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.