Water Surface Profile Computation for Compound Channels with Narrow Flood Plains

Naik, B.; Khatua, Kishanjit Kumar

doi:10.1007/s13369-016-2236-x

Cited by 9 publications

(6 citation statements)

References 13 publications

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“…Therefore, the ANN model best agrees with the experimental data with high R 2 values. Figure 14 compares developed models for predicting water surface profile with the previous methods developed by Naik et al (2022) and Naik & Khatua (2016). It has been noticed that the constructed models are very close to the best-fitted line compared to previous methods and have a strong potential for generalization.…”

Section: Resultsmentioning

confidence: 82%

“…Figure 3 represents the experimental setup and the types of equipment that were employed in the research. Figure 4 depicts a plan view of the nonprismatic cross-sections of Rezaei (2006), Naik & Khatua (2016), and the current channel. At the downstream end of the flume, a tailgate was installed to control the water surface profile and impose a specific flow depth in the flume portion.…”

Section: Methodsmentioning

confidence: 99%

“…Numerous studies have investigated the shear force distribution, momentum transfer, and discharge methods of the flow in nonprismatic compound channels. Rezaei (2006), Naik & Khatua (2016), and the author's experimental data are used in the current work. Table 1 provides information on the experimental channel dimensions of these compound channels with various geometric properties.…”

Section: Data Sourcementioning

confidence: 99%

“…Experiments conducted in a laboratory are essential in order to improve one's comprehension related to the water surface profile of compound channels that include both prismatic and nonprismatic floodplains. For the purpose of predicting the water surface profile of a converging compound channel, Naik & Khatua (2016) used multivariate analysis to suggest Equations ( 1)-( 21), while Naik et al (2022) used the GEP technique to offer Equation ( 22).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…The gradient of shear stress rises as the surface roughness of the floodplain increases. Naik & Khatua (2016) developed a multivariate regression model to predict the water surface profile for different compound channels with the nonprismatic floodplain using nondimensional geometric factors. The constructed model has a high level of concordance with both the empirical evidence and the findings of other scholars.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Assessment of water surface profile in nonprismatic compound channels using machine learning techniques

Kaushik

Kumar

2022

Water Supply

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Accurate prediction of water surface profile in an open channel is the key to solving numerous critical engineering problems. The goal of the current research is to predict the water surface profile of a compound channel with converging floodplains using machine learning approaches, including Gene Expression Programming (GEP), Artificial Neural Networks (ANN), and Support Vector Machines (SVM), in terms of both geometric and flow variables, as past studies were more focused on geometric variables. A novel equation was also proposed using gene expression programming to predict the water surface profile. In order to evaluate the performance and efficacy of these models, statistical indices are used to validate the produced models for the experimental analysis. The findings demonstrate that the suggested ANN model accurately predicted the water surface profile, with coefficient of determination (R2) of 0.999, root mean square error (RMSE) of 0.003, and mean absolute percentage error (MAPE) of 0.107%, respectively, when compared to GEP, SVM, and previously developed methods. The study confirms the application of machine learning approaches in the field of river hydraulics, and forecasting water surface profile of nonprismatic compound channels using a proposed novel equation by gene expression programming made this study unique.

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Section: Resultsmentioning

confidence: 82%

Section: Methodsmentioning

confidence: 99%