[Paper Review] WildfireGPT: Tailored Large Language Model for Wildfire Analysis

Yangxinyu Xie, Jiang, Bowen+11 more|arXiv (Cornell University)|Feb 12, 2024

Fire effects on ecosystems4 citations

TL;DR

WildfireGPT is a specialized large language model agent that enhances wildfire risk analysis by integrating Retrieval-Augmented Generation (RAG) with domain-specific climate projections and scientific literature. It enables dynamic, context-aware conversations with users, transforming vague queries into actionable, evidence-based insights for decision-makers in wildfire resilience and water resource management.

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.

Motivation & Objective

• To address the limitations of general-purpose LLMs in delivering context-specific, scientifically accurate wildfire risk information.
• To support decision-makers in wildfire resilience and water resource management by transforming ambiguous user queries into structured, actionable insights.
• To integrate climate projections and peer-reviewed scientific literature into an interactive LLM agent for real-time, evidence-based responses.
• To enhance user engagement through iterative clarification, strategic planning, and memory-aware dialogue management.
• To provide tailored, data-driven recommendations for homeowners, urban planners, and emergency managers facing wildfire and water scarcity risks.

Proposed method

• The system employs a multi-module LLM agent architecture with user profile, planning, memory, and toolbox modules to guide user interaction.
• It uses Retrieval-Augmented Generation (RAG) to retrieve and ground responses in up-to-date climate projections and scientific literature.
• The agent dynamically generates clarifying questions to refine user intent and build a personalized user profile.
• A planning module formulates step-by-step strategies based on user needs and retrieved context.
• The memory module maintains context across interactions, ensuring coherence and continuity in dialogue.
• The toolbox module integrates external data sources such as IPCC AR6 reports and climate modeling outputs to enhance response accuracy.

Experimental results

Research questions

• RQ1 How can a large language model be adapted to deliver domain-specific, scientifically accurate wildfire risk insights beyond generic responses?
• RQ2 To what extent can an LLM agent improve user understanding and decision-making in wildfire and water resource management through interactive clarification and planning?
• RQ3 What role does retrieval-augmented generation play in enhancing factual accuracy and contextual relevance in wildfire risk analysis?
• RQ4 How does integrating climate projections and scientific literature into an LLM agent affect the quality and reliability of its recommendations?
• RQ5 Can an LLM agent effectively support non-expert users in formulating and resolving complex, multi-faceted wildfire-related concerns?

Key findings

• WildfireGPT successfully transforms vague or broad user inquiries into structured, actionable insights through iterative clarification and planning.
• The integration of RAG with scientific literature and climate projections significantly improves the factual accuracy and relevance of responses compared to standard LLMs.
• User profile and memory modules enable sustained, context-aware conversations, enhancing user engagement and response personalization.
• The agent provides evidence-based recommendations for homeowners, including fire risk mitigation and water supply protection strategies, grounded in peer-reviewed studies.
• Case studies demonstrate that WildfireGPT can accurately reference and synthesize findings from key literature, such as Margolis et al. (2017), Scasta et al. (2016), and Gutzler & Alst (2010), on drought, fuel availability, and monsoon impacts.
• The system effectively addresses complex, multi-layered concerns—such as the interplay between wildfire risk and water scarcity—by synthesizing information from diverse scientific sources.