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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2601.11686 |
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| _version_ | 1866908772491853824 |
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| author | Caron, Nicolas Guyeux, Christophe Noura, Hassan Aynes, Benjamin |
| author_facet | Caron, Nicolas Guyeux, Christophe Noura, Hassan Aynes, Benjamin |
| contents | Current state-of-the-art approaches to wildfire risk assessment often overlook operational needs, limiting their practical value for first responders and firefighting services. Effective wildfire management requires a multi-target analysis that captures the diverse dimensions of wildfire risk, including meteorological danger, ignition activity, intervention complexity, and resource mobilization, rather than relying on a single predictive indicator. In this proof of concept, we propose the development of a hybrid framework that combines predictive models for each risk dimension with large language models (LLMs) to synthesize heterogeneous outputs into structured, actionable reports. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_11686 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Proof of Concept: Multi-Target Wildfire Risk Prediction and Large Language Model Synthesis Caron, Nicolas Guyeux, Christophe Noura, Hassan Aynes, Benjamin Machine Learning Artificial Intelligence Current state-of-the-art approaches to wildfire risk assessment often overlook operational needs, limiting their practical value for first responders and firefighting services. Effective wildfire management requires a multi-target analysis that captures the diverse dimensions of wildfire risk, including meteorological danger, ignition activity, intervention complexity, and resource mobilization, rather than relying on a single predictive indicator. In this proof of concept, we propose the development of a hybrid framework that combines predictive models for each risk dimension with large language models (LLMs) to synthesize heterogeneous outputs into structured, actionable reports. |
| title | Proof of Concept: Multi-Target Wildfire Risk Prediction and Large Language Model Synthesis |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2601.11686 |