Detecting Anomalies in Meteorological Data Using Support Vector Regression
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 spa...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2018/5439256 |
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| _version_ | 1832555854147092480 |
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| author | Min-Ki Lee Seung-Hyun Moon Yourim Yoon Yong-Hyuk Kim Byung-Ro Moon |
| author_facet | Min-Ki Lee Seung-Hyun Moon Yourim Yoon Yong-Hyuk Kim Byung-Ro Moon |
| author_sort | Min-Ki Lee |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3eb40dc3f740488c80793f5641b354e0 |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-3eb40dc3f740488c80793f5641b354e02025-02-03T05:47:00ZengWileyAdvances in Meteorology1687-93091687-93172018-01-01201810.1155/2018/54392565439256Detecting Anomalies in Meteorological Data Using Support Vector RegressionMin-Ki Lee0Seung-Hyun Moon1Yourim Yoon2Yong-Hyuk Kim3Byung-Ro Moon4School of Computer Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of KoreaSchool of Computer Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of KoreaDepartment of Computer Engineering, College of Information Technology, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do 13120, Republic of KoreaSchool of Software, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, Republic of KoreaSchool of Computer Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of KoreaSignificant 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.http://dx.doi.org/10.1155/2018/5439256 |
| spellingShingle | Min-Ki Lee Seung-Hyun Moon Yourim Yoon Yong-Hyuk Kim Byung-Ro Moon Detecting Anomalies in Meteorological Data Using Support Vector Regression Advances in Meteorology |
| title | Detecting Anomalies in Meteorological Data Using Support Vector Regression |
| title_full | Detecting Anomalies in Meteorological Data Using Support Vector Regression |
| title_fullStr | Detecting Anomalies in Meteorological Data Using Support Vector Regression |
| title_full_unstemmed | Detecting Anomalies in Meteorological Data Using Support Vector Regression |
| title_short | Detecting Anomalies in Meteorological Data Using Support Vector Regression |
| title_sort | detecting anomalies in meteorological data using support vector regression |
| url | http://dx.doi.org/10.1155/2018/5439256 |
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