A Hybrid Decision Support Framework Integrating Combined Probabilistic Spatial Modeling with Large Language Models for Post-Earthquake Search and Rescue

Abstract

This study describes the design and implementation of a hybrid decision support framework for post-earthquake urban Search and Rescue (SAR) prioritization, which combines two probabilistic spatial models with a Large Language Model (LLM). This approach examines the combination of two distance decay models: truncated negative exponential and lognormal models with the aim to transform discrete geolocated incident reports into a probabilistic priority surface. High-priority hotspots are identified using thresholding and spatial clustering. The proposed framework includes the use of the OpenAI model in its open source version for generating structured SAR recommendations. This approach is assessed using a synthetic dataset indicating post-earthquake locations in Mexico City. In addition, a sensitivity analysis was carried out to show stability in hotspot ranking. Our preliminary results indicate that the recommendations generated by the LLM match the hotspot scores. Thus, our proposed framework provides a suitable integration among geospatial modeling and LLM features for reasoning in urban disaster decision support.