A Surrogate Modeling for Storm Surge Prediction Using an Artificial Neural Network

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.