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Title: MACHINE LEARNING METHODS IN WEATHER FORECASTS
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MACHINE LEARNING METHODS IN WEATHER FORECASTS
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1314-2704
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English
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19
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2.1
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Growth in computational performance, the amount of accumulated data about the environment and experience of handling such amounts of data leads to an increase in the number of applications of data analysis. Weather forecasting is one of these areas. Weather forecasting uses a variety of data and meteorological models describing the physical processes in the atmosphere. Machine learning algorithms can correct some errors of these models and improve weather forecasts. To improve the temperature forecast, we added to the training data for different models readings from the nearest meteorological stations. This technique proved to be useful for the short-term temperature prediction. We evaluated the results of experiments by the changing of the root-mean-square error. Yandex.Weather service successfully uses the improved model for forecasting. The described technique has already been added to the Yandex.Weather and used on an ongoing basis in production. In addition, we suggest possible directions of this task development.
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conference
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19th International Multidisciplinary Scientific GeoConference SGEM 2019
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19th International Multidisciplinary Scientific GeoConference SGEM 2019, 30 June - 6 July, 2019
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Proceedings Paper
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STEF92 Technology
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International Multidisciplinary Scientific GeoConference-SGEM
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Bulgarian Acad Sci; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Russian Acad Sci; Serbian Acad Sci & Arts; Slovak Acad Sci; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; World Acad Sci; European Acad Sci, Arts & Letters; Ac
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391-398
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30 June - 6 July, 2019
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website
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cdrom
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5373
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weather prediction; machine learning; gradient boosting
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