Filling gaps in wave records with artificial neural networks
This contribution presents a neural data interpolation methodology, which was implemented to restore missing wave measurements. The methodology is based on the ability of artificial neural networks to find and reproduce non-linear dependencies within complex geophysical systems. The data were obtain...
| Main Authors: | , , , |
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| Format: | Conference Paper |
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Balkema
2005
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| Online Access: | http://www.tandf.co.uk http://hdl.handle.net/20.500.11937/40874 |
| _version_ | 1848755987732758528 |
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| author | Makarynskyy, Oleg Makarynska, D. Rusu, E. Gavrilov, Alexander |
| author_facet | Makarynskyy, Oleg Makarynska, D. Rusu, E. Gavrilov, Alexander |
| author_sort | Makarynskyy, Oleg |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This contribution presents a neural data interpolation methodology, which was implemented to restore missing wave measurements. The methodology is based on the ability of artificial neural networks to find and reproduce non-linear dependencies within complex geophysical systems. The data were obtained from a field campaign during July 1985- ecember 1993 near Tasmania. Wave observations from a "Waverider" buoy were broadcasted as a high frequency radio signal via a quarter-wave antenna to a "Diwar" receiver. These measurements were used to train and to validate the neural nets employed. To restore missing data over time periods from 12 to 36 hours, five feed-forward, three-layered, artificial neural networks of a similar structure were implemented. The artificial neural networks' performance was estimated in terms of the bias, root mean square error, correlation coefficient, and scatter index. The methodology demonstrated reliable results with a fairly good overall agreement between the restored wave records and actual measurements. |
| first_indexed | 2025-11-14T09:05:02Z |
| format | Conference Paper |
| id | curtin-20.500.11937-40874 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:05:02Z |
| publishDate | 2005 |
| publisher | Balkema |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-408742017-01-30T14:46:26Z Filling gaps in wave records with artificial neural networks Makarynskyy, Oleg Makarynska, D. Rusu, E. Gavrilov, Alexander artificial intelligence data measurements interpolation simulation significant wave height This contribution presents a neural data interpolation methodology, which was implemented to restore missing wave measurements. The methodology is based on the ability of artificial neural networks to find and reproduce non-linear dependencies within complex geophysical systems. The data were obtained from a field campaign during July 1985- ecember 1993 near Tasmania. Wave observations from a "Waverider" buoy were broadcasted as a high frequency radio signal via a quarter-wave antenna to a "Diwar" receiver. These measurements were used to train and to validate the neural nets employed. To restore missing data over time periods from 12 to 36 hours, five feed-forward, three-layered, artificial neural networks of a similar structure were implemented. The artificial neural networks' performance was estimated in terms of the bias, root mean square error, correlation coefficient, and scatter index. The methodology demonstrated reliable results with a fairly good overall agreement between the restored wave records and actual measurements. 2005 Conference Paper http://hdl.handle.net/20.500.11937/40874 http://www.tandf.co.uk Balkema restricted |
| spellingShingle | artificial intelligence data measurements interpolation simulation significant wave height Makarynskyy, Oleg Makarynska, D. Rusu, E. Gavrilov, Alexander Filling gaps in wave records with artificial neural networks |
| title | Filling gaps in wave records with artificial neural networks |
| title_full | Filling gaps in wave records with artificial neural networks |
| title_fullStr | Filling gaps in wave records with artificial neural networks |
| title_full_unstemmed | Filling gaps in wave records with artificial neural networks |
| title_short | Filling gaps in wave records with artificial neural networks |
| title_sort | filling gaps in wave records with artificial neural networks |
| topic | artificial intelligence data measurements interpolation simulation significant wave height |
| url | http://www.tandf.co.uk http://hdl.handle.net/20.500.11937/40874 |