Journal Article

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

Zhu, Feilin, Zhang, Hanchen, Cao, Qing, Hao, Zhenchun, Yuan, Feifei

2022

Overview

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.

• Publisher: Blackwell Publishing Ltd,John Wiley & Sons, Inc,Wiley
• Subject: Benchmarks / bidirectional long short‐term memory / Error reduction / Flood control / Flood forecasting / flood modeling / flood risk / Floods / Hydrology / Learning / Modelling / Multilayer perceptrons / multi‐step‐ahead runoff forecast / neural networks / Peak floods / prediction / Rainfall / Rainfall runoff / Rainfall-runoff modeling / Rainfall-runoff relationships / Response time / risk management / River discharge / Runoff / sequence‐to‐sequence learning / Sequencing / simulation models / Time lag / time series analysis