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Abstract Timely, accurate, and reliable information is essential for decision-makers, emergency managers, and infrastructure operators during flood events. This study demonstrates that a proposed machine learning model,MaxFloodCast, trained on physics-based hydrodynamic simulations in Harris County, offers efficient and interpretable flood inundation depth predictions. Achieving an average$$R^2$$ of 0.949 and a Root Mean Square Error of 0.61 ft (0.19 m) on unseen data, it proves reliable in forecasting peak flood inundation depths. Validated against Hurricane Harvey and Tropical Storm Imelda,MaxFloodCastshows the potential in supporting near-time floodplain management and emergency operations. The model’s interpretability aids decision-makers in offering critical information to inform flood mitigation strategies, to prioritize areas with critical facilities and to examine how rainfall in other watersheds influences flood exposure in one area. TheMaxFloodCastmodel enables accurate and interpretable inundation depth predictions while significantly reducing computational time, thereby supporting emergency response efforts and flood risk management more effectively.
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Suitability of the height above nearest drainage (HAND) model for flood inundation mapping in data-scarce regions: a comparative analysis with hydrodynamic models
Abstract Unprecedented floods from extreme rainfall events worldwide emphasize the need for flood inundation mapping for floodplain management and risk reduction. Access to flood inundation maps and risk evaluation tools remains challenging in most parts of the world, particularly in rural regions, leading to decreased flood resilience. The use of hydraulic and hydrodynamic models in rural areas has been hindered by excessive data and computational requirements. In this study, we mapped the flood inundation in Huron Creek watershed, Michigan, USA for an extreme rainfall event (1000-year return period) that occurred in 2018 (Father’s Day Flood) using the Height Above Nearest Drainage (HAND) model and a synthetic rating curve developed from LIDAR DEM. We compared the flood inundation extent and depth modeled by the HAND with flood inundation characteristics predicted by two hydrodynamic models, viz., HEC-RAS 2D and SMS-SRH 2D. The flood discharge of the event was simulated using the HEC-HMS hydrologic model. Results suggest that, in different channel segments, the HAND model produces different degrees of concurrence in both flood inundation extent and depth when compared to the hydrodynamic models. The differences in flood inundation characteristics produced by the HAND model are primarily due to the uncertainties associated with optimal parameter estimation of the synthetic rating curve. Analyzing the differences between the HAND and hydrodynamic models also highlights the significance of terrain characteristics in model predictions. Based on the comparable predictive capability of the HAND model to map flood inundation areas during extreme rainfall events, we demonstrate the suitability of the HAND-based approach for mitigating flood risk in data-scarce, rural regions.
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- Award ID(s):
- 2133279
- PAR ID:
- 10485736
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Earth Science Informatics
- Volume:
- 17
- Issue:
- 3
- ISSN:
- 1865-0473
- Format(s):
- Medium: X Size: p. 1907-1921
- Size(s):
- p. 1907-1921
- Sponsoring Org:
- National Science Foundation
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