自己組織化マップを利用した梅雨期における前線帯・小低気圧の認識
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概要
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The Self-Organizing Map algorithm, unsupervised neural network, was applied to the recognition of synoptic field patterns during the rainy season in Japan. During this season, the location of the frontal zone is fluctuating due to the balance between the low and high-pressure system. In addition, heavy rainfall events occur during this season. In the prediction, the temporal and spatial information on the synoptic fields have not ever been considered because of their complicity. However, they are deeply related to actual phenomena. Therefore, it is necessary to consider them. As the first approach, this study attempted to recognize synoptic field patterns during this season using a Self-Organizing Map algorithm. Two SOM's were structured, one is the Frontal Zone SOM (FZS) for the detection of location of a frontal zone and the other is the Low Pressure SOM (LPS) for the detection of location and existence of a low-pressure system. Finally, this study validated the recognizing ability of the two SOMs.
- 水文・水資源学会の論文
水文・水資源学会 | 論文
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