Outlier Detection for Observational Data of Automatic Meteorological Station Based on Least Square Support Vector Machine

Zhen Meng; Shi Chang Zhang; Zeng Lin Huang

doi:10.4028/www.scientific.net/AMM.295-298.945

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 295-298Outlier Detection for Observational Data of...

Outlier Detection for Observational Data of Automatic Meteorological Station Based on Least Square Support Vector Machine

Abstract:

Automatic meteorological station is important for meteorological observation and the existence of outliers in the observational data is inevitable. The paper proposes outlier detection for observational data of automatic meteorological station based on least square support vector machine (LS-SVM). The method establishes the LS-SVM model for the meteorological factor and uses the model to evaluate the observational data. If the observational data deviate from the model, they would be seemed as outliers. The ground temperature data observed by two real automatic meteorological stations are used in experiments. Experiments results verify that the proposed method realize outlier detection for observational data of automatic meteorological station effectively and ensures subsequent process and analysis of the meteorological data.

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Periodical:

Applied Mechanics and Materials (Volumes 295-298)

Pages:

945-949

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.295-298.945

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Online since:

February 2013

Authors:

Zhen Meng, Shi Chang Zhang, Zeng Lin Huang

Keywords:

Automatic Meteorological Station, Least Squares Support Vector Machines (Ls-SVM), Meteorological Observation Data, Outlier Detection

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