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Storm surge prediction using an artificial neural network model and cluster analysis

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  • Sung You
  • Jang-Won Seo

Abstract

In this study, an artificial neural network model was developed to predict storm surges in all Korean coastal regions, with a particular focus on regional extension. The cluster neural network model (CL-NN) assessed each cluster using a cluster analysis methodology. Agglomerative clustering was used to determine the optimal clustering of 21 stations, based on a centroid-linkage method of hierarchical clustering. Finally, CL-NN was used to predict storm surges in cluster regions. In order to validate model results, sea levels predicted by the CL-NN model were compared with results using conventional harmonic analysis and the artificial neural network model in each region (NN). The values predicted by the NN and CL-NN models were closer to observed data than values predicted using harmonic analysis. Data such as root mean square error and correlation coefficient varied only slightly between CL-NN and NN model results. These findings demonstrate that cluster analysis and the CL-NN model can be used to predict regional storm surges and may be used to develop a forecast system. Copyright Springer Science+Business Media B.V. 2009

Suggested Citation

  • Sung You & Jang-Won Seo, 2009. "Storm surge prediction using an artificial neural network model and cluster analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 51(1), pages 97-114, October.
    • Handle: RePEc:spr:nathaz:v:51:y:2009:i:1:p:97-114
    DOI: 10.1007/s11069-009-9396-x
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    Cited by:

    1. Jonathan Fabián Dato & Matías Gabriel Dinápoli & Enrique Eduardo D’Onofrio & Claudia Gloria Simionato, 2024. "On water level forecasting using artificial neural networks: the case of the Río de la Plata Estuary, Argentina," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(11), pages 9753-9776, September.
    2. Yen-Ming Chiang & Wei-Guo Cheng & Fi-John Chang, 2012. "A hybrid artificial neural network-based agri-economic model for predicting typhoon-induced losses," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 63(2), pages 769-787, September.
    3. Hassan Sharafi & Isa Ebtehaj & Hossein Bonakdari & Amir Hossein Zaji, 2016. "Design of a support vector machine with different kernel functions to predict scour depth around bridge piers," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(3), pages 2145-2162, December.
    4. Junli Xu & Yuhong Zhang & Xianqing Lv & Qiang Liu, 2019. "Inversion of Wind-Stress Drag Coefficient in Simulating Storm Surges by Means of Regularization Technique," IJERPH, MDPI, vol. 16(19), pages 1-16, September.
    5. Seung-Woo Kim & Jeffrey Melby & Norberto Nadal-Caraballo & Jay Ratcliff, 2015. "A time-dependent surrogate model for storm surge prediction based on an artificial neural network using high-fidelity synthetic hurricane modeling," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 76(1), pages 565-585, March.

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