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

arXiv: https://arxiv.org/abs/2601.11686 · HTML: https://mlsanity.com/paper/2601.11686/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 8 references that are in the map.
- First posted on arXiv 16 Jan 2026.

## Builds on (most cited first, 1 of 1)

- [Extreme-value forest fire prediction A study of the Loss Function in an Ordinality Scheme](https://mlsanity.com/paper/2601.03327/) (2026)

## Built on by (most cited first, 1 of 1)

- [Risk Is Not the Target: A Monotonic Framework for Evaluating Wildfire Operational Risk Signals](https://mlsanity.com/paper/2607.21597/) (2026)

## Abstract (arXiv)

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.