Multi-class classification of ionospheric scintillations using SMOTE-Super Learner ensemble technique

Abstract

Ionospheric scintillation is a phenomenon that influences radio waves from the Global Navigation Satellite System (GNSS) in the ionosphere, reducing the accuracy, integrity, and continuity of tracking and navigation applications. Automatic and accurate detection of scintillation events based on threat level is essential for space weather forecasting applications. Therefore, a multi-class classification of four categories based on scintillation intensities was developed. In this paper, a Synthetic Minority Oversampling Technique (SMOTE) based Super Learner (LR) machine learning classification algorithm is proposed with two months of GPS ionospheric amplitude scintillations S4 data obtained for 2015 from Hyderabad station (17.45°N, 78.47°E). The Synthetic Minority Oversampling Technique (SMOTE) is implemented to oversample the minority classes to balance the events in each class. Moreover, we also focused on annotating the data transmitted by all the visible satellites. Later, a machine learning method is proposed to achieve the automated detection of ionospheric scintillation to improve the detection performance and obtain the detected results for each category. The experimental results show that the proposed SL model approach considerably has detection accuracy on the classified data as 97.8% during the quiet day and 78.3% during storm day for the Hyderabad station. The confusion matrix results indicate the proposed algorithm’s effectiveness for quiet and disturbed conditions.