Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 270-280 |
| Number of pages | 11 |
| Journal | Ocean Engineering |
| Volume | 169 |
| DOIs | |
| Publication status | Published - 2018 Dec 1 |
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All Science Journal Classification (ASJC) codes
- Environmental Engineering
- Ocean Engineering
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Development of a warning model for coastal freak wave occurrences using an artificial neural network. / Doong, Dong-Jiing; Peng, Jen Ping; Chen, Ying Chih.
In: Ocean Engineering, Vol. 169, 01.12.2018, p. 270-280.Research output: Contribution to journal › Article
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
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.
UR - http://www.scopus.com/inward/record.url?scp=85054684603&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85054684603&partnerID=8YFLogxK
U2 - 10.1016/j.oceaneng.2018.09.029
DO - 10.1016/j.oceaneng.2018.09.029
M3 - Article
AN - SCOPUS:85054684603
VL - 169
SP - 270
EP - 280
JO - Ocean Engineering
JF - Ocean Engineering
SN - 0029-8018
ER -