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首页> 外文期刊>Journal of Hydraulic Engineering >Predicting Longitudinal Dispersion Coefficient in Natural Streams by Artificial Neural Network
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Predicting Longitudinal Dispersion Coefficient in Natural Streams by Artificial Neural Network

机译：用人工神经网络预测自然流中的纵向弥散系数。

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An artificial neural network (ANN) model was developed to predict the longitudinal dispersion coefficient in natural streams and rivers. The hydraulic variables [flow discharge (Q), flow depth (H), flow velocity (U), shear velocity (u~*), and relative shear velocity (U/u~*)] and geometric characteristics [channel width (B), channel sinuosity (σ), and channel shape parameter (β)] constituted inputs to the ANN model, whereas the dispersion coefficient (K_x) was the target model output. The model was trained and tested using 71 data sets of hydraulic and geometric parameters and dispersion coefficients measured on 29 streams and rivers in the United States. The training of the ANN model was accomplished with an explained variance of 90% of the dispersion coefficient. The dispersion coefficient values predicted by the ANN model satisfactorily compared with the measured values corresponding to different hydraulic and geometric characteristics. The predicted values were also compared with those predicted using several equations that have been suggested in the literature and it was found that the ANN model was superior in predicting the dispersion coefficient. The results of sensitivity analysis indicated that the Q data alone would be sufficient for predicting more frequently occurring low values of the dispersion coefficient (K_x < 100 m~2/s). For narrower channels (B/H < 50) using only U/u~* data would be sufficient to predict the coefficient. If β and σ were used along with the flow variables, the prediction capability of the ANN model would be significantly improved.
机译：建立了人工神经网络（ANN）模型来预测自然河流和河流中的纵向弥散系数。水力变量[流量排放（Q），流量深度（H），流量（U），剪切速度（u〜*）和相对剪切速度（U / u〜*）]和几何特性[通道宽度（B ），通道弯曲度（σ）和通道形状参数（β）]构成了ANN模型的输入，而弥散系数（K_x）是目标模型的输出。使用71个水力和几何参数数据集以及在美国29条河流和河流上测得的弥散系数对模型进行了训练和测试。 ANN模型的训练是在90％的分散系数方差下完成的。由ANN模型预测的色散系数值与对应于不同水力和几何特性的测量值令人满意地比较。还将预测值与使用文献中提出的几个方程式预测的值进行了比较，发现ANN模型在预测色散系数方面表现优异。灵敏度分析的结果表明，仅Q数据就足以预测更频繁出现的色散系数的低值（K_x <100 m〜2 / s）。对于较窄的信道（B / H <50），仅使用U / u *数据就足以预测系数。如果将β和σ与流量变量一起使用，则ANN模型的预测能力将大大提高。

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