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.

中文翻译：

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日本鸟取县境港基于GMDH的风暴潮预报模型的开发

当前的研究使用分组数据处理（GMDH）算法开发了日本鸟取县境港的风暴潮后预报模型，其交货时间为5、12和24小时。为了进行培训，在六个台站收集了在台港Maemi（2003年），Songda（2004年）和Megi（2004年）期间在境港观测到的当地气象和水动力数据。在预报实验中，分别使用了Maemi和Megi这两个台风以及Songda台风进行了训练和测试。结果发现，基本输入参数随预报的提前期而变化，并且与训练有关的许多类型的输入参数对于风暴潮水平的近距离预报时间序列是必需的。此外，