Abstract
Estimating Manning’s roughness coefficient ( n ) is one of the essential factors in predicting the discharge in a stream. Present research work is focused on prediction of Manning’s n in meandering compound channels by using the Group Method of Data Handling Neural Network (GMDH-NN) approach. The width ratio ( α ) , relative depth ( β ) , sinuosity ( s ) , Channel bed slope ( S o ) , and meander belt width ratio ( ω ) are specified as input parameters for the development of the model. The performance of GMDH-NN is evaluated with two different machine learning techniques, namely the support vector regression (SVR) and multivariate adaptive regression spline (MARS) with various statistical measures. Results indicate that the proposed GMDH-NN model predicts the Manning’s n satisfactorily as compared to the MARS and SVR model. This GMDH-NN approach can be useful for practical implementation as the prediction of Manning’s coefficient and subsequently discharge through Manning’s equation in the compound meandering channels are found to be quite adequate.
Highlights
In river engineering, the Manning’s coefficient (n) plays a vital role in the computation of flood discharge, velocity distribution, designing structures, calculating energy losses [1,2], and other hydraulic parameters
The fitness of the Group Method of Data Handling Neural Network (GMDH-NN) model is compared with support vector regression (SVR) and multivariate adaptive regression spline (MARS) machine learning methods
The models achieved in this study seem to be very useful for the prediction of the Manning’s n of two-stage meandering channels
Summary
The Manning’s coefficient (n) plays a vital role in the computation of flood discharge, velocity distribution, designing structures, calculating energy losses [1,2], and other hydraulic parameters. Accurate estimation of Manning’s roughness (n) plays an important factor in flood conveyance estimation. Manning’s n represented bed roughness, and signifies the resistance to flow. Various equations are used for getting the discharge in simple channels using. Manning’s n, Chezy’s C and Darcy-Weisbach’s f [3], but those equations are not adequate well, while predicting discharge in meandering compound channels. There are various elements that affect the resistance coefficient, such as the bed roughness, slope of the bed, geometry of the channel, and other parameters of a river
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