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

Sung You () and Jang-Won Seo

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2009, vol. 51, issue 1, 97-114

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

Keywords: Cluster analysis; Neural network model; Storm surge prediction (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s11069-009-9396-x

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