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- W3159453823 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." @default.
- W3159453823 created "2021-05-10" @default.
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- W3159453823 date "2021-06-01" @default.
- W3159453823 modified "2023-10-13" @default.
- W3159453823 title "Deep Neural Networks With Convolutional and LSTM Layers for SYM‐H and ASY‐H Forecasting" @default.
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- W3159453823 doi "https://doi.org/10.1029/2021sw002748" @default.
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