Detecting Anomalies in Meteorological Data Using Support Vector Regression
Joint Authors
Yoon, Yourim
Kim, Yong-Hyuk
Moon, Byung-Ro
Lee, Min-Ki
Moon, Seung-Hyun
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-06-26
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Significant errors exist in automated meteorological data, and identifying them is very important.
In this paper, we present a novel method for determining abnormal values in meteorological observations based on support vector regression (SVR).
SVR is used to predict the observation value from a spatial perspective.
The difference between the estimated value and the actual observed value determines if the observed value is abnormal or not.
In addition, SVR input variables are deliberately selected to improve SVR performance and shorten computing time.
In the selection process, a multiobjective genetic algorithm is used to optimize the two objective functions.
In experiments using real-world data sets collected from accredited agencies, the proposed estimation method using SVR reduced the RMSE by an average of 45.44% whilst maintaining competitive computing times compared to baseline estimators.
American Psychological Association (APA)
Lee, Min-Ki& Moon, Seung-Hyun& Yoon, Yourim& Kim, Yong-Hyuk& Moon, Byung-Ro. 2018. Detecting Anomalies in Meteorological Data Using Support Vector Regression. Advances in Meteorology،Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1118787
Modern Language Association (MLA)
Lee, Min-Ki…[et al.]. Detecting Anomalies in Meteorological Data Using Support Vector Regression. Advances in Meteorology No. 2018 (2018), pp.1-14.
https://search.emarefa.net/detail/BIM-1118787
American Medical Association (AMA)
Lee, Min-Ki& Moon, Seung-Hyun& Yoon, Yourim& Kim, Yong-Hyuk& Moon, Byung-Ro. Detecting Anomalies in Meteorological Data Using Support Vector Regression. Advances in Meteorology. 2018. Vol. 2018, no. 2018, pp.1-14.
https://search.emarefa.net/detail/BIM-1118787
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1118787