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

2025-06-01

AI Summary

Traditional wildfire risk prediction suffers from oversimplification (e.g., binary classification), poor regional adaptability, and limited operational utility for fire departments. Addressing these limitations—and specifically meeting the practical needs of French fire services—this work proposes a *department-aware* risk modeling paradigm. Our approach integrates geospatial features, meteorological time-series data, and domain-specific prior knowledge from fire departments to construct an interpretable, multi-source deep learning model that upgrades prediction from binary classification to continuous, department-level risk quantification. Key contributions include: (1) the first formal definition and implementation of a department-aware modeling framework; (2) the release of France’s first national AI-powered wildfire risk benchmark dataset; and (3) nationwide deployment enabling high-accuracy, low-latency, localized risk assessment. Experimental results demonstrate significant improvements in resource allocation efficiency and decision-making credibility, confirming strong operational feasibility and scalability.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Machine Learning: Calibration & Uncertainty Quantification
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Predicts localized forest fire risk by department
• Addresses non-uniform risk across diverse regions
• Provides actionable AI-based operational decision support

Innovation

• Department-aware AI for localized fire risk
• National-scale benchmark with advanced AI
• Region-specific predictions for operational use