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Advances in Meteorology
Volume 2016 (2016), Article ID 4063632, 13 pages
Research Article

Spatiotemporal Pattern Networks of Heavy Rain among Automatic Weather Stations and Very-Short-Term Heavy-Rain Prediction

1Department of Computer Science and Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 139-701, Republic of Korea
2Department of Computer Engineering, Gachon University, 1342 Sengnamdaero, Sujeong-gu, Seongnam-si, Gyeonggi-do 461-701, Republic of Korea

Received 8 May 2015; Revised 6 July 2015; Accepted 14 July 2015

Academic Editor: Tomoo Ushio

Copyright © 2016 Yong-Hyuk Kim and Yourim Yoon. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Spatiotemporal pattern networks of heavy rain among automatic weather stations, which reflect the mobility of heavy rain, were constructed and analyzed based on the hourly precipitation data over the last ten years (from 2003 to 2012) in South Korea. Moreover, a new algorithm applying the constructed heavy-rain pattern networks to very-short-term heavy-rain prediction was developed, and significant prediction results could be obtained.