Anticipate Manning’s Coefficient in Meandering Compound Channels
Department of Civil Engineering, National Institute of Technology Rourkela, Odisha 769008, India
Department of Civil Engineering, National Institute of Technology Patna, Bihar 800005, India
Author to whom correspondence should be addressed.
Received: 18 July 2018 / Revised: 20 August 2018 / Accepted: 22 August 2018 / Published: 27 August 2018
Estimating Manning’s roughness coefficient
is one of the essential factors in predicting the discharge in a stream. Present research work is focused on prediction of Manning’s
in meandering compound channels by using the Group Method of Data Handling Neural Network (GMDH-NN) approach. The width ratio
, relative depth
, Channel bed slope
, and meander belt width ratio
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
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.
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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MDPI and ACS Style
Mohanta, A.; Patra, K.C.; Sahoo, B.B. Anticipate Manning’s Coefficient in Meandering Compound Channels. Hydrology 2018, 5, 47.
Mohanta A, Patra KC, Sahoo BB. Anticipate Manning’s Coefficient in Meandering Compound Channels. Hydrology. 2018; 5(3):47.
Mohanta, Abinash; Patra, Kanhu C.; Sahoo, Bibhuti B. 2018. "Anticipate Manning’s Coefficient in Meandering Compound Channels." Hydrology 5, no. 3: 47.
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