A comparative evaluation of machine learning approaches for erosion prediction in dam-break problems

This study presents a novel integration of empirical erosion models optimized using machine learning methods in the finite volume solution of dam-break flows over erodible beds. Computational hydraulics and machine learning approaches are implemented and compared in modeling and simulation of erosio...

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Veröffentlicht in:	European physical journal plus Jg. 140; H. 10; S. 976
Hauptverfasser:	Al-Ghosoun, Alia, Gumus, Veysel, Seaid, Mohammed
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 14.10.2025 Springer Nature B.V
Schlagworte:	Accuracy Applied and Technical Physics Atomic Bed load Complex Systems Condensed Matter Physics Dam failure Datasets Entrainment Environmental impact Finite volume method Fluid flow Forecasting techniques Grain size Hydraulics Machine learning Mathematical and Computational Physics Mathematical models Molecular Neural networks Numerical analysis Optical and Plasma Physics Optimization Physics Physics and Astronomy Regular Article Researchers Sediment transport Shallow water Shallow water equations Soil erosion Support vector machines Theoretical Unmanned aerial vehicles Water
ISSN:	2190-5444, 2190-5444
Online-Zugang:	Volltext
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