Proof of Concept: Multi-Target Wildfire Risk Prediction and Large Language Model Synthesis

TL;DR

This paper proposes 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.

Abstract

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.

Figures (6)

• Figure 1: Position of the Alpes Maritimes in France and the segmentation in meteorological zones
• Figure 2: Normalized distributions of the four predictive targets across the seven meteorological zones.
• Figure 3: Correlation matrice between each targets.
• Figure 4: GRU neural network used in this article.
• Figure 5: Comparison of predicted and real signal in the zone 63 obtained for each target (GRU model).