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Using Technologies of OLAP and Machine Learning for Validation of the Numerical Models of Convective Clouds

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Computational Science and Its Applications -- ICCSA 2016 (ICCSA 2016)

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Abstract

The paper is a continuation of the works [13] where complex information system for organization of the input data for the models of convective clouds is presented. In the present work we use the information system for obtaining statistically significant amount of meteorological data about the state of the atmosphere in the place and at the time when dangerous convective phenomena are recorded. Corresponding amount of information has been collected about the state of the atmosphere in cases when no dangerous convective phenomena have been observed. Feature selection for thunderstorm forecasting based on Recursive feature elimination with cross-validation algorithm is provided. Three methods of machine learning: Support Vector Machine, Logistic Regression and Ridge Regression are used for making the decision on whether or not a dangerous convective phenomenon occurs at present atmospheric conditions. The OLAP technology is used for development of the concept of multidimensional data base intended for distinguishing the types of the phenomena (thunderstorm, heavy rainfall and light rain).

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Acknowledgment

This research was sponsored by the Russian Foundation for Basic Research under the projects: № 16-07-01113, № 16-07-00886, № 16-07-01111 and the Contract № 02.G25.31.0149 dated 01.12.2015 (Board of Education of Russia).

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Correspondence to Elena N. Stankova .

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Stankova, E.N., Balakshiy, A.V., Petrov, D.A., Shorov, A.V., Korkhov, V.V. (2016). Using Technologies of OLAP and Machine Learning for Validation of the Numerical Models of Convective Clouds. In: Gervasi, O., et al. Computational Science and Its Applications -- ICCSA 2016. ICCSA 2016. Lecture Notes in Computer Science(), vol 9788. Springer, Cham. https://doi.org/10.1007/978-3-319-42111-7_36

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  • DOI: https://doi.org/10.1007/978-3-319-42111-7_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42110-0

  • Online ISBN: 978-3-319-42111-7

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