Kim, S.-W.; Lee, A., and Mun, J., 2018. A Surrogate Modeling for Storm Surge Prediction Using an Artificial Neural Network. 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. 866–870. Coconut Creek (Florida), ISSN 0749-0208. A surrogate model for storm surge prediction is developed using on an artificial neural network with the measured tidal level in Korea peninsula. The 59 historical storms during 1978 to 2014 years are used in this modelling. Tidal data recorded for 15 years was applied. The neural network between seven input parameters (i.e., latitude, longitude, moving speed, heading direction, central pressure, radius of strong wind speed, maximum wind speed) and the storm surge is trained by Levenberg-Marquardt backpropagation algorithm. The type of network is a multilayer feedforward network. The data is divided by 70% for training, 15 % for vali...