Comparative Efficiency of Prediction of Relativistic Electron Flux in the Near-Earth Space Using Various Machine Learning Methods

Авторы: Myagkova Irina, Shirokii Vladimir, Vladimirov Roman, Barinov Oleg, Dolenko Sergey
Сборник: Advances in Neural Computation, Machine Learning, and Cognitive Research IV
Серия: Studies in Computational Intelligence
Том: 925
Год издания: 2021
Первая страница: 222
Последняя страница: 227
DOI: 10.1007/978-3-030-60577-3_25
Аннотация: This paper performs comparative analysis of the prediction quality for the time series of hourly average flux of relativistic electrons (E > 2 meV) in the near-Earth space 1 to 24 h ahead by various machine learning methods. As inputs, all the predictive models used hourly average values of the parameters of solar wind and interplanetary magnetic field measured at the Lagrange point between the Sun and the Earth, the values of Dst and Kp geomagnetic indexes, and the values of the flux of relativistic electrons itself. The machine learning methods used for prediction were the multi-layer perceptron (MLP) type artificial neural networks, the decision tree (random forest) method, and gradient boosting. A comparison of the quality indicators of short-term forecasts with a horizon of one to 24 h showed that the best results were demonstrated by the MLP. The horizon of satisfactory forecast accuracy on independent data is 9 h, the horizon of acceptable accuracy is 12 h.
Добавил в систему: Доленко Сергей Анатольевич

	ИСТИНА	Войти в систему Регистрация
	ФНКЦ РР
	Главная Поиск Статистика О проекте Помощь

ИСТИНА