Particle swarm optimization feedforward neural network for modeling runoff
Particle swarm optimization feedforward neural network for modeling runoff
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DOI：
10.1007/bf03326118
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发表时间：
2010-12-01
影响因子：
3.1
通讯作者：
Shamsuddin, S. M.
中科院分区：
文献类型：
--
作者：
Kuok, K. K.;Harun, S.;Shamsuddin, S. M.
径流-径流关系是最复杂的水文现象之一。近年来，水文学家已经成功地应用反向传播神经网络作为一种工具，模拟各种非线性水文过程，因为它能够概括模式的不精确或噪音和模糊的输入和输出数据集。然而，反向传播神经网络的收敛速度相对较慢，解决方案可能会陷入局部极小值。因此，在这项研究中，提出了一种新的进化算法，即粒子群优化算法来训练前馈神经网络。该粒子群优化前向神经网络应用于马来西亚沙捞越双溪Bedup流域的日径流量-径流量关系建模。模型性能的测量使用的相关系数和纳什-萨克利夫系数。模型的输入数据为当前降雨量、前期降雨量和前期径流量，输出数据为当前径流量。粒子群优化前向神经网络对当前径流的模拟精度较高，训练数据集的R = 0.872，E-2 = 0.775，测试数据集的R = 0.900，E-2 = 0.807。因此，粒子群优化前馈神经网络方法可以成功地应用于贝杜卜流域的径流-径流关系建模，并可推广到其他流域。
The rainfall-runoff relationship is one of the most complex hydrological phenomena. In recent years, hydrologists have successfully applied backpropagation neural network as a tool to model various nonlinear hydrological processes because of its ability to generalize patterns in imprecise or noisy and ambiguous input and output data sets. However, the backpropagation neural network convergence rate is relatively slow and solutions can be trapped at local minima. Hence, in this study, a new evolutionary algorithm, namely, particle swarm optimization is proposed to train the feedforward neural network. This particle swarm optimization feedforward neural network is applied to model the daily rainfall-runoff relationship in Sungai Bedup Basin, Sarawak, Malaysia. The model performance is measured using the coefficient of correlation and the Nash-Sutcliffe coefficient. The input data to the model are current rainfall, antecedent rainfall and antecedent runoff, while the output is current runoff. Particle swarm optimization feedforward neural network simulated the current runoff accurately with R = 0.872 and E-2 = 0.775 for the training data set and R = 0.900 and E-2 = 0.807 for testing data set. Thus, it can be concluded that the particle swarm optimization feedforward neural network method can be successfully used to model the rainfall-runoff relationship in Bedup Basin and it could be to be applied to other basins.