Assessing WildfireGPT: a comparative analysis of AI models for quantitative wildfire spread prediction

Meghana Ramesh, Ziheng Sun (), Yunyao Li, Li Zhang, Sai Kiran Annam, Hui Fang and Daniel Tong
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Meghana Ramesh: George Mason University
Ziheng Sun: George Mason University
Yunyao Li: University of Texas
Li Zhang: CIRES, University of Colorado
Sai Kiran Annam: George Mason University
Hui Fang: George Mason University
Daniel Tong: George Mason University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 11, No 30, 13117-13130

Abstract: Abstract This study examines the application of WildfireGPT for wildfire forecasting, focusing on its limitations in quantitative predicting Fire Radiative Power (FRP) spread and comparing its performance with a specialized predictive model based on TabNet. While WildfireGPT is widely accessible and convenient for wildfire-related discussions, it lacks the specialized training, real-time data integration, and algorithmic precision required for reliable wildfire forecasting. To highlight these shortcomings, we conducted an experiment using real-world NASA Fire Radiative Power (FRP) datasets. Our TabNet-based model, trained on variables such as Vapor Pressure Deficit (VPD), temperature (T), pressure (P), and Fire Weather Index (FWI), demonstrated high correlation, with low Mean Absolute Error (MAE) and Mean Squared Error (MSE) in forecasting FRP values. In contrast, RAG (retrieval-augmented generation) and LLM (large language model)-based chatbots like WildfireGPT have unreliable performance on quantitative FRP forecasting with the same input data as prompts. The findings underscore the potential risks of over-reliance on general-purpose AI tools like WildfireGPT for quantitative modeling tasks in wildfire management. This study advocates for informed usage of AI tools, emphasizing the necessity of domain-specific models for accurate and actionable wildfire forecasting.

Keywords: Wildfire prediction; Fire radiative power (FRP); WildfireGPT; TabNet; Machine learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11069-025-07344-7

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