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

Area of Science:

• Environmental Science
• Artificial Intelligence
• Remote Sensing

Background:

• WildfireGPT, a general AI tool, is often used for wildfire discussions.
• There is a need for reliable quantitative forecasting of wildfire behavior, specifically Fire Radiative Power (FRP).
• General-purpose AI may lack the specialized capabilities for accurate scientific prediction.

Purpose of the Study:

• To evaluate the quantitative forecasting performance of WildfireGPT for Fire Radiative Power (FRP) spread.
• To compare WildfireGPT's performance against a specialized TabNet-based predictive model.
• To highlight the limitations of general AI in complex environmental modeling.

Main Methods:

• Utilized real-world NASA Fire Radiative Power (FRP) datasets for experimentation.
• Developed and trained a TabNet-based model using variables like Vapor Pressure Deficit (VPD), temperature (T), pressure (P), and Fire Weather Index (FWI).
• Assessed the performance of both WildfireGPT (using RAG and LLM) and the TabNet model on quantitative FRP forecasting.

Main Results:

• The TabNet-based model demonstrated high correlation with low Mean Absolute Error (MAE) and Mean Squared Error (MSE) in FRP forecasting.
• WildfireGPT exhibited unreliable performance in quantitative FRP forecasting when given the same input data as prompts.
• General AI tools showed significant shortcomings compared to domain-specific models for this task.

Conclusions:

• General AI tools like WildfireGPT are not suitable for quantitative wildfire forecasting tasks.
• Domain-specific AI models are necessary for accurate and actionable wildfire management.
• Informed usage of AI tools is crucial, prioritizing specialized models for critical applications.