Gene expression programming to predict Manning’s <i>n</i> in meandering flows

Pradhan, Arpan; Khatua, Kishanjit Kumar

doi:10.1139/cjce-2016-0569

Cited by 24 publications

(7 citation statements)

References 8 publications

(6 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Manning's roughness is dependent upon various factors including channel geometry and sinuosity and not just the bed material [20][21][22]. erefore, calculation of Manning's n for the bed material is not appropriate on meandering channels.…”

Section: Methodsmentioning

confidence: 99%

Variation of Velocity Distribution in Rough Meandering Channels

Pradhan

Khatua

Sankalp

2018

Advances in Civil Engineering

Self Cite

View full text Add to dashboard Cite

Distribution of flow and velocity in a meandering river is important in river hydraulics to be investigated from a practical point of view in relation to the bank protection, navigation, water intakes, and sediment transport-depositional patterns. When flow enters a bend, the centrifugal force arising from the channel curvature leads to a transversal slope in the water surface. The interaction between the centrifugal force and transversal pressure gradient causes secondary flows in cross-sections, and the secondary flows spread further by moving along the bend. Hence, at the bends, these processes lead to longitudinal velocity increase in the inner wall and decrease at the outer wall. In this paper, experimentation is carried out for two different bed roughness on a 4.11 sinuosity meandering channel with 110° crossover. Longitudinal velocity distribution is analysed with the graphical illustrations for the detailed experimental study. Study of flow profile across the crossover is also particularly important as the inner bank of the bend changes to the outer bank and vice versa which has a significant effect on the water surface profile and hence on the velocity distribution along the full meander path. The objective of the analysis is to determine the effect of curvature and roughness on the velocity profiles, throughout the meander path. It is determined that the resistance of flow, on the smoother bed channel, is higher than that of the channel with higher Manning’s n above a certain depth at the apex and transition sections. A reciprocal study of the experimental investigation is attempted with a numerical hydrodynamic tool, namely, CCHE (Centre for Computational Hydroscience and Engineering) developed by NCCHE, University of Mississippi, US. The model is applied to simulate the inbank flow velocity distribution and validate the experimental observation for the meandering channel with rough bed.

show abstract

Section: Methodsmentioning

confidence: 99%

Variation of Velocity Distribution in Rough Meandering Channels

Pradhan

Khatua

Sankalp

2018

Advances in Civil Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…Ferreira [39] developed GEP based on fundamentals of gene development and evolution, which is a selection and rejection process. Guven and Gunal [40], Karbasi and Azamathulla [41] and Pradhan and Khatua [42] used this technique in the field of hydraulics to develop nonlinear mathematical models for local scour around structures, hydraulic jump characteristics and Manning's n, respectively. The two key factors that dominate the gene expression programming are chromosomes and the expressions of the genetic information encoded in the decision tree.…”

Section: Model Of α and β Correction Coefficients By Gene Expression mentioning

confidence: 99%

“…The convergence of such programming is analogous to the number of generation here, which conveys the number of iterations desired to obtain the optimal solution. The chromosomes can be unique single gene (unigenic) or multigenic with equal or unequal programs lengths [42]. The operators used in the generation can be arithmetic, geometric and/or combination of any mathematical expressions.…”

Section: Model Of α and β Correction Coefficients By Gene Expression mentioning

confidence: 99%

“…In the present study, GeneXproTool 5 software has been used for modelling the α and β correction coefficients for the converging and diverging channel sets, as mentioned in the previous section. RMSE fitness function produces the most accurate results in modelling references given by Karbasi and Azamathulla [41] and Pradhan and Khatua [42]. Following the same guidelines for the fitness function and selection of sets of training terminals, 70% for the function f (Eqs.…”

Section: Model Of α and β Correction Coefficients By Gene Expression mentioning

confidence: 99%

See 1 more Smart Citation

Models for kinetic energy and momentum correction coefficients for non-prismatic compound channels using regression and gene expression programming

et al. 2019

View full text Add to dashboard Cite

This paper studies the effect of different independent variables related to the characteristics of the compound channels on the kinetic energy and momentum correction coefficients (termed as α and β, respectively) of non-prismatic compound channels. A series of experiments were carried out in a converging compound channel with three converging angles, six width ratios, six relative depths and three relative distances. Meanwhile, flow data sets from three different authors were used for diverging compound channels, which include simple rectangular and trapezoidal cross sections. In the present study, conventional multivariable regression technique and gene expression programming were used for developing different models for calculating kinetic energy and momentum correction coefficients for converging and diverging compound channels, respectively. The independent variables used in the curve fitting process were also tested for their accountability and influence on the proposed models. The developed models were finally established with the validation data set and a natural river system.

show abstract

“…The GEP's capacity to generate mathematical correlations distinguishes it from other soft computing approaches such as ANN and SVM (Cousin & Savic 1997;Drecourt 1999;Savic et al 1999;Whigham & Crapper 1999, 2001Babovic & Keijzer 2002;Karimi et al 2015). However, river engineering using the GEP method has received far less attention (Harris et al 2003;Giustolisi 2004;Guven & Gunal 2008;Guven & Aytek 2009;Azamathulla et al 2013;Pradhan & Khatua 2017b). Parsaie et al (2015) use the support vector machine (SVM) technique to predict the discharge in the compound open channel.…”

Section: Introductionmentioning

confidence: 99%

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

Kaushik

Kumar

2022

Water Supply

View full text Add to dashboard Cite

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.

show abstract

Gene expression programming to predict Manning’s n in meandering flows

Cited by 24 publications

References 8 publications

Variation of Velocity Distribution in Rough Meandering Channels

Variation of Velocity Distribution in Rough Meandering Channels

Models for kinetic energy and momentum correction coefficients for non-prismatic compound channels using regression and gene expression programming

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

Contact Info

Product

Resources

About