Significant wave height modelling using a hybrid Wavelet-genetic Programming approach

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• 作者：Sajad Shahabi ; Mohammad-Javad Khanjani…
• 关键词：genetic programming ; lead time ; significant wave height ; time series ; wavelet
• 刊名：KSCE Journal of Civil Engineering
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：21
• 期：1
• 页码：1-10
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Civil Engineering; Industrial Pollution Prevention; Geotechnical Engineering & Applied Earth Sciences;
• 出版者：Korean Society of Civil Engineers
• ISSN：1976-3808
• 卷排序：21

文摘

In this paper, Genetic Programming (GP) based wavelet transform (WGP) was developed to forecast Significant Wave Height (SWH) in different lead times. The hourly SWH values for two buoy stations located in the North Atlantic Ocean were applied to train and validate the WGP model. For this purpose, the SWH main time series was decomposed into some subseries using wavelet transform and then decomposed time series were imported to GP model to forecast the SWH. Furthermore, GP approach was independently used to the same data set for comparison purposes. Performance of the WGP model was evaluated using correlation coefficient (R), Root Mean Square Error (RMSE), index of agreement (Ia) and Mean Absolute Error (MAE). The analysis proved that the model accuracy is highly depended on the decomposition levels. The obtained results showed that WGP model is able to forecast the SWH with a high reliability.