Applied Water Science · 2024 · 12 citations · 27 references
Natural ResourcesCivil EngineeringHydrologyCatchment HydrologyHydrologic ModelingWater Resources EngineeringCatchment AnalysisFlood ForecastingFlood Inundation ModelingHydraulic ModelingHydrological ModelingRainfall-runoff ModelingCatchment Hydrology ModelingDam Breach ModelingDigital Elevation Model Inundation MappingFlood Hydrograph CharacterizationFlood ModelingHigh-resolution Hydrodynamic ModelingRainfall-runoff SimulationSatellite Imagery Analysis
Dam breach floods pose significant threats to downstream areas, necessitating accurate prediction of inundation scenarios to mitigate potential damage. This paper presents a novel methodology for hydrodynamic modeling of dam breach floods, leveraging a comprehensive approach that integrates satellite imagery, unmanned aerial vehicles (UAVs), and Google Earth Engine (GEE) to forecast downstream inundation scenarios. Specifically, UAVs were utilized to generate high-resolution Digital Elevation Models (DEMs) of the flood-affected areas, ensuring precise representation of topography in the model. The approach incorporates Cartosat DEM data for catchment modeling, while NASA's Global Precipitation Measurement mission data, integrated with GEE, facilitated accurate estimation of rainfall in ungagged catchment areas. Furthermore, the Hydrological Engineering Center-Hydrological Modeling System was employed for rainfall-runoff simulation and flood hydrograph derivation, followed by application of the HEC River Analysis System (RAS) for hydrodynamic modeling under dam breach conditions. This integrated modeling approach was applied as a case study of Banaskantha district, Gujarat, India. The outcome was the generation of scenario maps based on HEC-RAS results, which include flood extent, water depth, and flow velocity, highlighting downstream areas affected by flooding. Validation of the hydrodynamic dam breach model performance was conducted using actual field measurements and simulated results, employing statistical analysis methods including Support Vector Regression (SVR) and linear regression to determine coefficient of determination ( R 2 ), Root-Mean-Square Error, and Mean Absolute Error of observed and simulated data. The coefficient of determination ( R 2 ) values for measured and simulated flow (0.91) and water level (0.86) calculated using SVR demonstrate strong correlation between observed and simulated values. This integrated study of hydrodynamic modeling in data-scarce areas aids in accurate estimation of future probable flooding in downstream areas in the event of a dam break, underscoring the potential of advanced surveying and modeling techniques in flood assessment and management. Ultimately, this integration of technologies aims to enhance community resilience and mitigate socioeconomic costs associated with dam breach floods.
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Flood Prediction Using Machine Learning Models: Literature Review
Dhruvesh Patel, Jorge Ramirez, Prashant K. Srivastava et al. · Natural Hazards · 2017 · 262 citations · Full text
Patrick Matgen, Guy Schumann, Jean-Baptiste Henry et al. · International Journal of Applied Earth Observation and Geoinformation · 2006 · 260 citations
Natural Resources, Hydrology, Water Resources Management +17