A Spatiotemporal Convolutional Gated Recurrent Unit Network for Mean Wave Period Field Forecasting
Mean wave period (MWP) is one of the key parameters affecting the design of marine facilities. Currently, there are two main methods, numerical and data-driven methods, for forecasting wave parameters, of which the latter are widely used. However, few studies have focused on MWP forecasting, and eve...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-04-01
|
| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/9/4/383 |
| _version_ | 1827695924882702336 |
|---|---|
| author | Ting Yu Jichao Wang |
| author_facet | Ting Yu Jichao Wang |
| author_sort | Ting Yu |
| collection | DOAJ |
| description | Mean wave period (MWP) is one of the key parameters affecting the design of marine facilities. Currently, there are two main methods, numerical and data-driven methods, for forecasting wave parameters, of which the latter are widely used. However, few studies have focused on MWP forecasting, and even fewer have investigated it with spatial and temporal information. In this study, correlations between ocean dynamic parameters are explored to obtain appropriate input features, significant wave height (SWH) and MWP. Subsequently, a data-driven approach, the convolution gated recurrent unit (Conv-GRU) model with spatiotemporal characteristics, is utilized to field forecast MWP with 1, 3, 6, 12, and 24-h lead times in the South China Sea. Six points at different locations and six consecutive moments at every 12-h intervals are selected to study the forecasting ability of the proposed model. The Conv-GRU model has a better performance than the single gated recurrent unit (GRU) model in terms of root mean square error (RMSE), the scattering index (SI), Bias, and the Pearson’s correlation coefficient (R). With the lead time increasing, the forecast effect shows a decreasing trend, specifically, the experiment displays a relatively smooth forecast curve and presents a great advantage in the short-term forecast of the MWP field in the Conv-GRU model, where the RMSE is 0.121 m for 1-h lead time. |
| first_indexed | 2024-03-10T12:36:46Z |
| format | Article |
| id | doaj.art-7eb4f7b999244a23b6d3b844b26528ae |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-10T12:36:46Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-7eb4f7b999244a23b6d3b844b26528ae2023-11-21T14:11:54ZengMDPI AGJournal of Marine Science and Engineering2077-13122021-04-019438310.3390/jmse9040383A Spatiotemporal Convolutional Gated Recurrent Unit Network for Mean Wave Period Field ForecastingTing Yu0Jichao Wang1College of Science, China University of Petroleum, Qingdao 266580, ChinaCollege of Science, China University of Petroleum, Qingdao 266580, ChinaMean wave period (MWP) is one of the key parameters affecting the design of marine facilities. Currently, there are two main methods, numerical and data-driven methods, for forecasting wave parameters, of which the latter are widely used. However, few studies have focused on MWP forecasting, and even fewer have investigated it with spatial and temporal information. In this study, correlations between ocean dynamic parameters are explored to obtain appropriate input features, significant wave height (SWH) and MWP. Subsequently, a data-driven approach, the convolution gated recurrent unit (Conv-GRU) model with spatiotemporal characteristics, is utilized to field forecast MWP with 1, 3, 6, 12, and 24-h lead times in the South China Sea. Six points at different locations and six consecutive moments at every 12-h intervals are selected to study the forecasting ability of the proposed model. The Conv-GRU model has a better performance than the single gated recurrent unit (GRU) model in terms of root mean square error (RMSE), the scattering index (SI), Bias, and the Pearson’s correlation coefficient (R). With the lead time increasing, the forecast effect shows a decreasing trend, specifically, the experiment displays a relatively smooth forecast curve and presents a great advantage in the short-term forecast of the MWP field in the Conv-GRU model, where the RMSE is 0.121 m for 1-h lead time.https://www.mdpi.com/2077-1312/9/4/383mean wave perioddata-drivenconvolutional gated recurrent unitspatiotemporal |
| spellingShingle | Ting Yu Jichao Wang A Spatiotemporal Convolutional Gated Recurrent Unit Network for Mean Wave Period Field Forecasting Journal of Marine Science and Engineering mean wave period data-driven convolutional gated recurrent unit spatiotemporal |
| title | A Spatiotemporal Convolutional Gated Recurrent Unit Network for Mean Wave Period Field Forecasting |
| title_full | A Spatiotemporal Convolutional Gated Recurrent Unit Network for Mean Wave Period Field Forecasting |
| title_fullStr | A Spatiotemporal Convolutional Gated Recurrent Unit Network for Mean Wave Period Field Forecasting |
| title_full_unstemmed | A Spatiotemporal Convolutional Gated Recurrent Unit Network for Mean Wave Period Field Forecasting |
| title_short | A Spatiotemporal Convolutional Gated Recurrent Unit Network for Mean Wave Period Field Forecasting |
| title_sort | spatiotemporal convolutional gated recurrent unit network for mean wave period field forecasting |
| topic | mean wave period data-driven convolutional gated recurrent unit spatiotemporal |
| url | https://www.mdpi.com/2077-1312/9/4/383 |
| work_keys_str_mv | AT tingyu aspatiotemporalconvolutionalgatedrecurrentunitnetworkformeanwaveperiodfieldforecasting AT jichaowang aspatiotemporalconvolutionalgatedrecurrentunitnetworkformeanwaveperiodfieldforecasting AT tingyu spatiotemporalconvolutionalgatedrecurrentunitnetworkformeanwaveperiodfieldforecasting AT jichaowang spatiotemporalconvolutionalgatedrecurrentunitnetworkformeanwaveperiodfieldforecasting |