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Интеллектуальная Система Тематического Исследования НАукометрических данных |
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Investigation of Parameters of Meteorological Models Based on Patternsстатья
Информация о цитировании статьи получена из
Scopus
Дата последнего поиска статьи во внешних источниках: 24 ноября 2018 г.
- Автор: Gorshenin A.K.
- Сборник: CEUR Workshop Proceedings
- Том: 2177
- Год издания: 2018
- Первая страница: 4
- Последняя страница: 10
- Аннотация: The probabilistic characteristics and the forecasts for precipitation on the basis of a special transformation of the initial data, which makes it possible to reveal patterns in observations, are briefly discussed. Patterns in data analysis can be used to improve the accuracy and speed of forecasting. Moreover, pattern’s methodology is a convenient approach to the solution of various climatological problems. The issues of testing the Markov property of data, probabilistic and neural network forecasting for statistical observations without involvement of any additional information about meteorological conditions are investigated. The initial data is volumes of daily precipitation observed during 60 years. The best accuracy for neural networks trained on patterns based on sequences of «D» (dry days, i.e. without precipitations) and «W» (wet ones, i.e. with any nonzero volume) is 97% for one-day and 89% for two-day forecasts. Few directions for further investigations are suggested. The paper continues the author’s research in the fields of creation of mathematical models and data mining algorithms for meteorological observations.
- Добавил в систему: Горшенин Андрей Константинович