Modeling and prediction of bed loads is an important but difficult issue in river engineering. The introduced empirical equations due to restricted applicability even in similar conditions provide different accuracies with each other and measured data. In this paper, three different artificial neural networks (ANNs) including multilayer percepterons, radial based function (RBF), and generalized feed forward neural network using five dominant parameters of bed load transport formulas for the Main Fork Red River in Idaho-USA were developed. The optimum models were found through 102 data sets of flow discharge, flow velocity, water surface slopes, flow depth, and mean grain size. The deficiency of empirical equations for this river by conducted comparison between measured and predicted values was approved where the ANN models presented more consistence and closer estimation to observed data. The coefficient of determination between measured and predicted values for empirical equations varied from 0.10 to 0.21 against the 0.93 to 0.98 in ANN models. The accuracy performance of all models was evaluated and interpreted using different statistical error criteria, analytical graphs and confusion matrixes. Although the ANN models predicted compatible outputs but the RBF with 79% correct classification rate corresponding to 0.191 network error was outperform than others.

中文翻译：

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使用不同的人工神经网络模型预测床荷沉积物

河床荷载的建模和预测是河流工程中一个重要但困难的问题。即使在相似的条件下，由于适用性的限制，引入的经验方程式也提供了彼此不同的精度和测量数据。本文开发了三种不同的人工神经网络（ANN），包括多层感知器，径向基函数（RBF）和广义前馈神经网络，该网络使用了五个主要参数在美国爱达荷州的主叉红河床荷载输送公式中。通过流量，流量，水面坡度，流量深度和平均粒度的102个数据集找到了最佳模型。通过在测量值和预测值之间进行比较，对该河流的经验方程式的缺陷得到了认可，其中ANN模型对观测数据显示出更多的一致性和更接近的估计。经验方程的测量值和预测值之间的确定系数从0.10到0.21相对于ANN模型中的0.93到0.98有所不同。使用不同的统计误差标准，分析图和混淆矩阵对所有模型的准确性进行了评估和解释。尽管人工神经网络模型预测了可兼容的输出，但是具有79％正确分类率（对应于0.191网络错误）的RBF优于其他模型。相对于ANN模型中的0.93至0.98，则为21。使用不同的统计误差标准，分析图和混淆矩阵对所有模型的准确性进行了评估和解释。尽管人工神经网络模型预测了可兼容的输出，但是具有79％正确分类率（对应于0.191网络错误）的RBF优于其他模型。相对于ANN模型中的0.93至0.98，则为21。使用不同的统计误差标准，分析图和混淆矩阵对所有模型的准确性进行评估和解释。尽管人工神经网络模型预测了可兼容的输出，但是具有79％正确分类率（对应于0.191网络错误）的RBF优于其他模型。