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 wild...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-12-01
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| Series: | MethodsX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016125003437 |
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| Summary: | 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. |
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| ISSN: | 2215-0161 |