利用人工神经网络确定雨水渠淤积极限泥沙浓度

IF 0.9 Q4 WATER RESOURCES
Adhemar Romero, J. Ota
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引用次数: 0

摘要

雨水渠中沉积极限泥沙输运的概念代表了一种操作条件,即避免沉积物沉积,保持管道的排放能力。本研究利用文献中的544个实验数据,应用人工神经网络多层感知器(ANN-MLP)模型预测沉积极限时的体积浓度,对该条件进行了分析。对不同的输入变量组合和模型配置进行了评估,显示了模型对这些变化的敏感性。通过本研究,证明了所提出的模型优于现有方程,在沉积极限下的体积浓度测定中给出了更自信的预测,结果R2 = 0.92,平均绝对百分比误差(MAPE) = 35.09%,平均平均误差(MAE) = 59.84 ppm。通过所进行的分析,研究选择了一个方程用于外推,以确定在暴雨下水道中沉积极限的体积浓度。所选方程具有较好的理论基础。这项工作包括了一个更好的方法来获得在沉积极限的流动条件的概念。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using artificial neural networks to determine sediment concentration at limit of deposition in storm sewers
ABSTRACT The concept of sediment transport at the limit of deposition in storm sewers represents one operational condition that avoid deposition of sediments maintaining the discharge capacity of the pipes. In this study, this condition was analyzed applying one Artificial Neural Network Multilayer Perceptron (ANN-MLP) model to predict the volumetric concentration at the limit of deposition, using 544 experimental data from literature. It was evaluated different input variables combinations and model configurations, showing the sensitivity of the model with these changes. Through this study, it was demonstrated that the proposed model outperforms the existing equations, leading to more assertive predictions in the determination of volumetric concentrations at the limit of deposition, resulting in values of R2 = 0.92, Mean Absolute Percentage Error (MAPE) = 35.09 % and Mean Average Error (MAE) = 59.84 ppm. With the performed analysis, the study selects one equation to be used for extrapolations when determining the volumetric concentration at the limit of deposition in storm sewers. The selected equation is superior due to its theoretical basis. This work includes one more concept to a better methodology in obtaining the conditions of the flow at the limit of deposition.
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来源期刊
CiteScore
1.60
自引率
12.50%
发文量
18
审稿时长
16 weeks
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