Browsing by Author "Ajeesh, K."

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    Forecasting of Significant Wave Height Using Support Vector Regression
    (2016) Ajeesh, K.; Deka, P.C.
    The reliability of wave prediction is a crucial issue in coastal, harbor and ocean engineering. Support vector machine (SVM) is an appropriate and suitable method for significant wave height (Hs) prediction due to its best versatility, robustness, and effectiveness. In this present work, only significant wave height (Hs) of previous time steps were used as predictors during the period 01-01-2004 to 01-04-2004. The data used is processed significant wave height (Hs) of the station SW4(Latitude 12056?31? and longitude 74043?58?) located near west coast of India.70% of the data used for calibration of model parameters and remaining 30% data used for validation using various input combinations. The performance of both the RBF and PUK models is assessed using different statistical indices. (E.g. CC (RBF - SVR) = 0.82, CC (PUK-SVR) = 0.93, MAE (RBF - SVR) = 0.04, MAE (PUK-SVR) =0.04 RMSE (RBF-SVR) =0.06, RMSE (PUK-SVR) =0.05. The results show that SVM can be successfully used for prediction of Hs. � 2015 IEEE.
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    Forecasting of Significant Wave Height Using Support Vector Regression
    (Institute of Electrical and Electronics Engineers Inc., 2016) Ajeesh, K.; Deka, P.C.
    The reliability of wave prediction is a crucial issue in coastal, harbor and ocean engineering. Support vector machine (SVM) is an appropriate and suitable method for significant wave height (Hs) prediction due to its best versatility, robustness, and effectiveness. In this present work, only significant wave height (Hs) of previous time steps were used as predictors during the period 01-01-2004 to 01-04-2004. The data used is processed significant wave height (Hs) of the station SW4(Latitude 12056′31″ and longitude 74043′58″) located near west coast of India.70% of the data used for calibration of model parameters and remaining 30% data used for validation using various input combinations. The performance of both the RBF and PUK models is assessed using different statistical indices. (E.g. CC (RBF - SVR) = 0.82, CC (PUK-SVR) = 0.93, MAE (RBF - SVR) = 0.04, MAE (PUK-SVR) =0.04 RMSE (RBF-SVR) =0.06, RMSE (PUK-SVR) =0.05. The results show that SVM can be successfully used for prediction of Hs. © 2015 IEEE.