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首页> 外文期刊>Australian journal of water resources >Applicability of artificial neural network in hydraulic experiments using a new sewer overflow screening device
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Applicability of artificial neural network in hydraulic experiments using a new sewer overflow screening device

机译：人工神经网络在新型下水道溢流筛分装置在水力实验中的适用性

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摘要

During wet weather conditions, sewer overflows to receiving water bodies raise serious environmental, aesthetic and public health problems. These issues trigger the need the most appropriate device/system for a particular installation, especially at unmanned remote locations. A new sewer overflow device consists of a rectangular tank and a sharp crested weir with a series of vertical combs is presented. A series of laboratory tests to determine trapping efficiencies for common sewer solids were conducted for different flow conditions, number of combs layers and spacing of combs. To overcome physical limitations inherent in laboratory studies such as significant cost and time. Artificial neural model was adopted as it has the capacity to accurately predict the outcome of complex, non-linear physical systems with relatively poorly understood physicochemical processes. A series of laboratory tests were conducted with 55 different sets of data. Forty-seven sets of experimental data are used with 60% for training, 20% each for testing and validation of the model. A separate validation data sets were used to judge the overall performance of the trained network. The model can successfully predict the experimental results with more than 90% accuracy with an average absolute percentage error of around 7%.
机译：在潮湿的天气条件下，下水道溢出到接收水体会引起严重的环境，美学和公共卫生问题。这些问题触发了针对特定安装（尤其是在无人值守的远程位置）最合适的设备/系统的需求。提出了一种新的下水道溢流装置，该装置由矩形水箱和带有一系列垂直梳的尖顶堰组成。针对不同的流量条件，梳子层数和梳子间距，进行了一系列实验室测试，以确定常见下水道固体的捕集效率。克服实验室研究固有的物理限制，例如大量的成本和时间。采用了人工神经模型，因为它具有准确预测物理化学过程相对较差的复杂，非线性物理系统的结果的能力。使用55组不同的数据进行了一系列实验室测试。使用47组实验数据，其中60％用于训练，每组20％用于测试和验证模型。单独的验证数据集用于判断受训网络的整体性能。该模型可以成功地以90％以上的准确度预测实验结果，平均绝对百分比误差约为7％。

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