Fluctuant and complicated hydrological processes can result in the uncertainty of runoff forecasting. Thus, it is necessary to apply the multi-method integrated modeling approaches to simulate runoff. Integrating the ensemble empirical mode decomposition?(EEMD), the back-propagation artificial neural network?(BPANN) and the nonlinear regression equation, we put forward a hybrid model to simulate the annual runoff?(AR) of the Kaidu River in northwest China. We also validate the simulated effects by using the coefficient of determination?(iR/isup2/sup) and the Akaike information criterion?(AIC) based on the observed data from?1960 to?2012 at the Dashankou hydrological station. The average absolute and relative errors show the high simulation accuracy of the hybrid model. iR/isup2/sup and AIC both illustrate that the hybrid model has a much better performance than the single BPANN. The hybrid model and integrated approach elicited by this study can be applied to simulate the annual runoff of similar rivers in northwest China.
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机译：波动和复杂的水文过程可能导致径流预报的不确定性。因此，有必要应用多方法集成建模方法来模拟径流。结合集成经验模型分解（EEMD），反向传播人工神经网络（BPANN）和非线性回归方程，提出了一种混合模型来模拟西北开都河的年径流量（AR）。 。我们还基于从1960到2005年的观测数据，使用确定系数（ R 2 ）和Akaike信息准则（AIC）验证了模拟效果。 2012年在大山口水文站。平均绝对和相对误差显示了混合模型的高仿真精度。 R 2 和AIC都表明，混合模型的性能要比单个BPANN好得多。这项研究得出的混合模型和综合方法可用于模拟中国西北地区类似河流的年径流量。
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