An Integrated XGBoost and Large Language Model Framework for Disaster Prediction and Alert Generation
Abstract
The escalating frequency and intensity of natural disasters necessitate predictive systems that combine computational robustness with operational efficiency. This paper presents a hybrid disaster management framework that integrates XG-Boost, a gradient-boosted tree algorithm, with a large language model (LLM) to enhance forecast accuracy and decision support. The system processes heterogeneous data streams-including meteorological readings, seismic signals, social media activity, and infrastructure sensor data-to generate hazard classifications and intensity projections. XGBoost handles structured tabular data through iterative gradient optimization, delivering probabilistic outputs with interpretable feature contributions. Concurrently, the LLM processes unstructured textual inputs, translating predictive outputs into contextualized situational reports, plain-language alerts, and actionable response checklists. A feedback loop incorporates post-event logs to refine both model parameters and communication templates. Validation against historical disaster records and simulated emergency scenarios demonstrates that the integrated architecture reduces false alarm rates, decreases decision latency by 62%, and improves communication clarity among first responders and civic authorities. The proposed system provides a scalable template for next-generation early warning infrastructures that balance statistical rigor with human-centered information delivery.