In the present study, the performance of the real time storm surge forecast model based on the neural network is examined by forecasting Typhoon Megi storm surge 2003 at Sakai, Japan in terms of a variety of the combinations of data obtained from Typhoons Songda 2004 and Maemi 2003. In the experiments, the data sets are trained with the meteorological data measured at five stations: the sea level pressure, the depression rate of the sea level pressure, the wind speed, the wind direction; the hydroulic data: the sea surface level and the storm surge at Sakai; the typhoon parameters: the typhoon position, the central pressure of the typhoon and the highest wind speed near the typhoon center. In addition, the forecast time spans of 01, 02, 03, 04, 05, 12 and 24 hours are investigated for all cases of the data sets. From the results, It is found that the performance of the real time forecast models shows best when training the neural network with the data set of the storm surge, the sea level pressure, the depression rates of the sea level pressure, the wind speed and the typhoon position at Sakai
