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.
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Research Article|
October 20 2015
Hybrid wavelet-GMDH model to forecast significant wave height Available to Purchase
Sajad Shahabi;
1Civil Engineering Department, Faculty of Engineering, Shahid Bahonar University of Kerman, P. O. Box 76169-133, Kerman, Iran
E-mail: [email protected]
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Mohammad-Javad Khanjani;
Mohammad-Javad Khanjani
1Civil Engineering Department, Faculty of Engineering, Shahid Bahonar University of Kerman, P. O. Box 76169-133, Kerman, Iran
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Masoudreza Hessami Kermani
Masoudreza Hessami Kermani
1Civil Engineering Department, Faculty of Engineering, Shahid Bahonar University of Kerman, P. O. Box 76169-133, Kerman, Iran
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Water Supply (2016) 16 (2): 453–459.
Article history
Received:
January 13 2015
Accepted:
September 29 2015
Citation
Sajad Shahabi, Mohammad-Javad Khanjani, Masoudreza Hessami Kermani; Hybrid wavelet-GMDH model to forecast significant wave height. Water Supply 1 April 2016; 16 (2): 453–459. doi: https://doi.org/10.2166/ws.2015.151
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