Detecting Anomalies in Meteorological Data Using Support Vector Regression

Min-Ki Lee, Seung-Hyun Moon, Yourim Yoon, Yong-Hyuk Kim, Byung-Ro Moon
2018 Advances in Meteorology  
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
more » ... ed 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.
doi:10.1155/2018/5439256 fatcat:e5xxnwoq5jfqbavxxjvr5td6fu