Accurate and reliable runoff forecasting plays an increasingly important role in the optimal management of water resources. To improve the prediction accuracy, a hybrid model based on variational mode decomposition (VMD) and deep neural networks (DNN), referred to as VMD-DNN, is proposed to perform daily runoff forecasting. First, VMD is applied to decompose the original runoff series into multiple intrinsic mode functions (IMFs), each with a relatively local frequency range. Second, predicted models of decomposed IMFs are established by learning the deep feature values of the DNN. Finally, the ensemble forecasting result is formulated by summing the prediction sub-results of the modelled IMFs. The proposed model is demonstrated using daily runoff series data from the Zhangjiashan Hydrological Station in Jing River, China. To fully illustrate the feasibility and superiority of this approach, the VMD-DNN hybrid model was compared with EMD-DNN, EEMD-DNN, and multi-scale feature extraction -based VMD-DNN, EMD-DNN and EEMD-DNN. The results reveal that the proposed hybrid VMD-DNN model produces the best performance based on the Nash-Sutcliffe efficiency (NSE?=?0.95), root mean square error (RMSE?=?9.92) and mean absolute error (MAE?=?3.82) values. Thus the proposed hybrid VMD-DNN model is a promising new method for daily runoff forecasting.
相似文献Reasonable runoff forecasting is the foundation of water resource management. However, the impact of environmental change on streamflow was not fully revealed due to the lack of enough streamflow features in many previous studies. In contrast, too many features also could lead cause undesired problems, including unstable model, interpretation difficulty, overfitting, high computational complexity, and high memory complexity. To address the above problems, this study proposes a cause-driven runoff forecasting framework based on linear-correlated reconstruction and machine learning model and refers to this framework as CSLM. We use variance inflation factor (VIF), pairwise linear correlation (PLC) reconstruction, and long short-term memory (LSTM) to realize this framework, referred to as VIF-PLC-LSTM. Four experiments were conducted to demonstrate the accuracy and efficiency of the proposed framework and its VIF-PLC-LSTM realization. Four experiments compare 1) different filter thresholds of driving factors, 2) different combination prediction features, 3) different reconstruction methods of linear-correlated features, and 4) different CSLM models. Experimental results on daily streamflow data from the Tangnaihai station at the Yellow River source and the Yangxian station at the Han River show that 1) data filtering has the risk of feature information loss, 2) when the streamflow, ERA5L, and meteorology data are used as inputs at the same time, the performance of the model is superior to the combination of other prediction features; the prediction effect of different prediction features, 3) the reconstruction of linear-correlated features is not only better than dimension reduction but also can improve the forecasting performance for streamflow prediction, and 4) among different CSLM models, LSTM is superior to other models.
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