Neural Computing and Applications

, Volume 23, Issue 5, pp 1343–1349

An expert system for predicting Manning’s roughness coefficient in open channels by using gene expression programming

  • H. Md. Azamathulla
  • Z. Ahmad
  • Aminuddin Ab. Ghani
Original Article

DOI: 10.1007/s00521-012-1078-z

Cite this article as:
Azamathulla, H.M., Ahmad, Z. & Ab. Ghani, A. Neural Comput & Applic (2013) 23: 1343. doi:10.1007/s00521-012-1078-z

Abstract

Manning’s roughness coefficient (n) has been widely used in the estimation of flood discharges or depths of flow in natural channels. Accurate estimation of Manning’s roughness coefficient is essential for the computation of flow rate, velocity. Conventional formulae that are greatly based on empirical methods lack in providing high accuracy for the prediction of Manning’s roughness coefficient. Consequently, new and accurate techniques are still highly demanded. In this study, gene expression programming (GEP) is used to estimate the Manning’s roughness coefficient. The estimated value of the roughness coefficient is used in Manning’s equation to compute the flow parameters in open-channel flows in order to carry out a comparison between the proposed GEP-based approach and the conventional ones. Results show that computed discharge using estimated value of roughness coefficient by GEP is in good agreement (±10%) with the experimental results compared to the conventional formulae (R2 = 0.97 and RMSE = 0.0034 for the training data and R2 = 0.94 and RMSE = 0.086 for the testing data).

Keywords

Open channel Friction coefficient Neural networks Genetic programming Manning’s equation 

Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  • H. Md. Azamathulla
    • 1
  • Z. Ahmad
    • 2
  • Aminuddin Ab. Ghani
    • 1
  1. 1.River Engineering and Urban Drainage Research Centre (REDAC)Universiti Sains MalaysiaNibong Tebal, Pulau PinangMalaysia
  2. 2.Department of Civil EngineeringIndian Institute of Technology RoorkeeRoorkeeIndia

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