Applying a Machine Learning Technique to Classification of Japanese Pressure Patterns

Data Science Journal

H Kitagawa²  H Kusaka¹  H Kawashima²  H Kimura² 

[1] Center for Computational Sciences, University of Tsukuba;Graduate School of Systems and Information Engineering, University of Tsukuba

关键词: support vector machine (SVM);    machine learning;    pressure pattern;    classification;   

DOI  :  10.2481/dsj.8.S59

学科分类：计算机科学（综合）

来源: Ubiquity Press Ltd.

【 摘 要 】

References(5)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.

【 授权许可】

Unknown