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

Cao, Qing ; Zhang, Hanchen ; Zhu, Feilin ; Hao, Zhenchun and Yuan, Feifei LU (2022) In Journal of Flood Risk Management 15(4).
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... (More)
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. (Less)
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author
; ; ; and
publishing date
type
Contribution to journal
publication status
published
subject
in
Journal of Flood Risk Management
volume
15
issue
4
publisher
Blackwell Publishing
external identifiers
  • scopus:85131730131
ISSN
1753-318X
DOI
10.1111/jfr3.12827
language
English
LU publication?
no
id
5ca7384c-6b87-4540-8b04-a368744216a9
date added to LUP
2024-12-16 20:28:50
date last changed
2025-04-04 14:39:37
@article{5ca7384c-6b87-4540-8b04-a368744216a9,
  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.}},
  author       = {{Cao, Qing and Zhang, Hanchen and Zhu, Feilin and Hao, Zhenchun and Yuan, Feifei}},
  issn         = {{1753-318X}},
  language     = {{eng}},
  month        = {{12}},
  number       = {{4}},
  publisher    = {{Blackwell Publishing}},
  series       = {{Journal of Flood Risk Management}},
  title        = {{Multi‐step‐ahead flood forecasting using an improved BiLSTM‐S2S model}},
  url          = {{http://dx.doi.org/10.1111/jfr3.12827}},
  doi          = {{10.1111/jfr3.12827}},
  volume       = {{15}},
  year         = {{2022}},
}