modeling using transformer networks and fuzzy inference systems\",\"authors\":\"Parul Dubey , Pushkar Dubey\",\"doi\":\"10.1016/j.mex.2025.103498\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Wildfires present a growing threat to ecosystems, human settlements, and climate stability, necessitating accurate and interpreted prediction systems. Existing AI-based models often prioritize performance over explainability, limiting their utility in real-time decision-making contexts. Current wildfire forecasting models struggle to incorporate uncertainty and offer transparent response strategies. Moreover, many models fail to integrate domain knowledge in a way that supports actionable interventions. This study utilizes the Canadian Fire Spread Dataset, augmented with Sentinel, ERA5, and SRTM data, encompassing vegetation, meteorological, and topographic variables. The suggested system uses a Transformer-based model to predict fires over time and space, along with a Fuzzy Rule-Based System (FRBS) to create rules for responding to those predictions. This integration allows for both high accuracy and interpretability in decision-making under uncertain environmental conditions. The novelty lies in the use of symbolic fuzzy reasoning layered onto a deep attention-based architecture. Performance was evaluated using metrics such as accuracy, precision, recall, F1-score, and AUC. The model achieved an F1-score of 92.9 % and accuracy of 94.8 %, significantly outperforming baseline and deep learning alternatives.</div><div>• Integrates deep learning with fuzzy logic for both accurate forecasting and interpretable response planning.</div><div>• Enables uncertainty-aware reasoning by translating predictions into actionable fire management rules.</div><div>• Demonstrates superior performance across diverse environmental datasets using multi-source satellite and climate inputs.</div></div>\",\"PeriodicalId\":18446,\"journal\":{\"name\":\"MethodsX\",\"volume\":\"15 \",\"pages\":\"Article 103498\"},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2025-07-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"MethodsX\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2215016125003437\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"MethodsX","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2215016125003437","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
Bridging spatiotemporal wildfire prediction and decision modeling using transformer networks and fuzzy inference systems
Wildfires present a growing threat to ecosystems, human settlements, and climate stability, necessitating accurate and interpreted prediction systems. Existing AI-based models often prioritize performance over explainability, limiting their utility in real-time decision-making contexts. Current wildfire forecasting models struggle to incorporate uncertainty and offer transparent response strategies. Moreover, many models fail to integrate domain knowledge in a way that supports actionable interventions. This study utilizes the Canadian Fire Spread Dataset, augmented with Sentinel, ERA5, and SRTM data, encompassing vegetation, meteorological, and topographic variables. The suggested system uses a Transformer-based model to predict fires over time and space, along with a Fuzzy Rule-Based System (FRBS) to create rules for responding to those predictions. This integration allows for both high accuracy and interpretability in decision-making under uncertain environmental conditions. The novelty lies in the use of symbolic fuzzy reasoning layered onto a deep attention-based architecture. Performance was evaluated using metrics such as accuracy, precision, recall, F1-score, and AUC. The model achieved an F1-score of 92.9 % and accuracy of 94.8 %, significantly outperforming baseline and deep learning alternatives.
• Integrates deep learning with fuzzy logic for both accurate forecasting and interpretable response planning.
• Enables uncertainty-aware reasoning by translating predictions into actionable fire management rules.
• Demonstrates superior performance across diverse environmental datasets using multi-source satellite and climate inputs.