Domain Adaptation of Spacecraft Data in Neural Network Prediction of Geomagnetic Dst Index

Авторы: Karimov Elvir Z., Shirokiy Vladimir R., Barinov Oleg G., Myagkova Irina N.
Сборник: Advances in Neural Computation, Machine Learning, and Cognitive Research VII
Серия: Studies in Computational Intelligence
Том: 1120
Год издания: 2023
Издательство: Springer Nature
Местоположение издательства: Switzerland
Первая страница: 398
Последняя страница: 405
DOI: 10.1007/978-3-031-44865-2_43
Аннотация: This study focuses on improving the neural network prediction of the geomagnetic indexes, in particular Dst-index, in a scenario, where input data is collected by two spacecraft (SC) with different data availability. One of the SC is approaching the end of its operational lifespan, while the other one lacks sufficient data history for constructing a high-quality neural network prediction. To effectively perform the transition between the two SC data, domain adaptation methods are needed. The study evaluates and compares various data translation techniques and optimizes the parameters for each translated feature to minimize domain discrepancies. The findings highlight the enhancement in the forecast, when employing domain adaptation methods and selecting relevant features, surpassing the results obtained using untranslated data.
Добавил в систему: Доленко Сергей Анатольевич

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