关键词: 短期径流预报, 递归神经网络, 预报因子组合, 海岛, 水库

Abstract: Accuracy of short-term runoff forecasting is often lowered due to low or even zero river flows in island areas. This study adopts three different recurrent neural networks (RNNs) to forecast several runoff series at different lead times and input combinations. Application to a case study of the Zhoushan Island shows that runoff forecasting coupled with future meteorological forecasting information achieves better performance than that based on runoff information only. As forecasting lead time increases, the long short-term memory network and gated recurrent unit are becoming better than the simple RNN model. RNN models perform better on stationary runoff series than non-stationary ones, and their stability and reliability in calculation of non-stationary runoff series can be improved by coupling meteorological information and through parameter optimization.

Key words: short-term runoff forecast, recurrent neural networks, input combinations, island, reservoir