Meteorological data outlier detection: a principal component approach

Arthur C. Depoian II; Eric King; Colleen P. Bailey; Parthasarathy Guturu

doi:10.1117/12.2574178

20 September 2020 Meteorological data outlier detection: a principal component approach

Arthur C. Depoian II, Eric King, Colleen P. Bailey, Parthasarathy Guturu

Proceedings Volume 11528, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXII; 115280Q (2020) https://doi.org/10.1117/12.2574178
Event: SPIE Remote Sensing, 2020, Online Only

Abstract

Meteorological modeling takes data captured from multiple sources that is then processed by data mining techniques to predict environmental changes. The most commonly used machine learning techniques for processing meteorological data are decision trees, rule-based methods, neural networks, naive Bayes, Bayesian belief networks, and support vector machines. These techniques require accurate data for effective models to be simulated. Meteorological datasets can contain outliers and errors that can significantly skew the accuracy of the generated models that are relied upon for many sectors of society including agriculture, natural disasters, and meteorological forecasting. This paper proposes a method to eliminate outliers from meteorological data to enhance the accuracy of models by applying a blind thresholding algorithm to the principal components (PCs) obtained from L₁ and L₂ norm Principal Component Analysis to identify and discard outliers in the dataset.

Conference Presentation

Citation Download Citation

Arthur C. Depoian II, Eric King, Colleen P. Bailey, and Parthasarathy Guturu "Meteorological data outlier detection: a principal component approach", Proc. SPIE 11528, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXII, 115280Q (20 September 2020); https://doi.org/10.1117/12.2574178

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