Toward Resilient Communities: Integrating Predictive Flood Models with Natural Language Processing for Actionable Insights

Communities worldwide are increasingly concerned about flooding, making accurate forecasting crucial. This paper introduces two innovative models to improve the mapping of flood inundation areas and depths using large language models (LLMs) and advanced computational techniques. The first model analyzes historical gauge data to establish distinct inundation thresholds for each pixel, significantly enhancing forecast accuracy. The second model employs digital elevation models (DEMs) alongside projected water levels to determine water depth in real time. We tested these models in a flood-prone region, comparing results with traditional physical models. The threshold-based model achieved an impressive average F1-score of 0.87, outperforming the physical model’s score of 0.75. Additionally, our DEM-based model maintained a mean absolute error of only 0.15 m for water depth predictions, while the physical model’s error was 0.30 m. These findings demonstrate that our models can predict floods more accurately and efficiently. The integration of LLMs enhances computational effectiveness, enabling rapid processing of large datasets and facilitating real-time flood forecasting. LLMs simplify complex numerical data into actionable insights, generating tailored reports and alerts for city planners and emergency responders. Unlike traditional models that require extensive time and resources for calibration, our approach allows for quick adjustments to varying hydrological conditions and real-time updates. Overall, these innovative models represent a significant advancement in flood mapping, providing a more accurate, scalable, and economical alternative while enhancing resilience in flood-prone areas.