基于变分模态分解和深度门控网络的径流预测

为提高水库中长期入库径流预测精度，提出变分模态分解、相空间重构和深度门控网络相结合的径流组合预测模型。首先对历史径流数据进行变分模态分解，产生多个模态分量；接着将分解得到的模态分量重构到高维特征空间，形成深度学习的输入；然后利用深度门控网络获取历史径流详细特征并进行预测；最后累加各模态分量的预测值完成重构。以白山水库为例，将所建模型分别与单一预测模型和其他组合预测模型进行对比分析。结果表明：所建模型能有效分解非平稳性的径流序列，充分学习内嵌的水文规律，预测误差最小，且在整个测试集上分布更为合理，拟合优度检验值最高。研究结果可为水库水资源规划管理提供技术依据。

Runoff prediction based on variational mode decomposition and deep gated network

To improve the accuracy in medium- and long-term prediction of reservoir runoff, this paper develops a combined prediction model integrating variational mode decomposition, phase space reconstruction, and a deep gated network. For a series of historical runoff data, this model first generates its multiple mode components using variational mode decomposition, and uses phase space reconstruction to reconstruct the components into a high-dimensional feature space to generate inputs for deep learning. Then, detailed characteristics of the series are obtained and predicted using a deep gated network, and the reconstruction is completed by superimposing the predicted values of the mode components. In a case study of the Baishan reservoir, this combined prediction model is compared with typical single prediction models and other combined prediction models. The results show that our method can effectively decompose non-stationary runoff series, learn the intrinsic hydrological patterns, and offer the smallest prediction error, the highest goodness of fit, and more reasonable error distributions over the whole test set, thus helping the planning and management of reservoir water resources.