Laboratory for Operation and Control of Cascaded Hydropower Station, China Three Gorges University, Yichang 44302, China

Department of Economics, National University of Modern Languages, Islamabad 44000, Pakistan

流量预测的准确性和一致性在涉及水文资源管理（例如发电，供水和防洪）的几种应用中起着重要作用。然而，气候因素的非线性动力学危害了有效预测模型的发展。因此，为了提高流量预测的可靠性和准确性，本文开发了一种三阶段混合模型，即IVL（ICEEMDAN-VMD-LSTM），该模型将改进的完整整体经验模式分解与加性噪声（ICEEMDAN），变分模式集成在一起分解（VMD）和长短期记忆（LSTM）神经网络。以巴基斯坦斯瓦特河集水区从1971年1月至2015年12月的流量，温度和降水量每月数据系列为例。首先，相关分析和两阶段分解方法被用来为该模型选择合适的输入。ICEEMDAN被用作第一个分解阶段，将三个数据序列分解为固有模式函数（IMF）和残差分量。在第二分解阶段中，VMD将高频成分（IMF1）分解为第二分解。然后，使用LSTM网络预测通过校正分析和两步分解方法获得的所有成分。最后，汇总所有组成部分的预测结果，以对原始的每月流量序列进行整体预测。预测结果表明，所提出模型的性能优于其他已开发模型， The accuracy and consistency of streamflow prediction play a significant role in several applications involving the management of hydrological resources, such as power generation, water supply, and flood mitigation. However, the nonlinear dynamics of the climatic factors jeopardize the development of efficient prediction models. Therefore, to enhance the reliability and accuracy of streamflow prediction, this paper developed a three-stage hybrid model, namely, IVL (ICEEMDAN-VMD-LSTM), which integrated improved complete ensemble empirical mode decomposition with additive noise (ICEEMDAN), variational mode decomposition (VMD), and long short-term memory (LSTM) neural network. Monthly data series of streamflow, temperature, and precipitation in the Swat River Watershed, Pakistan, from January 1971 to December 2015 was used as a case study. Firstly, the correlation analysis and the two-stage decomposition approach were employed to select suitable inputs for the proposed model. ICEEMDAN was employed as a first decomposition stage, to decompose the three data series into intrinsic mode functions (IMFs) and a residual component. In the second decomposition stage, the component of high frequency (IMF1) was decomposed by VMD, as the second decomposition. Afterward, all the components obtained through the correction analysis and the two-stage decomposition approach were predicted by using the LSTM network. Finally, the predicted results of all components were aggregated, to formulate an ensemble prediction for the original monthly streamflow series. The predicted results showed that the performance of the proposed model was superior to the other developed models, in respect of several evaluation benchmarks, demonstrating the applicability of the proposed IVL model for monthly streamflow prediction.