The importance of short lag-time in the runoff forecasting model based on long short-term memory

By Xi Chen, Jiaxu Huang, Zhen Han, Hongkai Gao, Min Liu, Zhiqiang Li, Xiaoping Liu, Qingli Li, Honggang Qi, Yonggui Huang. 2020

81 citations recorded by the source corpus.

Abstract It is still very challenging to enhance the accuracy and stability of daily runoff forecasts, especially several days ahead, owing to the non-linearity of the forecasted processes. Here, we hypothesize that short lag-time has a significant impact on forecasting results. Thus, we incorporate short previous time steps into long short-term memory (LSTM) and develop the Self-Attentive Long Short-Term Memory (SA-LSTM). In SA-LSTM, the self-attention mechanism is used to model interdependencies within short previous time steps. SA-LSTM is evaluated at eight runoff datasets. The experimental results demonstrate that, compared with state-of-art benchmark models, SA-LSTM achieves the best performance. The RMSEs of SA-LSTM are at least 2.3% smaller than that of the second best model at the seventh day. The NSEs and NSE_In of SA-LSTM are at least 4.6% and 6.4% higher than those of the second best model at the seventh day. Furthermore, SA-LSTM also surpasses the baseline methods for base, mean and peak flows. The superiority of SA-LSTM can be attributed to its exploitation of information in short lag-time.

Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/7473199e0111a4133b0fec0dfbb56d56b9028b11