ORCID Profile
0000-0002-8829-6007
Current Organisation
University Of Strathclyde
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Publisher: Springer Science and Business Media LLC
Date: 19-08-2019
Publisher: Elsevier BV
Date: 2011
Publisher: Springer Science and Business Media LLC
Date: 10-04-2015
Publisher: Elsevier BV
Date: 08-2016
Publisher: Elsevier BV
Date: 09-2014
Publisher: Elsevier BV
Date: 02-2016
Publisher: Elsevier BV
Date: 10-2020
Publisher: Coastal Education and Research Foundation
Date: 02-01-2013
DOI: 10.2112/SI65-043.1
Publisher: Elsevier BV
Date: 09-2013
Publisher: Springer Science and Business Media LLC
Date: 12-02-2019
Publisher: Elsevier BV
Date: 12-2017
Publisher: Thomas Telford Ltd.
Date: 03-2014
Abstract: Providing energy without unfavourable impact on the environment is an important issue that is considered by societies. This paper focuses on forecasting the wave energy over horizons of 1–12 h, in the southern part of the Caspian Sea. For this purpose, an artificial neural network was used to obtain the wave energy flux using two different methods. First, the components of wave energy flux, including the significant wave height and peak wave period were predicted separately and the wave energy flux was calculated by combining them and second, the wave energy flux was forecast directly. The results showed that the prediction of components separately yielded more accurate results. It was found that the longer the forecasting time horizon, the less accurate was the prediction. This is because in large time horizons, the previous wave characteristics have little influence on the wave energy flux. The forecast wave energy flux in both methods correlated well with observed data in short horizons.
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Bahareh Kamranzad.