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首页> 外文期刊>Journal of Hydroinformatics >Development of a discharge equation for side weirs using artificial neural networks
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Development of a discharge equation for side weirs using artificial neural networks

机译：使用人工神经网络开发侧堰的排水方程

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摘要

Flow over a side weir is one of the more complex flows to simulate in one-dimensional unsteady flow analysis, various experiments have been applied, but no agreement is apparent in the literature about the best method. In this study, an Artificial Neural Network model has been used to extract a discharge equation for side weirs which accurately estimates overflow discharges. The proposed methodology gives the advantage of accounting for both the geometric and hydraulic characteristics of the overflow structure. The developed model is calibrated and validated using experimental data. Model calibration is achieved by using a Multi-Layer Perceptron (MLP), trained with the back-propagation algorithm, in order to highlight the advantage of the developed model over an existing model widely in use, the model's performance is evaluated according to three comparison criteria. The provided results clearly reflect the ability of the developed model to overcome the weakness of conventional models.
机译：在一维非定常流动分析中，流经侧堰的流动是较复杂的流动之一，已经进行了各种实验，但是关于最佳方法的文献没有明显的共识。在这项研究中，人工神经网络模型已用于提取侧堰的排放方程，该方程可精确估算溢流流量。所提出的方法具有考虑溢流结构的几何和水力特性的优点。使用实验数据对开发的模型进行校准和验证。通过使用经过反向传播算法训练的多层感知器（MLP）来实现模型校准，以突出开发的模型相对于广泛使用的现有模型的优势，根据三个比较来评估模型的性能标准。提供的结果清楚地反映了开发模型克服常规模型的弱点的能力。

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