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Development of Advanced Data-Intelligence Models for Radial Gate Discharge Coefficient Prediction: Modeling Different Flow Scenarios

Author

Listed:
  • Zaher Mundher Yaseen

    (King Fahd University of Petroleum & Minerals
    King Fahd University of Petroleum & Minerals)

  • Omer A. Alawi

    (Universiti Teknologi Malaysia)

  • Ammar Mohammed Alshammari

    (King Fahd University of Petroleum & Minerals
    King Fahd University of Petroleum & Minerals)

  • Ali Alsuwaiyan

    (King Fahd University of Petroleum and Minerals
    KFUPM)

  • Mojeed Opeyemi Oyedeji

    (King Fahd University of Petroleum & Minerals (KFUPM))

  • Atheer Y. Oudah

    (University of Thi-Qar
    Al-Ayen University)

Abstract

This research aims to predict a radial gate's discharge coefficient (Cd) under free and submerged flow conditions using several machine learning (ML) algorithms. Several parameters are used to develop the learning process of the ML algorithms, including the gate radius (R), gate opening height (W), depth of water upstream (Yo), depth of water downstream (YT), trunnion pin height (h), and width (B). For this purpose, various new versions of ML models have been developed, such as the bagging regression tree (BGRT), bidirectional recurrent neural network (Bi-RNN), bidirectional long short-term memory (Bi-LSTM), Light Gradient Boosted Machine (LightGBM) Ensemble, Multiple Additive Regression Trees (MART), and Neural Regression Forests (NRFs). This study was extended to examine the sensitivity of the adopted predictors for Cd prediction. The adopted ML models generally achieved good and acceptable predictability. In quantitative metrics, Cd was accurately predicted using the Bi-LSTM model with a minimum value of mean absolute percentage error (MAPE = 2.245) and maximum Willmott index (WI = 0.861) over the testing phase for the free-flow condition. For the submerged flow condition, the BGRT model attained the best results, with (MAPE = 2.899) and (WI = 0.900).

Suggested Citation

  • Zaher Mundher Yaseen & Omer A. Alawi & Ammar Mohammed Alshammari & Ali Alsuwaiyan & Mojeed Opeyemi Oyedeji & Atheer Y. Oudah, 2023. "Development of Advanced Data-Intelligence Models for Radial Gate Discharge Coefficient Prediction: Modeling Different Flow Scenarios," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(14), pages 5677-5705, November.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:14:d:10.1007_s11269-023-03624-8
    DOI: 10.1007/s11269-023-03624-8
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    References listed on IDEAS

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    1. Jin-Cheng Fu & Hsiao-Yun Huang & Jiun-Huei Jang & Pei-Hsun Huang, 2019. "River Stage Forecasting Using Multiple Additive Regression Trees," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(13), pages 4491-4507, October.
    2. Jiun-Huei Jang & Kun-Fang Lee & Jin-Cheng Fu, 2022. "Improving River-Stage Forecasting Using Hybrid Models Based on the Combination of Multiple Additive Regression Trees and Runge–Kutta Schemes," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(3), pages 1123-1140, February.
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