Prediction of the Discharge Coefficient of a Labyrinth Weir Type D by an Artificial Neural Network Method

• Autorzy: Belaabed Faris , Arabet Leila

Identyfikatory

DOI

10.2478/heem-2024-0004

• Języki publikacji: EN

Abstrakty

EN

This study presents the use, and its advantages, of artificial intelligence methods to predict the discharge coefficient (Cw), considering the approach conditions of the labyrinth weir type D. The study suggests modifying the training and validation rates in AI tools, which are often fixed without proper justification in previous studies. Unlike most studies that use geometric dimensions as inputs, this work focuses on the approach conditions (the emplacement of the labyrinth weir and filling the alveoli upstream and downstream) of the labyrinth weir type D. The results, based on laboratory experiments, show that these modified inputs significantly impact the efficiency and cost of constructing the weir. Moreover, the Cw predictions based on these inputs are highly satisfactory compared to laboratory test results. In terms of training and validation ratios, the study confirms that the optimal ratio is 70/30 for accurate and highly satisfactory predictions.

Słowa kluczowe

EN

ANN model; approach conditions; discharge coefficient; data splitting; labyrinth weir

• Wydawca: Institute of Hydro-Engineering of Polish Academy of Sciences
• Czasopismo: Archives of Hydro-Engineering and Environmental Mechanics
• Rocznik: 2024
• Tom: Vol. 71, nr 1
• Strony: 59--72
• Opis fizyczny: Bibliogr. 17 poz., rys., tab., wykr.

Twórcy

autor

Belaabed Faris

• Department of Civil and Hydraulic Engineering, Laboratory LGCE, Faculty of Sciences and Technologies, University of Jijel, Algeria
• Laboratory of Hydraulic Planning and Environment, University of Biskra, Biskra, Algeria

autor

Arabet Leila

• Department of Civil and Hydraulic Engineering, Laboratory LGCE, Faculty of Sciences and Technologies, University of Jijel, Algeria

Bibliografia

• Ahmad F., Hussain A., Ansari M. A. (2023) Development of ANN model for the prediction of discharge coefficient of an arced labyrinth side weir, Modeling Earth Systems and Environment, 9 (2), 1835–1842.
• Ayaz M., Mansoor T. (2021) Development of ANN model for discharge prediction and optimal design of sharp-crested triangular plan form weir for maximum discharge using linked ANN–optimization model, Water Supply, 21 (6), 3027–3041.
• Belaabed F. (2019) Etude des d´eversoirs non rectilignes noy´es par l’aval (Study of non-rectilinear weirs submerged downstream), Universit´e Mohamed Khider–Biskra, Biskra University [in French].
• Belaabed F., Goudjil K., Arabet L., Ouamane A. (2021) Utilization of computational intelligence approaches to estimate the relative head of PK-Weir for submerged flow, Neural Computing and Applications, 33 (19), 13001–13013.
• Ben Said M., Ouamane A. (2022) Performance of rectangular labyrinth weir – an experimental and numerical study, Water Supply, 22 (4), 3628–3644.
• Biener E. (1985) Rehabilitation of old gravity dams, Paper presented at the International Congress of Large Dams, France.
• Crookston B. M., Tullis B. (2010), Labyrinth weirs, Hydraulic Structures, 59.
• Falvey H. T., Treille P. (1995) Hydraulics and design of fusegates, Journal of Hydraulic Engineering, 121 (7), 512–518.
• Filo G. (2023) Artificial Intelligence Methods in Hydraulic System Design, Energies, 16 (8), 3320.
• Hekmat M., Sarkardeh H., Jabbari E., Samadi M. (2023) Application of a hybrid ANFIS with metaheuristic algorithms to estimate the aeration design parameters, Water Supply, 23 (6), 2249–2266.
• Houston K. L. (1983) Hydraulic Model Study of Hyrum Dam Auxiliary Labyrinth Spillway, U.S. Department of the Interior, Bureau of Reclamation, Division of Research, Hydraulics Branch, All U.S. Government Documents (Utah Regional Depository). Paper 159.
• Idrees A. K., Al-Ameri R. (2022) A review of hydraulic performance and design methods of labyrinth weirs, Water Supply, 22 (11), 8120–8138. Lux III F. (1987) Discussion of “Boardman Labyrinth-Crest Spillway” by John J. Cassidy, Christopher A. Gardner and Robert T. Peacock (March, 1985, Vol. 111, No. 3), Journal of Hydraulic Engineering, 113 (6), 808–811.
• Majedi-Asl M., Fuladipanah M., Arun V., Tripathi R. P. (2022) Using data mining methods to improve discharge coefficient prediction in Piano Key and Labyrinth weirs, Water Supply, 22 (2), 1964–1982.
• Majedi-Asl M., Ghaderi A., Kouhdaragh M., Alavian T. O. (2024) A performance comparison of the meta model methods for discharge coefficient prediction of labyrinth weirs, Flow Measurement and Instrumentation, 102563.
• Ouamane, A., Lemp´eri`ere F. (2006) Nouvelle conception de d´eversoir pour l’accroissement de la capacit ´e des retenues des barrages (New spillway design to increase the capacity of dam reservoirs), Paper presented at the Colloque international sur la protection et la pr´eservation des resources en eau, Bilda, Alg´erie [in French].
• Salmasi F., Nouri M., Sihag P., Abraham J. (2021) Application of SVM, ANN, GRNN, RF, GP and RT models for predicting discharge coefficients of oblique sluice gates using experimental data, Water Supply, 21 (1), 232–248.
• Seyedian S. M., Haghiabi A., Parsaie A. (2023) Reliable prediction of the discharge coefficient of triangular labyrinth weir based on soft computing techniques, Flow Measurement and Instrumentation, 92, 102403.