Research Papers
Applying a Machine Learning Technique to Classification of Japanese Pressure Patterns
Authors:
H Kimura ,
Graduate School of Systems and Information Engineering, University of Tsukuba, Tsukuba, Ibaraki, Japan
H Kawashima,
Graduate School of Systems and Information Engineering, University of Tsukuba, Tsukuba, Ibaraki, Japan
Center for Computational Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan
H Kusaka,
Center for Computational Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan
H Kitagawa
Graduate School of Systems and Information Engineering, University of Tsukuba, Tsukuba, Ibaraki, Japan
Center for Computational Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan
Abstract
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.
How to Cite:
Kimura, H., Kawashima, H., Kusaka, H. and Kitagawa, H., 2009. Applying a Machine Learning Technique to Classification of Japanese Pressure Patterns. Data Science Journal, 8, pp.S59–S67. DOI: http://doi.org/10.2481/dsj.8.S59
Published on
01 Apr 2009.
Peer Reviewed
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