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A Hybrid Model for Predicting the Energy Dissipation on the Block Ramp Hydraulic Structures

Author

Listed:
  • Mostafa Rahmanshahi

    (The Hong Kong Polytechnic University)

  • Jafar Jafari-Asl

    (University of Sistan and Baluchestan)

  • Mahmood Shafai Bejestan

    (Shahid Chamran University of Ahvaz)

  • Seyedali Mirjalili

    (Torrens University
    Yonsei University)

Abstract

Block ramps are among the environmentally friendly hydraulic structures used for energy dissipation in rivers and waterways. Modeling the energy dissipation on these structures is ever-challenging in hydraulic engineering. The primary goal of the current study is to propose a novel metaheuristic-based artificial intelligence (AI) framework for energy dissipation prediction on block ramp structures. An improved African Vultures Optimization Algorithm (AVOA) is used to optimize the Adaptive Neuro-Fuzzy Inference System (ANFIS) in this investigation for accurate prediction of the energy dissipation on the block ramps. The performance of the hybrid ANFIS-IAVOA model is compared with an ANFIS and its hybrid versions using original AVOA, honey badger algorithm (ANFIS-HBA), grey wolf optimizer (ANFIS-GWO), monarch butterfly optimization (ANFIS-MBO), and black widow optimization (ANFIS-BWO) models. A dataset of 210 experiments measured at Shahid Chamran University of Ahvaz and 241 experiments collected from literature are used to construct the proposed hybrid models. The results demonstrate the better efficiency of hybrid ANFIS-IAVOA with RMSE of 0.018–0.020 and R2 of 0.98–0.98 compared to ANFIS-AVOA (RMSE ~ 0.023–0.25 and R2 ~ 0.97–0.97), ANFIS-HBA (RMSE ~ 0.021–0.025 and R2 ~ 0.97–0.97), ANFIS-MBO (RMSE ~ 0.022–0.023 and R2 ~ 0.97–0.97), ANFIS-GWO (RMSE ~ 0.022–0.024 and R2 ~ 0.97–0.97), ANFIS-BWO (RMSE ~ 0.027–0.028 and R2 ~ 0.96–0.96), and ANFIS (RMSE ~ 0.029–0.033 and R2 ~ 0.954 − 0.951). The statistical measures show that the proposed ANFIS-IAVOA performs better than the other metaheuristic-based and standalone ANFIS-developed models. The impressiveness of the proposed hybrid model demonstrates that it can be used for further investigations on the probabilistic assessment of the block ramp hydraulic structures.

Suggested Citation

  • Mostafa Rahmanshahi & Jafar Jafari-Asl & Mahmood Shafai Bejestan & Seyedali Mirjalili, 2023. "A Hybrid Model for Predicting the Energy Dissipation on the Block Ramp Hydraulic Structures," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(8), pages 3187-3209, June.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:8:d:10.1007_s11269-023-03497-x
    DOI: 10.1007/s11269-023-03497-x
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    References listed on IDEAS

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    1. Hashim, Fatma A. & Houssein, Essam H. & Hussain, Kashif & Mabrouk, Mai S. & Al-Atabany, Walid, 2022. "Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 192(C), pages 84-110.
    2. Rahman Khatibi & Farzin Salmasi & Mohammad Ghorbani & Hakimeh Asadi, 2014. "Modelling Energy Dissipation Over Stepped-gabion Weirs by Artificial Intelligence," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(7), pages 1807-1821, May.
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    1. Mehrdad Doustkam & Mostafa Rahmanshahi & Manoochehr Fathi-Moghadam & Alireza Keramat & Huan-Feng Duan, 2024. "Experimental Study on the Hydraulic Performance of Porous Broad-Crested Weirs with Sloping Crests," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(12), pages 4783-4802, September.

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