Localized Forest Fire Risk Prediction: A Department-Aware Approach for Operational Decision Support

• URL: http://arxiv.org/abs/2506.04254v1
• Date: Sun, 01 Jun 2025 16:54:48 GMT
• Title: Localized Forest Fire Risk Prediction: A Department-Aware Approach for Operational Decision Support
• Authors: Nicolas Caron, Christophe Guyeux, Hassan Noura, Benjamin Aynes,
• Abstract summary: With climate change intensifying fire behavior and frequency, accurate prediction has become one of the most pressing challenges in Artificial Intelligence (AI)<n>This paper proposes a new approach that tailors fire risk assessment to departmental contexts.<n>We present the first national-scale AI benchmark for metropolitan France using state-of-the-art AI models on a relatively unexplored dataset.
• Score: 1.8749305679160366
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Forest fire prediction involves estimating the likelihood of fire ignition or related risk levels in a specific area over a defined time period. With climate change intensifying fire behavior and frequency, accurate prediction has become one of the most pressing challenges in Artificial Intelligence (AI). Traditionally, fire ignition is approached as a binary classification task in the literature. However, this formulation oversimplifies the problem, especially from the perspective of end-users such as firefighters. In general, as is the case in France, firefighting units are organized by department, each with its terrain, climate conditions, and historical experience with fire events. Consequently, fire risk should be modeled in a way that is sensitive to local conditions and does not assume uniform risk across all regions. This paper proposes a new approach that tailors fire risk assessment to departmental contexts, offering more actionable and region-specific predictions for operational use. With this, we present the first national-scale AI benchmark for metropolitan France using state-of-the-art AI models on a relatively unexplored dataset. Finally, we offer a summary of important future works that should be taken into account. Supplementary materials are available on GitHub.

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