Development of GMDH-Based Storm Surge Forecast Models for Sakaiminato, Tottori, Japan

Abstract

The current study developed storm surge hindcast/forecast models with lead times of 5, 12, and 24 h at the Sakaiminato port, Tottori, Japan, using the group method of data handling (GMDH) algorithm. For training, local meteorological and hydrodynamic data observed in Sakaiminato during Typhoons Maemi (2003), Songda (2004), and Megi (2004) were collected at six stations. In the forecast experiments, the two typhoons, Maemi and Megi, as well as the typhoon Songda, were used for training and testing, respectively. It was found that the essential input parameters varied with the lead time of the forecasts, and many types of input parameters relevant to training were necessary for near–far forecasting time-series of storm surge levels. In addition, it was seen that the inclusion of the storm surge level at the input layer was critical to the accuracy of the forecast model.