Boundary shear stress distribution for a converging compound channel

Naik, B.; Khatua, Kishanjit Kumar

doi:10.1080/09715010.2016.1165633

Cited by 18 publications

(7 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The present study incorporates the works of Rezaei (2006), Naik and Khatua (2016), as well as the experimental data collected by the author. Table 1 presents data about the experimental channel dimensions of compound channels with different geometric features.…”

Section: Materials and Methods Data Sourcementioning

confidence: 99%

See 1 more Smart Citation

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

Kaushik,

Kumar

2024

Journal of Hydrology and Hydromechanics

View full text Add to dashboard Cite

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.

show abstract

Section: Materials and Methods Data Sourcementioning

confidence: 99%

“…The magnitude of shear stress increases with the augmentation of surface roughness on the floodplain. The researchers Naik and Khatua (2016) proposed a novel equation that allows for the estimation of the distribution of boundary shear stress by using non-dimensional geometric and flow variables.…”

Section: Introductionmentioning

confidence: 99%

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

Kaushik,

Kumar

2024

Journal of Hydrology and Hydromechanics

View full text Add to dashboard Cite

show abstract

“…To develop the mentioned artificial intelligence models,196 Laboratory data of Othman (2000); Bousmar (2002aBousmar ( , 2002b, Rezaei & Knight (2009), Yonesi et al (2013), andKhatua (2016) have been used Table 1 shows the statistical characteristics of the collected data.…”

Section: Non-prismatic Compound Open Channelsmentioning

confidence: 99%

Discharge modeling in compound channels with non-prismatic floodplains using GMDH and MARS models

et al. 2022

View full text Add to dashboard Cite

In this study, modeling of discharge was performed in compound open channels with non-prismatic floodplain (CCNPF) using soft computation models including Multivariate Adaptive Regression Splines (MARS) and Group Method of Data Handling (GMDH) and then their results were compared with the multilayer perceptron neural networks (MLPNN). In addition to the total discharge, the discharge separation between the floodplain and main channel was modeled and predicted. The parameters of relative roughness coefficient, the relative area of flow cross-section, relative hydraulic radius, bed slope, the relative width of water surface, relative depth, convergence or divergence angle, relative longitudinal distance as inputs, and discharge were considered as models output. The results demonstrated that the statistical indices of MARS, GMDH, and MLPNN models in the testing stage are R2 = 0.962(RMSE = 0.003), 0.930(RMSE = 0.004), and 0.933(RMSE = 0.004) respectively. Examination of statistical error indices o shows that all of the developed models have the appropriate accuracy to estimate the flow discharge in CCNPF. Examination of the structure of developed GMDH and MARS models demonstrated that the parameters of relative: roughness, area, hydraulic radius, flow aspect ratio, depth, and angle of convergence or divergence of floodplain have the greatest impact on modeling and estimation of discharge.

show abstract

“…2 , MAE, RMSE, and MAPE for different series of datasets, the established GEP model show superior outcomes in comparison to other methods as Naik and Khatua[48], Khatua et al[46], Knight and Hamed[17], Mohanty et al[54]. The GEP approach's compatibility is determined by the model's mean percentage of error and the approaches' suitability.…”

mentioning

confidence: 92%

Prediction of Boundary Shear Stress Distribution in Converging Compound Channel Using Gene Expression Programming

Naik

Kaushik

Kumar

2022

Preprint

View full text Add to dashboard Cite

The computation of the boundary shear stress distribution in an open channel flow is required for a variety of applications, including the flow resistance relationship and the construction of stable channels. The river breaches the main channel and spills across the floodplain during overbank flow conditions on both sides. Due to the momentum shift between the primary channel and adjacent floodplains, the flow structure in such compound channels becomes complicated. This has a profound impact on the shear stress distribution in the floodplain and main channel subsections. In addition, agriculture and development activities have occurred in floodplain parts of a river system. As a consequence, the geometry of the floodplain changes over the length of the flow, resulting in a converging compound channel. Traditional formulas, which rely heavily on empirical approaches, are ineffective in predicting shear force distribution with high precision. As a result, innovative and precise approaches are still in great demand. The boundary shear force carried by floodplains is estimated by gene expression programming (GEP) in this paper. In terms of non-dimensional geometric and flow variables, a novel equation is constructed to forecast boundary shear force distribution. The proposed GEP-based method is found to be best when compared to conventional methods. The findings indicate that the predicted percentage shear force carried by floodplains determined using GEP is in good agreement with the experimental data compared to the conventional formulas (R2 = 0.96 and RMSE = 3.395 for the training data and R2 = 0.95 and RMSE = 4.022 for the testing data).

show abstract

Boundary shear stress distribution for a converging compound channel

Cited by 18 publications

References 10 publications

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

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

Discharge modeling in compound channels with non-prismatic floodplains using GMDH and MARS models

Prediction of Boundary Shear Stress Distribution in Converging Compound Channel Using Gene Expression Programming

Contact Info

Product

Resources

About