Advanced Automation of HEC-RAS for Predictive Floodplain Mapping and Early Warning through Probabilistic Deep Learning

Effective flood risk management crucially depends on precise floodplain inundation mapping and proactive early warning systems. This study introduces an innovative framework that automates the Hydrologic Engineering Center's River Analysis System (HEC-RAS) for 2D unsteady flow simulations, integrated with a state-of-the-art probabilistic deep learning model for enhanced streamflow prediction. This framework innovatively forecasts both lower and upper inundation bounds, substantially improving the accuracy and reliability of flood risk assessments. It employs a probabilistic deep learning model using a Transformer-based neural network with a distribution head, allowing dynamic adaptation to diverse hydrological conditions. This adaptation supports the generation of precise flood scenarios and enables effective, timely interventions. Validation across a series of South Korean case studies, selected for their hydrological diversity, confirms the framework's enhanced predictive capabilities in mapping flood extents over conventional methods. Additionally, integrating automated parameter optimization, Monte Carlo simulations, and adaptive learning algorithms within HEC-RAS enhances the scalability and adaptability of flood modeling efforts. The automated framework streamlines complex simulation processes while effectively addressing inherent model uncertainties and integration challenges in practical applications. By providing a robust, scalable, and adaptable tool, this framework contributes to hydrological modeling and transforming flood risk management in flood-prone areas worldwide.