Coastal Wave Height Prediction using Recurrent Neural Networks (RNNs) in the South Caspian Sea |
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| Authors: | Tayeb Sadeghifar Maryam Nouri Motlagh Massoud Torabi Azad Mahdi Mohammad Mahdizadeh |
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| Institution: | 1. Department Physical Oceanography, Faculty of Marine Sciences, Tarbiat Modares University, Tehran, Iran;2. Department Physical Oceanography, Faculty of Marine Sciences, Isfahan University, Isfahan, Iran;3. Department Physical Oceanography, Islamic Azad University, North Tehran Branch, Tehran, Iran;4. Faculty of Science and Technologies Marines, University of Hormozgan, Bandar Abbas, Iran |
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| Abstract: | The prediction of wave parameters has a great significance in the coastal and offshore engineering. For this purpose, several models and approaches have been proposed to predict wave parameters, such as empirical, soft computing, and numerical based approaches. Recently, soft computing techniques such as recurrent neural networks (RNN) have been used to develop sea wave prediction models. In this study, the RNN for wave prediction based on the data gathered and the measurement of the sea waves in the Caspian Sea, in the north of Iran is used for this study. The efficiency of RNNs for 3, 6, and 12 hourly and diurnal wave prediction using correlation coefficients is calculated to be 0.96, 0.90, 0.87, and 0.73, respectively. This indicates that wave prediction by using RNNs yields better results than the previous neural network approaches. |
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| Keywords: | Correlation coefficients recurrent neural networks Southern Caspian Sea wave height |
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