Meteorological data analysis using extreme learning machines

I. A. Botygin; Yu. V. Volkov; V. S. Sherstnev; A. I. Sherstneva

doi:10.1117/12.2690069

17 October 2023 Meteorological data analysis using extreme learning machines

I. A. Botygin, Yu. V. Volkov, V. S. Sherstnev, A. I. Sherstneva

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Proceedings Volume 12780, 29th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics; 1278073 (2023) https://doi.org/10.1117/12.2690069
Event: XXIX International Symposium "Atmospheric and Ocean Optics, Atmospheric Physics", 2023, Moscow, Russian Federation

Abstract

A practical study of statistical modelling language packages R has been carried out using regularization algorithms, more precisely one of the algorithms called the Extreme Learning Machine (ELM). Due to its simple implementation, ELM requires less researcher intervention in setting its parameters. At the same time, the generalization performance of ELM is not sensitive to the dimensionality of the feature space (the number of hidden nodes). Even on a medium-power personal computer, this class of neural networks has made it possible to perform numerous experiments on model building, forecasting and identifying cause-effect relationships in meteorological time series, downloaded from the climate monitoring system of IMCES SB RAS in a reasonable amount of time.

Citation Download Citation

I. A. Botygin, Yu. V. Volkov, V. S. Sherstnev, and A. I. Sherstneva "Meteorological data analysis using extreme learning machines", Proc. SPIE 12780, 29th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics, 1278073 (17 October 2023); https://doi.org/10.1117/12.2690069

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KEYWORDS

Neural networks

Extreme learning machines

Neurons

Machine learning

Meteorology

Education and training

Wind speed

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