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Machine Learning Methodology for Ionosphere Total Electron Content Nowcasting

Aleksei Zhukov, Denis Sidorov, Anna Mylnikova, Yury Yasyukevich

Abstract



The dynamics of ionospheric parameters forecasting and nowcasting are actual and rather challenging tasks. The feature selection is the principal challenge for the accurate nowcasting models construction. The data-driven machine learning methodology for ionosphere total electron content (TEC) is proposed in this paper. As the experimental data, the vertical absolute TEC was used. The time resolution of the data is 30 minutes. Initial phase and psueduorange slant TEC were recorded by the mid-latitude station IRKJ (52 N, 104 E) in 2014. The approach revealed that current TEC, first time derivative of TEC, cosine from local time LT, current F10.7 and SYM/H indexes, exponential moving averages of TEC (with 12, 24, 96 hour periods), 12h-lagged, 2-days and 15-days lagged F10.7 are the significant features for vertical TEC 4-hour nowcasting model. Based on selected features six models have been constructed. Three models were based on machine learning approach (random forest, support vector regression, and gradient boosting), one was based on conventional
least squares linear regression, and two naive models were used. All the models were evaluated and testing results comparison provided. Machine learning based models allow
us to achive small RMSE 2 TECU, linear regression model based on significant features results in 4 TECU, while naive models results to huge RMSE.

Keywords


regression, nowcasting, total electron content, random forest, SVM, feature selection, time series, feature extraction

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