ConvGRU-RMWP: A Regional Multi-Step Model for Wave Height Prediction

Youjun Sun, Huajun Zhang (), Shulin Hu, Jun Shi, Jianning Geng and Yixin Su
Additional contact information
Youjun Sun: School of Automation, Wuhan University of Technology, Wuhan 430070, China
Huajun Zhang: School of Automation, Wuhan University of Technology, Wuhan 430070, China
Shulin Hu: School of Automation, Wuhan University of Technology, Wuhan 430070, China
Jun Shi: CSSC Marine Technology Co., Ltd., Shanghai 200136, China
Jianning Geng: CSSC Marine Technology Co., Ltd., Shanghai 200136, China
Yixin Su: School of Automation, Wuhan University of Technology, Wuhan 430070, China

Mathematics, 2023, vol. 11, issue 9, 1-21

Abstract: Accurate large-scale regional wave height prediction is important for the safety of ocean sailing. A regional multi-step wave height prediction model (ConvGRU-RMWP) based on ConvGRU is designed for the two problems of difficult spatial feature resolution and low accuracy of multi-step prediction in ocean navigation wave height prediction. For multi-step prediction, a multi-input multi-output prediction strategy is used, and wave direction and wave period are used as exogenous variables, which are combined with historical wave height data to expand the sample space. For spatial features, a convolutional gated recurrent neural network with an Encoder-Forecaster structure is used to extract and resolve multi-level spatial information. In contrast to time series forecasting methods that consider only backward and forward dependencies in the time dimension and a single assessment of the properties of the predictor variables themselves, this paper additionally considers spatial correlations and implied correlations among the meteorological variables. This model uses the wave height information of the past 24 h to predict the wave height information for the next 12 h. The prediction results in both space and time show that the model can effectively extract spatial and temporal correlations and obtain good multi-step wave height prediction results. The proposed method has a lower prediction error than the other five prediction methods and verifies the applicability of this model for three selected sea areas along the global crude oil transportation route, all of which have a lower prediction error.

Keywords: wave height prediction; regional multi-step prediction; ConvGRU; exogenous variables (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/9/2013/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/9/2013/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:9:p:2013-:d:1131256

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().