Risk to Resilience: LLM-Driven Agentic AI for Natural Hazard Assessment and Decision Support

Recent advances in large language models (LLMs) are transforming how geoscientists interact with data, models, and decision-support systems. Beyond literature web search and text processing, LLMs now enable new forms of knowledge discovery, real-time analysis, and human–AI collaboration in natural hazards and climate-risk research. At the same time, the increasing availability of geospatial data, remote sensing images, and model outputs creates both opportunities and challenges for integrating text-as-data approaches into operational geoscientific workflows.

We present a set of applied case studies demonstrating how LLM-driven assistant agents can be embedded into geoscientific systems to support flood risk assessment, hazard communication, and mitigation planning and decision. The demonstrated system integrates LLM agents with hydrodynamic models (HEC-RAS), geospatial flood and exposure datasets, a building-scale digital twin, and policy and planning documents such as the Louisiana State Hazard Mitigation Plan. Through a conversational interface, users can query flood risks, building exposure, mitigation scenarios, etc., while the LLM agent orchestrates model execution, data retrieval, and insights synthesis.

These case studies illustrate how LLMs can translate heterogeneous data sources into interpretable, policy-relevant information for practitioners and communities. In addition to demonstrating capabilities, we discuss methodological challenges related to reproducibility, transparency, and bias when deploying LLMs in hazard and hydrology applications, including issues of data provenance, prompt sensitivity, and model-driven interpretation. By sharing practical lessons learned from demonstrations in coastal Louisiana, this contribution highlights both the promise and limitations of using LLM agents as geoscientific assistants for real-time disaster monitoring, risk assessment, and decision support.