期刊
NATURAL HAZARDS
卷 51, 期 1, 页码 97-114出版社
SPRINGER
DOI: 10.1007/s11069-009-9396-x
关键词
Cluster analysis; Neural network model; Storm surge prediction
资金
- NIMR/KMA
- KORDI NAP
- Korea Meteorological Administration [NIMR-2009-B-3] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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.
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