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Novel Grain and Form Roughness Estimator Scheme Incorporating Artificial Intelligence Models

Author

Listed:
  • Majid Niazkar

    (Shiraz University)

  • Nasser Talebbeydokhti

    (Shiraz University)

  • Seied Hosein Afzali

    (Shiraz University)

Abstract

Determination of flow resistance in open channel flows is not only important for practical engineering applications but also challenging because of multiple factors involved. The literature review reveals that despite of various data-driven formulas and schemes, only classic Manning’s resistance equation and Keulegan’s formula have been utilized in practice. It also indicates that sole application of Artificial Intelligence (AI) models facilitates roughness estimation while they have not been used within a systematic roughness estimator scheme. In this study, a new eight-step scheme is developed to predict grain and total Manning’s coefficients when grain and form roughness are the major sources of friction, respectively. The new scheme not only uses a new explicit equation for computing hydraulic radius related to bed for estimating grain roughness coefficient but also utilizes AI models named artificial neural network and genetic programming in the seventh step for estimating form roughness coefficient. It improves R2 for estimating Manning’s grain coefficient and RMSE for estimating discharge by 21% and 64% comparing with that of one of common formulas available in the literature, respectively. Moreover, the new scheme incorporating AI models significantly enhances the accuracy of estimation results for predicting roughness coefficient and discharge comparing with the new scheme using new developed empirical formula based on RMSE, MARE and R2 criteria. The obtained improvement demonstrates that application of AI models as a part of a data-based roughness estimator scheme, like the one suggested, may considerably improve the precision of prediction results of flow resistance and discharge.

Suggested Citation

  • Majid Niazkar & Nasser Talebbeydokhti & Seied Hosein Afzali, 2019. "Novel Grain and Form Roughness Estimator Scheme Incorporating Artificial Intelligence Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(2), pages 757-773, January.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:2:d:10.1007_s11269-018-2141-z
    DOI: 10.1007/s11269-018-2141-z
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    References listed on IDEAS

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    1. Majid Niazkar & Seied Hosein Afzali, 2016. "Application of New Hybrid Optimization Technique for Parameter Estimation of New Improved Version of Muskingum Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4713-4730, October.
    2. 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.
    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.
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    Cited by:

    1. 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.
    2. Chia-Cheng Shiu & Chih-Chung Chung & Tzuping Chiang, 2024. "Enhancing the EPANET Hydraulic Model through Genetic Algorithm Optimization of Pipe Roughness Coefficients," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(1), pages 323-341, January.
    3. Majid Niazkar, 2020. "Discussion of “Accurate and Efficient Explicit Approximations of the Colebrook Flow Friction Equation Based on the Wright ω-Function” by Dejan Brkić and Pavel Praks, Mathematics 2019, 7 , 34; doi:10.3," Mathematics, MDPI, vol. 8(5), pages 1-6, May.

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