TY - JOUR
T1 - Development of a warning model for coastal freak wave occurrences using an artificial neural network
AU - Doong, Dong Jiing
AU - Peng, Jen Ping
AU - Chen, Ying Chih
N1 - Funding Information:
The authors thank the anonymous reviewers for their careful reading of the manuscript and their many insightful and constructive comments and suggestions to improve this paper. This research was supported by the Ministry of Science and Technology (Grant No: MOST 106-2628-E-006-008-MY3 ) and the Central Weather Bureau (Grant No: MOTC-CWB-107-O-02 ) of Taiwan. The buoy data used in this study were measured and qualified by the Coastal Ocean Monitoring Center of National Cheng Kung University . The authors would like to express their great thanks for all the supports. Thanks are also extended to Prof. Cheng-Han Tsai of the National Taiwan Ocean University, Prof. Jen-Chih Tsai of the Chungyu University of Film and Arts and Prof. Shien-Tsung Chen of the Feng Chia University of Taiwan for their constructive comments and suggestions during this research.
Funding Information:
The authors thank the anonymous reviewers for their careful reading of the manuscript and their many insightful and constructive comments and suggestions to improve this paper. This research was supported by the Ministry of Science and Technology (Grant No: MOST 106-2628-E-006-008-MY3) and the Central Weather Bureau (Grant No: MOTC-CWB-107-O-02) of Taiwan. The buoy data used in this study were measured and qualified by the Coastal Ocean Monitoring Center of National Cheng Kung University. The authors would like to express their great thanks for all the supports. Thanks are also extended to Prof. Cheng-Han Tsai of the National Taiwan Ocean University, Prof. Jen-Chih Tsai of the Chungyu University of Film and Arts and Prof. Shien-Tsung Chen of the Feng Chia University of Taiwan for their constructive comments and suggestions during this research.
Publisher Copyright:
© 2018 Elsevier Ltd
PY - 2018/12/1
Y1 - 2018/12/1
N2 - The potential for coastal freak waves (CFWs) represents a threat to people living in coastal areas. CFWs are generated via the evolution of a wave and its interactions with coastal structures or rocks; however, the exact mechanism of their formation is not clear. Here, a data-driven warning model based on an artificial neural network (ANN) is proposed to predict the possibility of CFW occurrence. Seven parameters (significant wave height, peak period, wind speed, wave groupiness factor, Benjamin Feir Index (BFI), kurtosis, and wind-wave direction misalignment) collected prior to the occurrence of the CFW are used to develop the model. The buoy data associated with 40 known CFW events are used for model training, and the data associated with 23 such events are used for validation. The use of data obtained during the 6-h period prior to CFW occurrence combined with the same amount of non-CFW data is shown to produce the best model. Two validations using media-published and camera-recorded CFW events show that the accuracy rate (ACR) exceeds 90% and the recall rate (RCR) exceeds 87%, demonstrating the accuracy of the proposed model. This warning model has been implemented in operational runs since 2016.
AB - The potential for coastal freak waves (CFWs) represents a threat to people living in coastal areas. CFWs are generated via the evolution of a wave and its interactions with coastal structures or rocks; however, the exact mechanism of their formation is not clear. Here, a data-driven warning model based on an artificial neural network (ANN) is proposed to predict the possibility of CFW occurrence. Seven parameters (significant wave height, peak period, wind speed, wave groupiness factor, Benjamin Feir Index (BFI), kurtosis, and wind-wave direction misalignment) collected prior to the occurrence of the CFW are used to develop the model. The buoy data associated with 40 known CFW events are used for model training, and the data associated with 23 such events are used for validation. The use of data obtained during the 6-h period prior to CFW occurrence combined with the same amount of non-CFW data is shown to produce the best model. Two validations using media-published and camera-recorded CFW events show that the accuracy rate (ACR) exceeds 90% and the recall rate (RCR) exceeds 87%, demonstrating the accuracy of the proposed model. This warning model has been implemented in operational runs since 2016.
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U2 - 10.1016/j.oceaneng.2018.09.029
DO - 10.1016/j.oceaneng.2018.09.029
M3 - Article
AN - SCOPUS:85054684603
SN - 0029-8018
VL - 169
SP - 270
EP - 280
JO - Ocean Engineering
JF - Ocean Engineering
ER -