Improving coastal oceanwave height forecasting during typhoons by using local meteorological and neighboring wave data in support vector regression models

Shien Tsung Chen, Yu Wei Wang

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Article number149
JournalJournal of Marine Science and Engineering
Volume8
Issue number3
DOIs
Publication statusPublished - 2020 Mar 1

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Water Science and Technology
  • Ocean Engineering

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