Kp forecasting with a recurrent neural network
In an effort to forecast the planetary Kp-index beyond the current 1-hour and 4-hour predictions, a recurrent neural network is trained on three decades of historical data from NASA’s Omni virtual observatory and forecasts Kp with a prediction horizon of up to 24 h. Using Matlab’s neural network too...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2019-01-01
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| Series: | Journal of Space Weather and Space Climate |
| Subjects: | |
| Online Access: | https://www.swsc-journal.org/articles/swsc/full_html/2019/01/swsc180037/swsc180037.html |
| _version_ | 1818675408317448192 |
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| author | Sexton Ernest Scott Nykyri Katariina Ma Xuanye |
| author_facet | Sexton Ernest Scott Nykyri Katariina Ma Xuanye |
| author_sort | Sexton Ernest Scott |
| collection | DOAJ |
| description | In an effort to forecast the planetary Kp-index beyond the current 1-hour and 4-hour predictions, a recurrent neural network is trained on three decades of historical data from NASA’s Omni virtual observatory and forecasts Kp with a prediction horizon of up to 24 h. Using Matlab’s neural network toolbox, the multilayer perceptron model is trained on inputs comprised of Kp for a given time step as well as from different sets of the following six solar wind parameters, Bz, n, V, |B|, σB and
σ
B
z
$ {\sigma }_{{B}_z}$
. The purpose of this study was to test which combination of the solar wind and Interplanetary Magnetic Field (IMF) parameters used for training gives the best performance as defined by correlation coefficient, C, between the predicted and actually measured Kp values and Root Mean Square Error (RMSE). The model consists of an input layer, a single nonlinear hidden layer with 28 neurons, and a linear output layer that predicts Kp up to 24 h in advance. For 24 h prediction, the network trained on Bz, n, V, |B|, σB performs the best giving C in the range from 0.8189 (for 31 predictions) to 0.8211 (for 9 months of predictions), with the smallest RMSE. |
| first_indexed | 2024-12-17T08:27:06Z |
| format | Article |
| id | doaj.art-12aa824a95db41918c193ae06dd504fd |
| institution | Directory Open Access Journal |
| issn | 2115-7251 |
| language | English |
| last_indexed | 2024-12-17T08:27:06Z |
| publishDate | 2019-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | Journal of Space Weather and Space Climate |
| spelling | doaj.art-12aa824a95db41918c193ae06dd504fd2022-12-21T21:56:42ZengEDP SciencesJournal of Space Weather and Space Climate2115-72512019-01-019A1910.1051/swsc/2019020swsc180037Kp forecasting with a recurrent neural networkSexton Ernest ScottNykyri KatariinaMa XuanyeIn an effort to forecast the planetary Kp-index beyond the current 1-hour and 4-hour predictions, a recurrent neural network is trained on three decades of historical data from NASA’s Omni virtual observatory and forecasts Kp with a prediction horizon of up to 24 h. Using Matlab’s neural network toolbox, the multilayer perceptron model is trained on inputs comprised of Kp for a given time step as well as from different sets of the following six solar wind parameters, Bz, n, V, |B|, σB and σ B z $ {\sigma }_{{B}_z}$ . The purpose of this study was to test which combination of the solar wind and Interplanetary Magnetic Field (IMF) parameters used for training gives the best performance as defined by correlation coefficient, C, between the predicted and actually measured Kp values and Root Mean Square Error (RMSE). The model consists of an input layer, a single nonlinear hidden layer with 28 neurons, and a linear output layer that predicts Kp up to 24 h in advance. For 24 h prediction, the network trained on Bz, n, V, |B|, σB performs the best giving C in the range from 0.8189 (for 31 predictions) to 0.8211 (for 9 months of predictions), with the smallest RMSE.https://www.swsc-journal.org/articles/swsc/full_html/2019/01/swsc180037/swsc180037.htmlneural networkspace weathermachine learning |
| spellingShingle | Sexton Ernest Scott Nykyri Katariina Ma Xuanye Kp forecasting with a recurrent neural network Journal of Space Weather and Space Climate neural network space weather machine learning |
| title | Kp forecasting with a recurrent neural network |
| title_full | Kp forecasting with a recurrent neural network |
| title_fullStr | Kp forecasting with a recurrent neural network |
| title_full_unstemmed | Kp forecasting with a recurrent neural network |
| title_short | Kp forecasting with a recurrent neural network |
| title_sort | kp forecasting with a recurrent neural network |
| topic | neural network space weather machine learning |
| url | https://www.swsc-journal.org/articles/swsc/full_html/2019/01/swsc180037/swsc180037.html |
| work_keys_str_mv | AT sextonernestscott kpforecastingwitharecurrentneuralnetwork AT nykyrikatariina kpforecastingwitharecurrentneuralnetwork AT maxuanye kpforecastingwitharecurrentneuralnetwork |