Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
A Review of Hydrodynamic and Machine Learning Approaches for Flood Inundation Modeling
Machine learning (also called data-driven) methods have become popular in modeling flood inundations across river basins. Among data-driven methods, traditional machine learning (ML) approaches are widely used to model flood events, and recently deep learning (DL) approaches have gained more attention across the world. In this paper, we reviewed recently published literature on ML and DL applications for flood modeling for various hydrologic and catchment characteristics. Our extensive literature review shows that DL models produce better accuracy compared to traditional approaches. Unlike physically based models, ML/DL models suffer from the lack of using expert knowledge in modeling flood events. Apart from challenges in implementing a uniform modeling approach across river basins, the lack of benchmark data to evaluate model performance is a limiting factor for developing efficient ML/DL models for flood inundation modeling.
A Review of Hydrodynamic and Machine Learning Approaches for Flood Inundation Modeling
Machine learning (also called data-driven) methods have become popular in modeling flood inundations across river basins. Among data-driven methods, traditional machine learning (ML) approaches are widely used to model flood events, and recently deep learning (DL) approaches have gained more attention across the world. In this paper, we reviewed recently published literature on ML and DL applications for flood modeling for various hydrologic and catchment characteristics. Our extensive literature review shows that DL models produce better accuracy compared to traditional approaches. Unlike physically based models, ML/DL models suffer from the lack of using expert knowledge in modeling flood events. Apart from challenges in implementing a uniform modeling approach across river basins, the lack of benchmark data to evaluate model performance is a limiting factor for developing efficient ML/DL models for flood inundation modeling.
A Review of Hydrodynamic and Machine Learning Approaches for Flood Inundation Modeling
Fazlul Karim (Autor:in) / Mohammed Ali Armin (Autor:in) / David Ahmedt-Aristizabal (Autor:in) / Lachlan Tychsen-Smith (Autor:in) / Lars Petersson (Autor:in)
2023
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Metadata by DOAJ is licensed under CC BY-SA 1.0
Flood inundation modelling by a machine learning classifier
| Taylor & Francis Verlag | 2023
A GPU-Accelerated Hydrodynamic Model for Urban Flood Inundation
| British Library Conference Proceedings | 2013
| Wiley | 2023
Comparison of 1D Hydrodynamic Flood Inundation Modeling Capabilities of MIKE Hydro and HEC-RAS
| Springer Verlag | 2025