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...
Main Author: | |
---|---|
Format: | Thesis |
Language: | Persian |
Published: |
Islamic Azad University, Science and Research Branch, Tehran, Marine Physics
2011
|
Subjects: | |
Online Access: | http://hdl.handle.net/1834/35911 |
id |
ftoceandocs:oai:aquadocs.org:1834/35911 |
---|---|
record_format |
openpolar |
spelling |
ftoceandocs:oai:aquadocs.org:1834/35911 2023-05-15T17:34:01+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 79 http://hdl.handle.net/1834/35911 fa per Islamic Azad University, Science and Research Branch, Tehran, Marine Physics http://www.srbiau.ac.ir http://hdl.handle.net/1834/35911 http://aquaticcommons.org/id/eprint/21059 17408 2016-10-12 07:53:09 21059 Islamic Azad University, Science and Research Branch, Tehran Environment Oceanography thesis 2011 ftoceandocs 2023-04-06T17:05:30Z 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. Masters Supervisor:E., Hasanzadeh; Counsellor: Ali Akbari, Bidokhti Thesis North Atlantic North Atlantic oscillation IODE-UNESCO: OceanDocs - E-Repository of Ocean Publications |
institution |
Open Polar |
collection |
IODE-UNESCO: OceanDocs - E-Repository of Ocean Publications |
op_collection_id |
ftoceandocs |
language |
Persian |
topic |
Environment Oceanography |
spellingShingle |
Environment Oceanography Ghanea, Hamid Reza Investigation and prediction of sea level variations in the northern coasts of Persian Gulf using artificial neural network |
topic_facet |
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. Masters Supervisor:E., Hasanzadeh; Counsellor: Ali Akbari, Bidokhti |
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 |
publisher |
Islamic Azad University, Science and Research Branch, Tehran, Marine Physics |
publishDate |
2011 |
url |
http://hdl.handle.net/1834/35911 |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_source |
http://aquaticcommons.org/id/eprint/21059 17408 2016-10-12 07:53:09 21059 Islamic Azad University, Science and Research Branch, Tehran |
op_relation |
http://www.srbiau.ac.ir http://hdl.handle.net/1834/35911 |
_version_ |
1766132709695946752 |