AAAI Publications, Twenty-Fifth International FLAIRS Conference

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Automated Weather Sensor Quality Control
Douglas E. Galarus, Rafal Angryk, John Sheppard

Last modified: 2012-05-16


In this paper, we investigate the application of data mining to existing techniques for quality control/anomaly detection on weather sensor observations. Specifically we adapt the popular Barnes Spatial interpolation method to use time-series distance rather than spatial distance to develop an online algorithm that uses readings from similar stations based on current and historical observations for interpolation and we demonstrate that this new algorithm exhibits less model error than the Barnes Spatial interpolation-based method. We focus on interpolation, which is a basis for this popular quality control method and other related methods, and examine a dataset of over 233 million temperature observations from California and surrounding areas. Our approach shows improved performance as indicated by mean squared error reduced by approximately one half for predicted values versus reported values.


data mining; sensor network; quality control; anomaly detection; outlier; machine learning

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