Open Access Open Access  Restricted Access Subscription or Fee Access

Machine Learning Methodology for Ionosphere Total Electron Content Nowcasting

Aleksei Zhukov, Denis Sidorov, Anna Mylnikova, Yury Yasyukevich


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.


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

Full Text:


Disclaimer/Regarding indexing issue:

We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information. Also: DOI is paid service which provided by a third party. We never mentioned that we go for this for our any journal. However, journal have no objection if author go directly for this paid DOI service.