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Use of Gene-Expression Programming to Estimate Manning’s Roughness Coefficient for High Gradient Streams

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  • H. Azamathulla
  • Robert Jarrett

Abstract

Manning’s roughness coefficient (n) has been widely used in the estimation of flood discharges or depths of flow in natural channels. Therefore, the selection of appropriate Manning’s n values is of paramount importance for hydraulic engineers and hydrologists and requires considerable experience, although extensive guidelines are available. Generally, the largest source of error in post-flood estimates (termed indirect measurements) is due to estimates of Manning’s n values, particularly when there has been minimal field verification of flow resistance. This emphasizes the need to improve methods for estimating n values. The objective of this study was to develop a soft computing model in the estimation of the Manning’s n values using 75 discharge measurements on 21 high gradient streams in Colorado, USA. The data are from high gradient (S > 0.002 m/m), cobble- and boulder-bed streams for within bank flows. This study presents Gene-Expression Programming (GEP), an extension of Genetic Programming (GP), as an improved approach to estimate Manning’s roughness coefficient for high gradient streams. This study uses field data and assessed the potential of gene-expression programming (GEP) to estimate Manning’s n values. GEP is a search technique that automatically simplifies genetic programs during an evolutionary processes (or evolves) to obtain the most robust computer program (e.g., simplify mathematical expressions, decision trees, polynomial constructs, and logical expressions). Field measurements collected by Jarrett (J Hydraulic Eng ASCE 110: 1519–1539, 1984 ) were used to train the GEP network and evolve programs. The developed network and evolved programs were validated by using observations that were not involved in training. GEP and ANN-RBF (artificial neural network-radial basis function) models were found to be substantially more effective (e.g., R 2 for testing/validation of GEP and RBF-ANN is 0.745 and 0.65, respectively) than Jarrett’s (J Hydraulic Eng ASCE 110: 1519–1539, 1984 ) equation (R 2 for testing/validation equals 0.58) in predicting the Manning’s n. Copyright Springer Science+Business Media Dordrecht 2013

Suggested Citation

  • H. Azamathulla & Robert Jarrett, 2013. "Use of Gene-Expression Programming to Estimate Manning’s Roughness Coefficient for High Gradient Streams," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(3), pages 715-729, February.
    • Handle: RePEc:spr:waterr:v:27:y:2013:i:3:p:715-729
    DOI: 10.1007/s11269-012-0211-1
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    References listed on IDEAS

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    1. Li, Zhe & Zhang, Juntao, 2001. "Calculation of Field Manning's Roughness Coefficient," Agricultural Water Management, Elsevier, vol. 49(2), pages 153-161, July.
    2. Seydou Traore & Aytac Guven, 2012. "Regional-Specific Numerical Models of Evapotranspiration Using Gene-Expression Programming Interface in Sahel," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(15), pages 4367-4380, December.
    3. Hazi Azamathulla & Aminuddin Ghani, 2011. "Genetic Programming for Predicting Longitudinal Dispersion Coefficients in Streams," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(6), pages 1537-1544, April.
    4. Hazi Azamathulla & Aminuddin Ghani & Cheng Leow & Chun Chang & Nor Zakaria, 2011. "Gene-Expression Programming for the Development of a Stage-Discharge Curve of the Pahang River," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(11), pages 2901-2916, September.
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    Cited by:

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    2. Mohammad Bahrami Yarahmadi & Abbas Parsaie & Mahmood Shafai-Bejestan & Mostafa Heydari & Marzieh Badzanchin, 2023. "Estimation of Manning Roughness Coefficient in Alluvial Rivers with Bed Forms Using Soft Computing Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3563-3584, July.
    3. Vasileios Kitsikoudis & Epaminondas Sidiropoulos & Lazaros Iliadis & Vlassios Hrissanthou, 2015. "A Machine Learning Approach for the Mean Flow Velocity Prediction in Alluvial Channels," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(12), pages 4379-4395, September.
    4. Kazem Shahverdi & Hossein Talebmorad, 2023. "Automating HEC-RAS and Linking with Particle Swarm Optimizer to Calibrate Manning’s Roughness Coefficient," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(2), pages 975-993, January.
    5. Vesna Đukić & Zoran Radić, 2016. "Sensitivity Analysis of a Physically Based Distributed Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(5), pages 1669-1684, March.
    6. Gokmen Tayfur, 2017. "Modern Optimization Methods in Water Resources Planning, Engineering and Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(10), pages 3205-3233, August.
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    8. Vesna Đukić & Zoran Radić, 2016. "Sensitivity Analysis of a Physically Based Distributed Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(5), pages 1669-1684, March.

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