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Improving Coastal Ocean Wave Height Forecasting during Typhoons by using Local Meteorological and Neighboring Wave Data in Support Vector Regression Models

1
Department of Hydraulic and Ocean Engineering, National Cheng Kung University, Tainan City 701, Taiwan
2
Department of Water Resources Engineering and Conservation, Feng Chia University, Taichung City 407, Taiwan
*
Author to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2020, 8(3), 149; https://doi.org/10.3390/jmse8030149
Received: 5 February 2020 / Revised: 19 February 2020 / Accepted: 24 February 2020 / Published: 26 February 2020
(This article belongs to the Special Issue Coastal Flooding and Protection)
This study is aimed at applying support vector regression to perform real-time typhoon wave height forecasting with lead times of 1 to 3 h. Two wave rider buoys in the coastal ocean northeast of Taiwan provided real-time observation wave and meteorological data for the study. Information from actual typhoon events was collected and used for model calibration and validation. Three model structures were developed with different combinations of input variables, including wave, typhoon, and meteorological data. Analysis of forecasting results indicated that the proposed models have good generalization ability, but forecasts with longer lead times underestimate extreme wave heights. Comparisons of models with different inputs indicated that adding local meteorological data enhanced forecasting accuracy. Backup models were also developed in case local wave and meteorological data were unavailable. Analysis of these models revealed that when local wave heights are unknown, using neighboring wave heights can improve forecasting performance. View Full-Text
Keywords: wave height forecasting; typhoon; support vector regression wave height forecasting; typhoon; support vector regression
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Chen, S.-T.; Wang, Y.-W. Improving Coastal Ocean Wave Height Forecasting during Typhoons by using Local Meteorological and Neighboring Wave Data in Support Vector Regression Models. J. Mar. Sci. Eng. 2020, 8, 149.

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