Machine Learning Approach for Solar Wind Categorization. Issue 5 (2nd May 2020)

• Record Type: Journal Article
• Authors: Li, Hui; Wang, Chi; Tu, Cui; Xu, Fei
• Is Part Of: Earth and space science. Volume 7:Issue 5(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-05-02
• Subjects: solar wind classification -- machine learning -- automatical method -- k‐nearest neighbors -- space weather early warning; Space sciences -- Periodicals; Geophysics -- Periodicals; 500.5
• DOI: 10.1029/2019EA000997
• Languages: English
• ISSNs: 2333-5084

Abstract

Abstract: Solar wind classification is conducive to understanding the ongoing physical processes at the Sun and in solar wind evolution in interplanetary space, and, furthermore, it is helpful for early warning of space weather events. With rapid developments in the field of artificial intelligence, machine learning approaches are increasingly being used for pattern recognition. In this study, an approach from machine learning perspectives is developed to automatically classify the solar wind at 1 AU into four types: coronal‐hole‐origin plasma, streamer‐belt‐origin plasma, sector‐reversal‐region plasma, and ejecta. By exhaustive enumeration, an eight‐dimensional scheme ( B T, N P, T P, V P, N α p, T exp / T P, S p, and M f ) is found to perform the best among 8, 191 combinations of 13 solar wind parameters. Ten popular supervised machine learning models, namely, k ‐nearest neighbors (KNN), Support Vector Machines with linear and radial basic function kernels, Decision Tree, Random Forest, Adaptive Boosting, Neural Network, Gaussian Naive Bayes, Quadratic Discriminant Analysis, and eXtreme Gradient Boosting, are applied to the labeled solar wind data sets. Among them, KNN classifier obtains the highest overall classification accuracy, 92.8%. Although the accuracy can be improved by 1.5% when O 7+ /O 6+ information is additionally considered, our scheme without composition measurements is still good enough for solar wind classification. In addition, two application examples indicate that solar wind classification is helpful for the risk evaluation of predicted magnetic storms and surface charging of geosynchronous spacecraft. Key Points: An eight‐dimensional scheme for four‐type solar wind categorization is developed based on 10 supervised machine learning classifiers Two application examples indicate that solar wind classification is useful for space weather early warning Although the accuracy drops by 1.5%, our scheme without composition information is good enough and has wide applicability