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Showing 2 results for Wave Simulation

Fereshteh Komijani, Masoud Sadri Nasab, Vahid Chegini, Seyed Mostafa Siadat Mousavi,
Volume 6, Issue 23 (10-2015)
Abstract

In this study, wave was simulated in the southern part of Caspian Sea using nested grids of SWAN model and utilizing ECMWF-ERAI wind data. Wave model was verified with the correlation of %94 comparing the modeled and measured data at Neka, Nushahr and Anzali stations. The simulations were repeated utilizing wind data used in ISWM II. Although, wind data used in ISWM II have been verified in the Caspian Sea, the higher spatial resolution of ECMWF-ERAI wind data resulted in a better prediction of the wave periods up to 1s and wave heights up to 0.5-1.5 m in the east and central parts of the southern Caspian Sea. However, the results of SWAN using ECMWF-Reanalysis INIO wind data were in a better agreement with the trend of measurements in west part of the Caspian Sea. Also, wind data used in ISWM II resulted in higher accuracy prediction of wave characteristic for measured wave heights less than 1.5 m.


Dr. Fereshte Komijani, Dr. Masoud Montazeri Namin, Dr. Asghar Bohluly,
Volume 11, Issue 42 (7-2020)
Abstract

In this study, Artificial Neural Networks (ANN) has been used for reducing the errors of sea wave model predictions. Firstly, stand-alone PMODynamicsI model has been implemented to predict Bushehr deep-water wave characteristics. Results implies that PMODynamicsI performed better in simulating ordinary wave with height less than 1m, but it is underestimated about 75cm related to a weak  wind Global Forecasting System (GFS) forecasts during east and southeast storms. In order to increase the wave model accuracy, a MLP ANN system consists of three layers of nodes has been defined to predict the wave model errors, which optimal selection of a number and type of input neurons among factors influence the formation of "wind waves" has helped to find the relationship between input and output in ANN to minimize model error. The combination of PMODynamicsI together with ANN technique has been improved the accuracy of the sea wave model forecast till %90 and reduced RMS error from 0.31 in stand-alone PMODynamicsI to 0.22 in combinations models. As a result of the use of combined wave and ANN systems makes accurate predictions for extreme wave about 60cm.


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نشریه علمی پژوهشی اقیانوس شناسی Journal of Oceanography
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