改进EEMD-GRU混合模型在径流预报中的应用

为解决径流预测模型存在的预测精确度低、稳定性差、延时高等问题，结合门控制循环单元神经网络（gated recurrent unit, GRU）,集合经验模态分解（ensemble empirical mode decomposition, EEMD）的各自优点，提出一种基于改进EEMD方法的深度学习模型（EEMD-GRU）。该模型首先以智能算法对径流信号进行边界拓延，以解决EEMD边界效应。然后利用改进EEMD方法将径流信号分解为若干稳态分量，将各分量作为GRU模型的输入并对其进行预测。实验结果表明，与结合了经验模态分解的支持向量回归模型相比，并行EEMD-GRU径流预测模型的预测精准度、可信度和效率分别提高82.50%、144.67%和95.49%。基于EEMD-GRU的最优运算结果表明，该方法可进一步减少区域防洪的经济损失，提高灾害监管的工作效率。

Application of improved EMD-GRU hybrid model in runoff forecasting

Runoff prediction models suffer from low prediction accuracy, poor stability, and high delay. Combined with the advantages of gate control recurrent unit (GRU) neural network and ensemble empirical mode decomposition (EEMD), we propose a deep learning model based on the improved EEMD method (EEMD-GRU) to solve the aforementioned problems of runoff models. Our model initially uses an intelligent algorithm to extend the boundary of the runoff signal to solve the EEMD boundary effect. Subsequently, the improved EEMD method is used to decompose the runoff signals into numerous steady-state components. Each component is then used as the input in the GRU model and is predicted. The experimental results show that the prediction accuracy, credibility, and efficiency of the parallel EEMD-GRU runoff prediction model improve by 82.50%, 144.67%, and 95.49%, respectively, in comparison to the support vector regression model combined with empirical mode decomposition. The optimal calculation results based on EEMD-GRU show that this method can further reduce the economic loss of regional flood control and improve the efficiency of disaster supervision.