Detecting Anomalies in Meteorological Data Using Support Vector Regression

Abstract

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.