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Discharge coefficient of oblique sharp crested weir for free and submerged flow using trained ANN model

机译:倾斜尖顶堰自由流和淹没流的排放系数的训练神经网络模型

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In the present study, ANN models have been developed to predict the discharge coefficients of oblique sharp-crested weirs for free and submerged flow cases using Borghei et al.’s experimental data. The discharge coefficients predicted by ANN models are then used to predict the discharges. The results so obtained are compared with the traditional regression model analysis performed by Borghei et al. (2003) in which the prediction error in the discharge was found within the range of ±5%. On the other hand, the developed ANN models predict the discharge coefficients as well as discharges within the error range of ±1%. Furthermore, sensitivity analysis of developed ANN models have been carried out for all the parameters (weir height, oblique weir length, head over weir and downstream head over weir) involved in the study and it was found that the weir length (L) is the most and weir height (P) is the least sensitive input variable toANN-1model. In the case ofANN-2model, weir length (L) is the most and downstream head over weir (Hd) is the least sensitive input variable.
机译:在本研究中,已经使用Borghei等人的实验数据开发了ANN模型,以预测自由流动和淹没流动情况下斜尖顶堰的排放系数。然后将由ANN模型预测的放电系数用于预测放电。将如此获得的结果与Borghei等人进行的传统回归模型分析进行比较。 (2003),其中放电的预测误差被发现在±5%的范围内。另一方面,已开发的ANN模型可预测放电系数以及误差在±1%范围内的放电。此外,已经对研究涉及的所有参数(堰高,斜堰长度,堰顶和下游堰顶)进行了已开发的ANN模型的灵敏度分析,发现堰长(L)为堰高(P)最大和最小是对ANN​​-1模型的最不敏感输入变量。在ANN-2模型的情况下,堰长(L)是最大的,下游头顶堰(Hd)是最不敏感的输入变量。

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