Mid-Long Term Inflow Forecasting Using the J-T Test and Long Short-Term Memory Considering Monthly factors

Mid-long term inflow forecasting plays an important supporting role in reservoir production planning, drought and flood control, comprehensive utilization and water resource management. Although the inflow data have some periodicity and predictability characteristics, the inflow sequence has complex nonlinearity due to the comprehensive influence of climate, underlying surfaces, human activities and other factors. Therefore, it is difficult to achieve accurate inflow forecasting. In this study, a new hybrid inflow forecast framework that uses previous inflows and monthly factors as inputs, and that adopts Long Short-Term Memory (LSTM) and the Jonckheere-Terpstra test (J-T test) is developed for mid-long term inflow forecasting. First, the J-T test can test whether the monthly average inflow sequence set exhibits significant differences due to climate, underlying surfaces, human activities and other factors to ensure the effectiveness of the framework. Second, the LSTM, which is good at determining the nonlinearity law of the time sequence and finding the best solution, is chosen as the framework algorithm. Finally, due to the periodicity of the inflow sequence, adding monthly factors into the framework can provide more information for the framework to improve the accuracy of the forecast. Xiaowan Hydropower Station in the Lancang River of China is selected as the research area. Six evaluation criteria are used to evaluate established framework using historical monthly inflow data (January 1954-December 2016). The performance of the framework is compared with that of the Back Propagation Neural Network (BPNN) and Support Vector Regression (SVR) models. The results show that the introduction of monthly factors greatly improves the accuracy of the inflow forecast studied, and the proposed method is also better than other frameworks.