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Article

Accurate Storm Surge Prediction with a Parametric Cyclone and Neural Network Hybrid Model

1
Center of Excellence for Ocean Engineering, National Taiwan Ocean University, Keelung 20224, Taiwan
2
Department of Marine Environmental Informatics, National Taiwan Ocean University, Keelung 20224, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editor: Enedir Ghisi
Water 2022, 14(1), 96; https://doi.org/10.3390/w14010096
Received: 29 November 2021 / Revised: 21 December 2021 / Accepted: 1 January 2022 / Published: 4 January 2022
(This article belongs to the Special Issue Marine Environmental Research)
Storm surges are one of the most devastating coastal disasters. Numerous efforts have continuously been made to achieve better prediction of storm surge variation. In this paper, we propose a parametric cyclone and neural network hybrid model for accurate, long lead-time storm surge prediction. The model was applied to the northeastern coastal region of Taiwan, i.e., Longdong station. A total of 14 historical typhoon events were used for model training and validation, and the results and questions associated with this hybrid approach carefully discussed. Overall, the proposed method reduced the complexity of network structure while retaining the important typhoon indicators. In particular, local pressure and winds estimated from the storm parameters through physically-based parametric cyclone models allow for inferring the possible future influence of a typhoon, unlike the simple collection and direct usage of observation data from local stations in earlier works. Meanwhile, the error-tolerance capability of the neural network alleviated some discrepancy in the model inputs and enabled good surge prediction. Further, the proposed method showed better and faster convergence thanks to the retention of storm information and the reduced dimensions of the search space. The hybrid model presented excellent performance or maintained reasonable capability for short lead-time and long lead-time storm surge prediction. Compared with the pure neural network model under the same network dimensions, the present model demonstrated great improvement in accuracy as the prediction lead time increased to 8 h, e.g., 33–40% (13–21%) and 32–37% (18–29%) RMSE and CE, respectively, in the training/validation phase. View Full-Text
Keywords: storm surge; lead-time; parametric cyclone model; artificial neural network; hybrid approach; network dimensions storm surge; lead-time; parametric cyclone model; artificial neural network; hybrid approach; network dimensions
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MDPI and ACS Style

Chao, W.-T.; Young, C.-C. Accurate Storm Surge Prediction with a Parametric Cyclone and Neural Network Hybrid Model. Water 2022, 14, 96. https://doi.org/10.3390/w14010096

AMA Style

Chao W-T, Young C-C. Accurate Storm Surge Prediction with a Parametric Cyclone and Neural Network Hybrid Model. Water. 2022; 14(1):96. https://doi.org/10.3390/w14010096

Chicago/Turabian Style

Chao, Wei-Ting, and Chih-Chieh Young. 2022. "Accurate Storm Surge Prediction with a Parametric Cyclone and Neural Network Hybrid Model" Water 14, no. 1: 96. https://doi.org/10.3390/w14010096

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