Applying a machine learning technique to classification of Japanese pressure patterns

H. Kimura, H. Kawashima, H. Kusaka, H. Kitagawa

研究成果: Article査読

1 被引用数 (Scopus)

抄録

In climate research, pressure patterns are often very important. When a climatologists need to know the days of a specific pressure pattern, for example "low pressure in Western areas of Japan and high pressure in Eastern areas of Japan (Japanese winter-type weather)," they have to visually check a huge number of surface weather charts. To overcome this problem, we propose an automatic classification system using a support vector machine (SVM), which is a machine-learning method. We attempted to classify pressure patterns into two classes: "winter type" and "non-winter type". For both training datasets and test datasets, we used the JRA-25 dataset from 1981 to 2000. An experimental evaluation showed that our method obtained a greater than 0.8 F-measure. We noted that variations in results were based on differences in training datasets.

本文言語English
ページ(範囲)S59-S67
ジャーナルData Science Journal
8
DOI
出版ステータスPublished - 2009 3 30
外部発表はい

ASJC Scopus subject areas

  • コンピュータ サイエンス(その他)
  • コンピュータ サイエンスの応用

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