<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Climate change can have a profound impact on river flooding, leading to increased frequency and severity of floods. To mitigate these effects, it is crucial to focus on enhancing early warning systems and bolstering infrastructure resilience through improved forecasting. This proactive approach enables communities to better plan for and respond to flood events, thereby minimizing the adverse consequences of climate change on river floods. During river flooding, the channels often take on a compound nature, with varying geometries along the flow length. This complexity arises from construction and agricultural activities along the floodplains, resulting in converging, diverging, or skewed compound channels. Modelling the flow in these channels requires consideration of additional momentum transfer factors. In this study, machine learning techniques, including Gene Expression Programming (GEP), Artificial Neural Networks (ANN), and Support Vector Machines (SVM), were employed. The focus was on a compound channel with converging floodplains, predicting the shear force carried by the floodplains in terms of non-dimensional flow and hydraulic parameters. The findings indicate that the proposed ANN model outperformed GEP, SVM, and other established approaches in accurately predicting floodplain shear force. This research underscores the efficacy of utilizing machine learning techniques in the examination of river hydraulics.</p>
</abstract>

Climate change can have a profound impact on river flooding, leading to increased frequency and severity of floods. To mitigate these effects, it is crucial to focus on enhancing early warning systems and bolstering infrastructure resilience through improved forecasting. This proactive approach enables communities to better plan for and respond to flood events, thereby minimizing the adverse consequences of climate change on river floods. During river flooding, the channels often take on a compound nature, with varying geometries along the flow length. This complexity arises from construction and agricultural activities along the floodplains, resulting in converging, diverging, or skewed compound channels. Modelling the flow in these channels requires consideration of additional momentum transfer factors. In this study, machine learning techniques, including Gene Expression Programming (GEP), Artificial Neural Networks (ANN), and Support Vector Machines (SVM), were employed. The focus was on a compound channel with converging floodplains, predicting the shear force carried by the floodplains in terms of non-dimensional flow and hydraulic parameters. The findings indicate that the proposed ANN model outperformed GEP, SVM, and other established approaches in accurately predicting floodplain shear force. This research underscores the efficacy of utilizing machine learning techniques in the examination of river hydraulics.

Prediction of shear stress distribution in compound channel with smooth converging floodplains

Journal of Hydrology and Hydromechanics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Climate change can have a profound impact on river flooding, leading to increased frequency and severity of floods. To mitigate these effects, it is crucial to...

Prediction of shear stress distribution in compound channel with smooth converging floodplains

Online veröffentlicht: 09. Mai 2024

Seitenbereich: 170 - 184

Eingereicht: 23. Nov. 2023

Akzeptiert: 10. Jan. 2024

DOI: https://doi.org/10.2478/johh-2024-0004

Schlüsselwörter
Compound channel, Converging floodplains, Shear stress distribution, Non-dimensional parameters, Machine learning approaches

© 2024 Vijay Kaushik et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Prediction of shear stress distribution in compound channel with smooth converging floodplains

Online veröffentlicht: 09. Mai 2024

Seitenbereich: 170 - 184

Eingereicht: 23. Nov. 2023

Akzeptiert: 10. Jan. 2024

DOI: https://doi.org/10.2478/johh-2024-0004

SchlüsselwörterCompound channel, Converging floodplains, Shear stress distribution, Non-dimensional parameters, Machine learning approaches

© 2024 Vijay Kaushik et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Compound channel, Converging floodplains, Shear stress distribution, Non-dimensional parameters, Machine learning approaches