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首页> 外文期刊>Journal of Hydroinformatics >Prediction of time variation of scour depth around spur dikes using neural networks
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Prediction of time variation of scour depth around spur dikes using neural networks

机译：利用神经网络预测丁坝周围冲刷深度的时间变化

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摘要

The maximum depth of scouring around spur dikes plays an important role in the hydraulic design process. There have been many studies on the maximum depth of scouring, but there is little information available on the time variation of scour depth. In this paper, the time variation of scouring around the first spur dike in a series was investigated experimentally. Experiments were carried out in four different bed materials under different flow intensities (U/U_(cr)). To achieve a time development of scouring around the first spur dike, more than 750 sets of experimental data were collected. The results showed that 70-90% of the equilibrium scour depths were occurring during the initial 20% of the overall time of scouring. Based on the data analysis, a regression model and artificial neural networks (ANNS) were developed. The models were compared with other empirical equations in the literature. However, the results showed that the developed regression model is quite accurate and more practical, but the ANN models by feed forward back propagation and radial basis function provide a better prediction of observation. Finally, by sensitivity analysis, the most and the least effective parameters, which affected time variation of scouring, were determined.
机译：丁坝周围的最大冲刷深度在水力设计过程中起着重要作用。关于冲刷最大深度的研究很多，但是关于冲刷深度随时间变化的信息很少。本文通过实验研究了第一个丁坝周围冲刷的时间变化。在四种不同的床层材料上以不同的流动强度（U / U_（cr））进行了实验。为了实现在第一个丁坝附近进行冲刷的时间发展，收集了超过750套实验数据。结果表明，在整个冲刷时间的最初20％期间，发生了70-90％的平衡冲刷深度。在数据分析的基础上，建立了回归模型和人工神经网络。将模型与文献中的其他经验方程式进行了比较。然而，结果表明，所开发的回归模型是相当准确和实用的，但是通过前馈传播和径向基函数的神经网络模型提供了更好的观测预测。最后，通过敏感性分析，确定了影响精练时间变化的最大和最小有效参数。

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