School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China; Hubei Key Laboratory of Digital Valley Science and Technology, Wuhan 430074, China.
School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China; Hubei Key Laboratory of Digital Valley Science and Technology, Wuhan 430074, China.
Sci Total Environ. 2022 Dec 10;851(Pt 2):158342. doi: 10.1016/j.scitotenv.2022.158342. Epub 2022 Aug 26.
Predicting river runoff accurately is of substantial significance for flood control, water resource allocation, and basin ecological dispatching. To explore the reasonable and effective application of time series decomposition in runoff forecasting, this study proposed a novel stepwise decomposition-integration-prediction considering boundary correction (SDIPBC) framework by using the stepwise decomposition sampling method and multi-input neural network. On this basis, we implemented a hybrid forecasting model combining seasonal-trend decomposition procedures based on loess (STL) with the long short-term memory (LSTM) network called STL-LSTM (SDIPBC) to estimate mid-long term river runoff. The reliability of the method was assessed using the historical runoff series of the Lianghekou and Jinping I Reservoirs in the Yalong River Basin, China, and developed several single models and hybrid models for comparative experiments. The results show that the existing decomposition-based hybrid forecasting frameworks are not suitable for practical runoff forecasting. The proposed SDIPBC framework can avoid using future information and improve the prediction accuracy of the single prediction model. For the Nash-Sutcliffe efficiency coefficient (NSE), the ten-day runoff forecasting accuracy of STL-LSTM (SDIPBC) in Lianghekou reservoir and Jinping I Reservoirs reached 0.845 and 0.862 respectively, which improved 1.81 % and 2.38 % than the single LSTM model, indicating that this is a practical and reliable decomposition-based hybrid runoff forecasting method.
准确预测河流径流量对防洪、水资源调配和流域生态调度具有重要意义。为探索时间序列分解在径流预测中的合理有效应用，本研究提出了一种考虑边界修正的分步分解-集成-预测新框架（SDIPBC），该框架采用逐步分解抽样方法和多输入神经网络。在此基础上，我们构建了一个混合预测模型，将基于局部均值分解（Loess）的季节性趋势分解程序（STL）与长短时记忆（LSTM）网络相结合，称为 STL-LSTM（SDIPBC），以估计中长期河流径流量。该方法的可靠性通过中国雅砻江流域两河口和锦屏一级水库的历史径流量序列进行评估，并开发了几种单模型和混合模型进行对比实验。结果表明，现有的基于分解的混合预测框架不适合实际径流预测。所提出的 SDIPBC 框架可以避免使用未来信息，提高单预测模型的预测精度。对于纳什-苏特克里夫效率系数（NSE），在两河口水库和锦屏一级水库中，STL-LSTM（SDIPBC）的十天径流预测精度分别达到 0.845 和 0.862，分别比单个 LSTM 模型提高了 1.81%和 2.38%，表明这是一种实用且可靠的基于分解的混合径流预测方法。
