Machine Learning-based Investigation of Feature Importance for High-latitude Ionospheric Scintillation Forecasting

Alexis J. Wu and Yunxiang Liu

Peer Reviewed

Abstract: This paper introduces a machine learning approach to investigate the feature importance for scintillation forecasting. Here, the features are historical measurements used as input for machine learning models. We propose to use gradient boosting as the machine learning algorithm to conduct a scintillation forecasting task at high latitudes. Once the gradient boosting model is trained, the rank of feature importance can be obtained. The preliminary results show that the top 10 most important features indeed are correlated with the future occurrence of scintillation. The feature importance ranking has the potential to guide feature selection for machine learning-based scintillation forecasting and improve forecasting performance. In addition, the feature importance list could also provide insights on the investigation of the complex coupling between solar wind and ionospheric disturbance.
Published in: Proceedings of the 2021 International Technical Meeting of The Institute of Navigation
January 25 - 28, 2021
Pages: 637 - 647
Cite this article: Wu, Alexis J., Liu, Yunxiang, "Machine Learning-based Investigation of Feature Importance for High-latitude Ionospheric Scintillation Forecasting," Proceedings of the 2021 International Technical Meeting of The Institute of Navigation, , January 2021, pp. 637-647.
https://doi.org/10.33012/2021.17855
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