Operational probabilistic forecasting of coastal freak waves by using an artificial neural network

Dong Jiing Doong, Shien Tsung Chen, Ying Chih Chen, Cheng Han Tsai

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Coastal freak waves (CFWs) are unpredictable large waves that occur suddenly in coastal areas and have been reported to cause casualties worldwide. CFW forecasting is difficult because the complex mechanisms that cause CFWs are not well understood. This study proposes a probabilistic CFW forecasting model that is an advance on the basis of a previously proposed deterministic CFW forecasting model. This study also develops a probabilistic forecasting scheme to make an artificial neural network model achieve the probabilistic CFW forecasting. Eight wave and meteorological variables that are physically related to CFW occurrence were used as the inputs for the artificial neural network model. Two forecasting models were developed for these inputs. Model I adopted buoy observations, whereas Model II used wave model simulation data. CFW accidents in the coastal areas of northeast Taiwan were used to calibrate and validate the model. The probabilistic CFW forecasting model can perform predictions every 6 h with lead times of 12 and 24 h. The validation results demonstrated that Model I outperformed Model II regarding accuracy and recall. In 2018, the developed CFW forecasting models were investigated in operational mode in the Operational Forecast System of the Taiwan Central Weather Bureau. Comparing the probabilistic forecasting results with swell information and actual CFW occurrences demonstrated the effectiveness of the proposed probabilistic CFW forecasting model.

Original languageEnglish
Article number165
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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