Deep Neural Networks With Convolutional and LSTM Layers for SYM‐H and ASY‐H Forecasting
Abstract Geomagnetic indices quantify the disturbance caused by the solar activity on a planetary scale or in particular regions of the Earth. Among them, the SYM‐H and ASY‐H indices represent the (longitudinally) symmetric and asymmetric geomagnetic disturbance of the horizontal component of the ma...
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| Format: | Article |
| Language: | English |
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Wiley
2021-06-01
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| Series: | Space Weather |
| Online Access: | https://doi.org/10.1029/2021SW002748 |
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| author | Armando Collado‐Villaverde Pablo Muñoz Consuelo Cid |
| author_facet | Armando Collado‐Villaverde Pablo Muñoz Consuelo Cid |
| author_sort | Armando Collado‐Villaverde |
| collection | DOAJ |
| description | Abstract Geomagnetic indices quantify the disturbance caused by the solar activity on a planetary scale or in particular regions of the Earth. Among them, the SYM‐H and ASY‐H indices represent the (longitudinally) symmetric and asymmetric geomagnetic disturbance of the horizontal component of the magnetic field at midlatitude with a 1‐min resolution. Their resolution, along with their relation to the solar wind parameters, makes the forecasting of the geomagnetic indices a problem that can be addressed through the use of Deep Learning, particularly using Deep Neural Networks (DNNs). In this work, we present two DNNs developed to forecast respectively the SYM‐H and ASY‐H indices. Both networks have been trained using the Interplanetary Magnetic Field (IMF) and the related index for the solar storms occurred in the last two solar cycles. As a result, the networks are able to accurately forecast the indices 2 h in advance, considering the IMF and indices values for the previous 200 min. The evaluation of both networks reveals a great forecasting precision, including good predictions for large storms that occurred during the solar cycle 23 and comparing with the persistence model for the period 2013–2020. |
| format | Article |
| id | doaj-art-9973670b1b1448e0bfe2d3b3d0126874 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2021-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-9973670b1b1448e0bfe2d3b3d01268742025-01-14T16:30:36ZengWileySpace Weather1542-73902021-06-01196n/an/a10.1029/2021SW002748Deep Neural Networks With Convolutional and LSTM Layers for SYM‐H and ASY‐H ForecastingArmando Collado‐Villaverde0Pablo Muñoz1Consuelo Cid2Departamento de Automática Escuela Politécnica Superior, Universidad de Alcalá Alcalá de Henares Madrid SpainDepartamento de Automática Escuela Politécnica Superior, Universidad de Alcalá Alcalá de Henares Madrid SpainDepartamento de Física y Matemáticas Universidad de Alcalá Alcalá de Henares Madrid SpainAbstract Geomagnetic indices quantify the disturbance caused by the solar activity on a planetary scale or in particular regions of the Earth. Among them, the SYM‐H and ASY‐H indices represent the (longitudinally) symmetric and asymmetric geomagnetic disturbance of the horizontal component of the magnetic field at midlatitude with a 1‐min resolution. Their resolution, along with their relation to the solar wind parameters, makes the forecasting of the geomagnetic indices a problem that can be addressed through the use of Deep Learning, particularly using Deep Neural Networks (DNNs). In this work, we present two DNNs developed to forecast respectively the SYM‐H and ASY‐H indices. Both networks have been trained using the Interplanetary Magnetic Field (IMF) and the related index for the solar storms occurred in the last two solar cycles. As a result, the networks are able to accurately forecast the indices 2 h in advance, considering the IMF and indices values for the previous 200 min. The evaluation of both networks reveals a great forecasting precision, including good predictions for large storms that occurred during the solar cycle 23 and comparing with the persistence model for the period 2013–2020.https://doi.org/10.1029/2021SW002748 |
| spellingShingle | Armando Collado‐Villaverde Pablo Muñoz Consuelo Cid Deep Neural Networks With Convolutional and LSTM Layers for SYM‐H and ASY‐H Forecasting Space Weather |
| title | Deep Neural Networks With Convolutional and LSTM Layers for SYM‐H and ASY‐H Forecasting |
| title_full | Deep Neural Networks With Convolutional and LSTM Layers for SYM‐H and ASY‐H Forecasting |
| title_fullStr | Deep Neural Networks With Convolutional and LSTM Layers for SYM‐H and ASY‐H Forecasting |
| title_full_unstemmed | Deep Neural Networks With Convolutional and LSTM Layers for SYM‐H and ASY‐H Forecasting |
| title_short | Deep Neural Networks With Convolutional and LSTM Layers for SYM‐H and ASY‐H Forecasting |
| title_sort | deep neural networks with convolutional and lstm layers for sym h and asy h forecasting |
| url | https://doi.org/10.1029/2021SW002748 |
| work_keys_str_mv | AT armandocolladovillaverde deepneuralnetworkswithconvolutionalandlstmlayersforsymhandasyhforecasting AT pablomunoz deepneuralnetworkswithconvolutionalandlstmlayersforsymhandasyhforecasting AT consuelocid deepneuralnetworkswithconvolutionalandlstmlayersforsymhandasyhforecasting |