Improving the fidelity and performance of a conceptual flood inundation mapping approach using a machine learning-based surrogate model

Abstract

This study focuses on enhancing the accuracy of Flood Inundation Mapping (FIM) by utilizing a surrogate modeling approach. The Height Above the Nearest Drainage (HAND) method is used as our baseline FIM approach. The terrain-based HAND-FIM framework was developed to allow large-scale applications at low computational costs. A Surrogate Model (SM) is constructed using machine learning-based methodologies to emulate the high-fidelity Hydrologic Engineering Center-River Analysis System (HEC-RAS) model. HAND-FIM, generated using streamflow data from the National Water Model, serves as the input to the SM, while the flood extent predicted by HEC-RAS for the same event serves as the target. Results demonstrate that SM reduces false alarms in HAND-FIM by 18 % while improving the Critical Success Index score by 26 %. Integrating the SM offers a promising approach for enhancing flood prediction accuracy, mitigating HAND-FIM limitations, and providing fast, cost-effective solutions for operational FIM applications, especially in data- and resource-limited regions.