Coastal Wave Height Prediction using Recurrent Neural Networks (RNNs) in the South Caspian Sea

被引:56
|
作者
Sadeghifar, Tayeb [1 ]
Motlagh, Maryam Nouri [2 ]
Azad, Massoud Torabi [3 ]
Mahdizadeh, Mahdi Mohammad [4 ]
机构
[1] Tarbiat Modares Univ, Fac Marine Sci, Dept Phys Oceanog, Tehran, Iran
[2] Isfahan Univ, Fac Marine Sci, Dept Phys Oceanog, Esfahan, Iran
[3] Islamic Azad Univ, North Tehran Branch, Dept Phys Oceanog, Tehran, Iran
[4] Univ Hormozgan, Fac Sci & Technol Marines, Bandar Abbas, Iran
关键词
Correlation coefficients; recurrent neural networks; Southern Caspian Sea; wave height; TIME-SERIES; MODELS;
D O I
10.1080/01490419.2017.1359220
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
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.
引用
收藏
页码:454 / 465
页数:12
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