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ABSTRACT Mun, J.; Kim, S.-W., and Melby, J. A. 2018. A surrogate model for wave prediction based on an artificial neural network using high-fidelity synthetic hurricane modelling. 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. 876–880. Coconut Creek (Florida), ISSN 0749-0208. A surrogate model for significant wave height and period prediction is developed based on an artificial neural network with synthetic simulations of hurricanes. The neural networks between five input parameters and the wave height or wave period are trained by the Levenberg-Marquardt backpropagation algorithm. A back propagation is a form of supervising training algorithm and the weighting factors and the bias are updated by the back propagation training within a limited epoch. The data consists high intensity 446 storms on west region in Texas. The 14 save points in this area are analysed by the surrogate model. The performance of the static surrogate model for the significant waves is expressed as the correlation coefficient and the mean square error at the 14 save points. The minimum correlation coefficient and the mean square error are respectively 0.981 and 0.011. The predicted wave height and period are good agreement with the target values. [ABSTRACT FROM AUTHOR]

ABSTRACT 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 validation and 15 % for test. The six save points in southern Korea are analysed by the surrogate model. The performance of the storm surge surrogate model is expressed as the correlation coefficient and mean square error at the six save points. The minimum and maximum correlation coefficients are respectively 0.861 and 0.979. The developed surrogate model satisfies high-accuracy and high-speed for predicting he storm surge based on an artificial intelligence method and a grid-free system. [ABSTRACT FROM AUTHOR]

Kim, S.-W. and Suh, K.-D., 2013. Performance analysis of vertical breakwaters designed by partial safety factor method The partial safety factor method has been developed in Europe, Japan, and Korea to overcome the limitations of the deterministic method for design of the vertical breakwater. The practical engineers prefer the partial safety factor method because this method is relatively simple to use compared with other probabilistic methods. However, the partial safety factor method does not evaluate the displacement of the vertical breakwater, while only defining the boundary between safety and failure domains within a given target safety level. Therefore, it is required to examine the performance of the breakwater designed by the partial safety factor method in terms of caisson sliding distance. In the present study, the partial safety factors developed by an advanced research for vertical breakwaters are used with the target reliability indices of 2.33 and 2.0. The performance analysis of 12 cross-sections of five vertical breakwaters was conducted by the method based on the Monte-Carlo simulation. Especially, this method considers a technique to eliminate the unrealistic extreme values of the design variables. As a result, the breakwaters designed by the partial safety factor method with the target reliability index of 2.0 are found to be stable except for one cross-section with high uncertainty of wave height. On the other hand, all the breakwaters with the reliability index of 2.33 are found to be stable irrespective of any uncertainty of wave height. Finally, it is found that the vertical breakwaters designed with the reliability index of 2.33 satisfy the allowable exceedance probabilities for both repairable and ultimate limit states. [ABSTRACT FROM AUTHOR]