A Review of Hydrodynamic and Machine Learning Approaches for Flood Inundation Modeling
Open access copy at publisher website
Description
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.
Impact and interest:
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ID Code: | 238185 | ||
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Item Type: | Contribution to Journal (Review article) | ||
Refereed: | Yes | ||
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Additional Information: | Funding Information: This research was funded by the Commonwealth Scientific and Industrial Research Organisation through its Digital Water and Landscape (DWL) project. | ||
Measurements or Duration: | 21 pages | ||
Keywords: | deep learning, environmental modeling, hydrodynamic model, machine learning | ||
DOI: | 10.3390/w15030566 | ||
ISSN: | 2073-4441 | ||
Pure ID: | 126041946 | ||
Funding Information: | This research was funded by the Commonwealth Scientific and Industrial Research Organisation through its Digital Water and Landscape (DWL) project. | ||
Copyright Owner: | 2023 The Authors | ||
Copyright Statement: | This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au | ||
Deposited On: | 23 Feb 2023 00:48 | ||
Last Modified: | 15 Jul 2024 08:37 |
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