Development of GMDH-Based Storm Surge Forecast Models for Sakaiminato, Tottori, Japan
Development of GMDH-Based Storm Surge Forecast Models for Sakaiminato, Tottori, Japan
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DOI：
10.3390/jmse8100797
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发表时间：
2020-10
影响因子：
2.9
通讯作者：
Sooyoul Kim;H. Mase;N. B. Thủy;Masahide Takeda;Cao Truong Tran;V. H. Dang
中科院分区：
文献类型：
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作者：
Sooyoul Kim;H. Mase;N. B. Thủy;Masahide Takeda;Cao Truong Tran;V. H. Dang
当前的研究使用数据处理组方法 (GMDH) 算法，在日本鸟取县境港港开发了提前时间为 5、12 和 24 小时的风暴潮事后/预报模型。为了进行训练，在台风“前美”（2003 年）、“宋达”（2004 年）和“鲫鱼”（2004 年）台风期间，境港观测到的当地气象和水动力数据在六个站点收集。在预报实验中，分别用“前美”和“鲫鱼”两个台风以及台风“松达”进行训练和测试。研究发现，基本输入参数随预报提前时间的不同而变化，并且对于风暴潮水平近远预报时间序列来说，需要多种与训练相关的输入参数。此外，我们发现输入层包含风暴潮水平对于预测模型的准确性至关重要。
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.