Articles | Volume 19, issue 6
Research article
09 Nov 2023
Research article |  | 09 Nov 2023

Short-term prediction of the significant wave height and average wave period based on the variational mode decomposition–temporal convolutional network–long short-term memory (VMD–TCN–LSTM) algorithm

Qiyan Ji, Lei Han, Lifang Jiang, Yuting Zhang, Minghong Xie, and Yu Liu


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-960', Brandon Bethel, 20 Jun 2023
  • RC2: 'Comment on egusphere-2023-960', Anonymous Referee #2, 27 Jul 2023
  • RC3: 'Comment on egusphere-2023-960', Anonymous Referee #3, 08 Aug 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Lei Han on behalf of the Authors (11 Sep 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (13 Sep 2023) by Meric Srokosz
RR by Anonymous Referee #2 (13 Sep 2023)
RR by Anonymous Referee #3 (20 Sep 2023)
ED: Publish as is (20 Sep 2023) by Meric Srokosz
AR by Lei Han on behalf of the Authors (24 Sep 2023)  Manuscript 
Short summary
Accurate wave forecasts are essential to marine engineering safety. The research designs a model with combined signal decomposition and multiple neural network algorithms to predict wave parameters. The hybrid wave prediction model has good robustness and generalization ability. The contribution of the various algorithms to the model prediction skill was analyzed by the ablation experiments. This work provides a neoteric view of marine element forecasting based on artificial intelligence.