Investigation and prediction of sea level variations in the northern coasts of Persian Gulf using artificial neural network
Sea- level variations have a significant impact on coastal areas. Prediction of sea level variations expected from the pre most critical information needs associated with the sea environment. For this, various methods exist. In this study, on the northern coast of the Persian Gulf have been studied...
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ftaquaticcommons:oai:generic.eprints.org:21059 2023-05-15T17:34:05+02:00 Investigation and prediction of sea level variations in the northern coasts of Persian Gulf using artificial neural network Ghanea, Hamid Reza 2011 application/pdf http://aquaticcommons.org/21059/ http://aquaticcommons.org/21059/1/27006.pdf fa per http://aquaticcommons.org/21059/1/27006.pdf Ghanea, Hamid Reza (2011) Investigation and prediction of sea level variations in the northern coasts of Persian Gulf using artificial neural network. Masters Thesis, Islamic Azad University, Science and Research Branch, Tehran, 79pp. Environment Oceanography Thesis NonPeerReviewed 2011 ftaquaticcommons 2020-02-27T09:29:54Z Sea- level variations have a significant impact on coastal areas. Prediction of sea level variations expected from the pre most critical information needs associated with the sea environment. For this, various methods exist. In this study, on the northern coast of the Persian Gulf have been studied relation to the effectiveness of parameters such as pressure, temperature and wind speed on sea leve and associated with global parameters such as the North Atlantic Oscillation index and NAO index and present statistic models for prediction of sea level. In the next step by using artificial neural network predict sea level for first in this region. Then compared results of the models. Prediction using statistical models estimated in terms correlation coefficient R = 0.84 and root mean square error (RMS) 21.9 cm for the Bushehr station, and R = 0.85 and root mean square error (RMS) 48.4 cm for Rajai station, While neural network used to have 4 layers and each middle layer six neurons is best for prediction and produces the results reliably in terms of correlation coefficient with R = 0.90126 and the root mean square error (RMS) 13.7 cm for the Bushehr station, and R = 0.93916 and the root mean square error (RMS) 22.6 cm for Rajai station. Therefore, the proposed methodology could be successfully used in the study area. Thesis North Atlantic North Atlantic oscillation International Association of Aquatic and Marine Science Libraries and Information Centers (IAMSLIC): Aquatic Commons |
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International Association of Aquatic and Marine Science Libraries and Information Centers (IAMSLIC): Aquatic Commons |
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Persian |
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Environment Oceanography |
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Environment Oceanography Ghanea, Hamid Reza Investigation and prediction of sea level variations in the northern coasts of Persian Gulf using artificial neural network |
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Environment Oceanography |
description |
Sea- level variations have a significant impact on coastal areas. Prediction of sea level variations expected from the pre most critical information needs associated with the sea environment. For this, various methods exist. In this study, on the northern coast of the Persian Gulf have been studied relation to the effectiveness of parameters such as pressure, temperature and wind speed on sea leve and associated with global parameters such as the North Atlantic Oscillation index and NAO index and present statistic models for prediction of sea level. In the next step by using artificial neural network predict sea level for first in this region. Then compared results of the models. Prediction using statistical models estimated in terms correlation coefficient R = 0.84 and root mean square error (RMS) 21.9 cm for the Bushehr station, and R = 0.85 and root mean square error (RMS) 48.4 cm for Rajai station, While neural network used to have 4 layers and each middle layer six neurons is best for prediction and produces the results reliably in terms of correlation coefficient with R = 0.90126 and the root mean square error (RMS) 13.7 cm for the Bushehr station, and R = 0.93916 and the root mean square error (RMS) 22.6 cm for Rajai station. Therefore, the proposed methodology could be successfully used in the study area. |
format |
Thesis |
author |
Ghanea, Hamid Reza |
author_facet |
Ghanea, Hamid Reza |
author_sort |
Ghanea, Hamid Reza |
title |
Investigation and prediction of sea level variations in the northern coasts of Persian Gulf using artificial neural network |
title_short |
Investigation and prediction of sea level variations in the northern coasts of Persian Gulf using artificial neural network |
title_full |
Investigation and prediction of sea level variations in the northern coasts of Persian Gulf using artificial neural network |
title_fullStr |
Investigation and prediction of sea level variations in the northern coasts of Persian Gulf using artificial neural network |
title_full_unstemmed |
Investigation and prediction of sea level variations in the northern coasts of Persian Gulf using artificial neural network |
title_sort |
investigation and prediction of sea level variations in the northern coasts of persian gulf using artificial neural network |
publishDate |
2011 |
url |
http://aquaticcommons.org/21059/ http://aquaticcommons.org/21059/1/27006.pdf |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_relation |
http://aquaticcommons.org/21059/1/27006.pdf Ghanea, Hamid Reza (2011) Investigation and prediction of sea level variations in the northern coasts of Persian Gulf using artificial neural network. Masters Thesis, Islamic Azad University, Science and Research Branch, Tehran, 79pp. |
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