Using Artificial Neural Networks to estimate sea level in continental and island coastal environments
The knowledge of sea level variations is of great importance in geoenvironmental and ocean-engineering applications. Estimations of sea level change with different warning times are of vital importance for the population of low-lying regions and islands. This contribution describes some recent advan...
| Main Authors: | , , , |
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| Format: | Book Chapter |
| Published: |
Taylor & Francis Group
2005
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| Online Access: | http://hdl.handle.net/20.500.11937/27462 |
| _version_ | 1848752270718533632 |
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| author | Makarynskyy, Oleg Makarynska, D. Kuhn, Michael Featherstone, Will |
| author_facet | Makarynskyy, Oleg Makarynska, D. Kuhn, Michael Featherstone, Will |
| author_sort | Makarynskyy, Oleg |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The knowledge of sea level variations is of great importance in geoenvironmental and ocean-engineering applications. Estimations of sea level change with different warning times are of vital importance for the population of low-lying regions and islands. This contribution describes some recent advances in the application of a meshless artificial intelligence technique (neural networks) to the tasks of sea level retrieval and forecast. This technique was employed because it has been proven to approximate the non-linear behaviour in a geophysical system. The data used were taken from several SEAFRAME stations, which provide records for the Australian Baseline Sea Level Monitoring Project. A feed-forward, three-layered, artificial neural network was implemented to retrieve and predict sea level variations with different lead times. This methodology demonstrated reliable results in terms of the correlation coefficient (0.82-0.96), root mean square error (about 10% of tidal range) and scatter index (0.1-0.2), when compared with actual observations. |
| first_indexed | 2025-11-14T08:05:57Z |
| format | Book Chapter |
| id | curtin-20.500.11937-27462 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:05:57Z |
| publishDate | 2005 |
| publisher | Taylor & Francis Group |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-274622017-01-30T12:59:13Z Using Artificial Neural Networks to estimate sea level in continental and island coastal environments Makarynskyy, Oleg Makarynska, D. Kuhn, Michael Featherstone, Will artificial intelligence sea level prediction The knowledge of sea level variations is of great importance in geoenvironmental and ocean-engineering applications. Estimations of sea level change with different warning times are of vital importance for the population of low-lying regions and islands. This contribution describes some recent advances in the application of a meshless artificial intelligence technique (neural networks) to the tasks of sea level retrieval and forecast. This technique was employed because it has been proven to approximate the non-linear behaviour in a geophysical system. The data used were taken from several SEAFRAME stations, which provide records for the Australian Baseline Sea Level Monitoring Project. A feed-forward, three-layered, artificial neural network was implemented to retrieve and predict sea level variations with different lead times. This methodology demonstrated reliable results in terms of the correlation coefficient (0.82-0.96), root mean square error (about 10% of tidal range) and scatter index (0.1-0.2), when compared with actual observations. 2005 Book Chapter http://hdl.handle.net/20.500.11937/27462 Taylor & Francis Group restricted |
| spellingShingle | artificial intelligence sea level prediction Makarynskyy, Oleg Makarynska, D. Kuhn, Michael Featherstone, Will Using Artificial Neural Networks to estimate sea level in continental and island coastal environments |
| title | Using Artificial Neural Networks to estimate sea level in continental and island coastal environments |
| title_full | Using Artificial Neural Networks to estimate sea level in continental and island coastal environments |
| title_fullStr | Using Artificial Neural Networks to estimate sea level in continental and island coastal environments |
| title_full_unstemmed | Using Artificial Neural Networks to estimate sea level in continental and island coastal environments |
| title_short | Using Artificial Neural Networks to estimate sea level in continental and island coastal environments |
| title_sort | using artificial neural networks to estimate sea level in continental and island coastal environments |
| topic | artificial intelligence sea level prediction |
| url | http://hdl.handle.net/20.500.11937/27462 |