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 R², 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.

中文翻译：

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一种模拟拉伯林斯·韦尔斯的排放系数的新的非调整式自适应机器学习方法

在本研究中，在两种情况下，使用自适应极限学习机（SAELM）人工智能模型模拟了迷宫堰溢流系数：法向迷宫堰（NLWs）和倒置迷宫堰（ILWs）。首先，计算隐藏层的最优化神经元。隐层神经元的数量计算为30。此外，通过分析不同激活函数的结果，可以得出结论，乙状结肠激活函数的准确性比其他函数高。接下来，通过进行敏感性分析来确定上级模型。该模型以合理的精度近似迷宫堰的排放系数。例如R ²，最佳模型的散射指数和Nash-Sutcliffe效率系数分别估计为0.966、0.034和0.964。另外，最有效的方法是将堰顶上方的总水头与堰顶的高度之比（H _T / P）以及顶点几何形状的长度与单个循环宽度之比（A / w）确定为最有效的方法。参数。此外，不确定性分析结果表明，高级模型的性能被高估了。然后，针对上级模型的所有输入变量提出了一种关系。分级为4 +©2020 John Wiley＆Sons，Ltd.