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A NEW NON‐TUNED SELF‐ADAPTIVE MACHINE‐LEARNING APPROACH FOR SIMULATING THE DISCHARGE COEFFICIENT OF LABYRINTH WEIRS
Author(s) -
Norouzi Payam,
Rajabi Ahmad,
Izadbakhsh Mohammad Ali,
Shabanlou Saeid,
Yosefvand Fariborz,
Yaghoubi Behrouz
Publication year - 2020
Publication title -
irrigation and drainage
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.421
H-Index - 38
eISSN - 1531-0361
pISSN - 1531-0353
DOI - 10.1002/ird.2423
Subject(s) - weir , discharge coefficient , sigmoid function , crest , mathematics , sensitivity (control systems) , orientation (vector space) , geometry , artificial intelligence , computer science , engineering , physics , artificial neural network , thermodynamics , optics , geography , cartography , electronic engineering , nozzle
In this study, the labyrinth weir discharge coefficient was simulated using the self‐adaptive extreme learning machine (SAELM) artificial intelligence model in two cases: normal orientation labyrinth weirs (NLWs) and inverted orientation labyrinth weirs (ILWs). First, the most optimized neuron of the hidden layer was computed. The number of hidden layer neurons was calculated as 30. Also, by analysing the results of different activation functions, it was concluded that the sigmoid activation function has higher accuracy than the others. Next, the superior model was identified by conducting a sensitivity analysis. The model approximated the discharge coefficient of labyrinth weirs with reasonable accuracy. For example, the R 2 , scatter index and Nash–Sutcliffe efficiency coefficient for the best model were estimated as 0.966, 0.034 and 0.964, respectively. In addition, the ratio of the total head above the weir to the height of the weir crest ( H T / P ) and the ratio of length of apex geometry to width of a single cycle ( A / w ) were identified as the most effective parameters. Furthermore, the uncertainty analysis results indicated that the superior model had an overestimated performance. Then, a relationship was proposed in terms of all input variables for the superior model. © 2020 John Wiley & Sons, Ltd.