Multi-step-ahead flood forecasting using an improved BiLSTM-S2S model

Abstract

Rainfall–runoff modeling is a complex hydrological issue that still has room for improvement. This study developed a coupled bidirectional long short-term memory (LSTM) with sequence-to-sequence (Seq2Seq) learning (BiLSTM-Seq2seq) model to simulate multi-step-ahead runoff for flood events. The bidirectional LSTM with Seq2Seq learning (LSTM-Seq2Seq) and multilayer perceptron (MLP) was set as benchmarks. The results show that: (1) root mean absolute error is reduced by approximately 19% up to 27%, and the Nash–Sutcliffe coefficient of efficiency is improved by 14% up to 34% for 6-h-ahead runoff prediction for BiLSTM-Seq2Seq compared LSTM-Seq2Seq and MLP; (2) The BiLSTM-Seq2Seq model has good performance not only for one-peak flood events but also for multi-peak flood events; and (3) BiLSTM-Seq2Seq can mitigate the time-delay problem and time lag is shortened by 39% up to 69% in comparison to LSTM-Seq2Seq and MLP. These results suggest that the time-delay problem can be mitigated by BiLSTM-Seq2Seq, which has excellent potential in time series predictions in the hydrological field.