The Study for Storm Surge Prediction Using Generalized Regression Neural Networks

Abstract

Lee. H.; Kim, S., and Jun, K., 2018. The study for storm surge prediction using generalized regression neural networks. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 781–785. Coconut Creek (Florida), ISSN 0749-0208.Although the dangers of storm surge have recently garnered increasing awareness, studies on storm surge height predictions in accordance with regional characteristics are quite insufficient. However, accurate and prompt storm surge height prediction is necessary to promptly respond to disaster caused by storm surge. Currently, most storm surge height prediction figures are obtained from numerical modelling technique-based study, which has a number of analytical limitations. For example various meteorological data and complex physical factor analysis are lacking. Therefore, the Generalized Regression Neural Network(GRNN) is applied in this study in order to create a storm surge height prediction model through simplified data input and hidden layer and output layer. When the established GRNN was applied to the Tongyeong and Yeosu area, the obtained values were very similar to the actual surveyed data. When the model was applied to ungauged areas, the tendency of storm surge height was identified.