In this study, the group method of data handling (GMDH)-based wavelet transform (WT) was developed to forecast significant wave height (SWH) in different lead times. The SWH dataset was collected from a buoy station located in the North Atlantic Ocean. For this purpose, the time series of SWH was decomposed into some subseries using WT and then decomposed time series were imported to the GMDH model to forecast the SWH. Performance of the wavelet group method of data handling (WGMDH) model was evaluated using an index of agreement (Ia), coefficient of efficiency and root mean square error. The analysis proved that the model accuracy is highly dependent on the decomposition levels. The results showed that the WGMDH model is able to forecast the SWH with a high reliability.
Hybrid wavelet-GMDH model to forecast significant wave height
Sajad Shahabi, Mohammad-Javad Khanjani, Masoudreza Hessami Kermani; Hybrid wavelet-GMDH model to forecast significant wave height. Water Science and Technology: Water Supply 1 April 2016; 16 (2): 453–459. doi: https://doi.org/10.2166/ws.2015.151
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