Articles | Volume 27, issue 1
https://doi.org/10.5194/hess-27-83-2023
https://doi.org/10.5194/hess-27-83-2023
Research article
 | 
02 Jan 2023
Research article |  | 02 Jan 2023

Advance prediction of coastal groundwater levels with temporal convolutional and long short-term memory networks

Xiaoying Zhang, Fan Dong, Guangquan Chen, and Zhenxue Dai

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-236', Anonymous Referee #1, 20 Aug 2022
    • AC1: 'Reply on RC1', Zhenxue Dai, 18 Sep 2022
  • RC2: 'Comment on hess-2022-236', Anonymous Referee #2, 14 Sep 2022
    • AC2: 'Reply on RC2', Zhenxue Dai, 18 Sep 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (further review by editor and referees) (20 Sep 2022) by Zhongbo Yu
AR by Zhenxue Dai on behalf of the Authors (23 Sep 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (27 Sep 2022) by Zhongbo Yu
RR by Anonymous Referee #2 (18 Oct 2022)
RR by Anonymous Referee #3 (20 Oct 2022)
ED: Publish subject to minor revisions (review by editor) (09 Nov 2022) by Zhongbo Yu
AR by Zhenxue Dai on behalf of the Authors (14 Nov 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (01 Dec 2022) by Zhongbo Yu
AR by Zhenxue Dai on behalf of the Authors (05 Dec 2022)
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Short summary
In a data-driven framework, groundwater levels can generally only be calculated 1 time step ahead. We discuss the advance prediction with longer forecast periods rather than single time steps by constructing a model based on a temporal convolutional network. Model accuracy and efficiency were further compared with an LSTM-based model. The two models derived in this study can help people cope with the uncertainty of what might occur in hydrological scenarios under the threat of climate change.