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WildfireGPT: Tailored Large Language Model for Wildfire Analysis

Yangxinyu Xie, Bowen Jiang, Tanwi Mallick, Joshua David Bergerson, John K. Hutchison, Duane R. Verner, Jordan Branham, M. Ross Alexander, Robert B. Ross, Yan Feng, Leslie-Anne Levy, Weijie Su, Camillo J. Taylor

TL;DR

WildfireGPT addresses the need for domain-specific wildfire risk analysis by augmenting LLM capabilities with climate projections and scientific literature through a Retrieval-Augmented Generation framework. It employs a multi-module LLM agent (User Profile, Planning, Analyst) that engages in multi-turn conversations to tailor analyses to user backgrounds and locales, retrieving location-specific climate data (FWI, past incidents) and literature via Faiss-based embedding access. The study reports four case studies with domain experts, achieving high correctness and relevance scores and demonstrating practical value for wildfire management and policy development. The work highlights the potential and challenges of deployable, data-informed LLM assistants for wildfire resilience.

Abstract

Recent advancement of large language models (LLMs) represents a transformational capability at the frontier of artificial intelligence. However, LLMs are generalized models, trained on extensive text corpus, and often struggle to provide context-specific information, particularly in areas requiring specialized knowledge, such as wildfire details within the broader context of climate change. For decision-makers focused on wildfire resilience and adaptation, it is crucial to obtain responses that are not only precise but also domain-specific. To that end, we developed WildfireGPT, a prototype LLM agent designed to transform user queries into actionable insights on wildfire risks. We enrich WildfireGPT by providing additional context, such as climate projections and scientific literature, to ensure its information is current, relevant, and scientifically accurate. This enables WildfireGPT to be an effective tool for delivering detailed, user-specific insights on wildfire risks to support a diverse set of end users, including but not limited to researchers and engineers, for making positive impact and decision making.

WildfireGPT: Tailored Large Language Model for Wildfire Analysis

TL;DR

WildfireGPT addresses the need for domain-specific wildfire risk analysis by augmenting LLM capabilities with climate projections and scientific literature through a Retrieval-Augmented Generation framework. It employs a multi-module LLM agent (User Profile, Planning, Analyst) that engages in multi-turn conversations to tailor analyses to user backgrounds and locales, retrieving location-specific climate data (FWI, past incidents) and literature via Faiss-based embedding access. The study reports four case studies with domain experts, achieving high correctness and relevance scores and demonstrating practical value for wildfire management and policy development. The work highlights the potential and challenges of deployable, data-informed LLM assistants for wildfire resilience.

Abstract

Recent advancement of large language models (LLMs) represents a transformational capability at the frontier of artificial intelligence. However, LLMs are generalized models, trained on extensive text corpus, and often struggle to provide context-specific information, particularly in areas requiring specialized knowledge, such as wildfire details within the broader context of climate change. For decision-makers focused on wildfire resilience and adaptation, it is crucial to obtain responses that are not only precise but also domain-specific. To that end, we developed WildfireGPT, a prototype LLM agent designed to transform user queries into actionable insights on wildfire risks. We enrich WildfireGPT by providing additional context, such as climate projections and scientific literature, to ensure its information is current, relevant, and scientifically accurate. This enables WildfireGPT to be an effective tool for delivering detailed, user-specific insights on wildfire risks to support a diverse set of end users, including but not limited to researchers and engineers, for making positive impact and decision making.
Paper Structure (11 sections, 3 figures, 2 tables)

This paper contains 11 sections, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Case Studies with Domain Experts
  4. Conclusions and Future Work
  5. Acknowledgement
  6. Appendix
  7. Detailed evaluation criteria
  8. Data Sources and Retrieval Details of WildfireGPT
  9. Quantitative Data Integration
  10. Scientific Literature Retrieval
  11. Chat Interface

Figures (3)

  • Figure 1: Here is an overview of the LLM agent integrated with the RAG framework. The LLM agent evaluates the user's input to determine the need for additional information. If needed, it retrieves climate projections and/or scientific papers. The agent then merges the retrieved data with its memory and a customized prompt to provide a response based on the augmented information.
  • Figure 2: Snapshot from the case study on ecosystem fire management: WildfireGPT demonstrates its ability to integrate data analysis and domain knowledge to provide actionable recommendations for ecosystem fire management. The domain expert's positive feedback highlights WildfireGPT's nuanced approach and its potential to support informed decision-making in wildfire management.
  • Figure 3: Overview of the WildfireGPT user experience. The screenshots are taken from one of the case studies themed comprehensive wildfire impact. The User Profile Module (top left) engages the user in a conversation to understand their background and concerns. The Planning Module (top middle) generates a tailored analysis plan based on the user's profile. The Analyst Module then executes the plan, analyzing Fire Weather Index data (top right and bottom left), conducting a literature review (bottom middle), and generating personalized recommendations (bottom right) to address the user's wildfire risk concerns.