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

TL;DR

This paper presents a hybrid framework combining predictive models and large language models to assess multiple wildfire risk factors and generate actionable reports, addressing operational needs often overlooked by existing methods.

Contribution

It introduces a novel multi-target wildfire risk assessment approach that integrates diverse predictive models with LLMs for comprehensive, practical risk reporting.

Findings

Demonstrates feasibility of combining predictive models with LLMs for wildfire risk analysis

Provides structured, actionable wildfire risk reports from heterogeneous data

Addresses operational needs of firefighting services

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.