Recently deep learning (DL), as a new data‐driven technique compared to conventional
approaches, has attracted increasing attention in geophysical community, resulting in many …

Learning from successful applications of methods originating in statistical mechanics,
complex systems science, or information theory in one scientific field (eg, atmospheric …

Space weather describes varying conditions between the Sun and Earth that can degrade
Global Navigation Satellite Systems (GNSS) operations. Thus, these effects should be …

The application of deep learning technology to ionospheric prediction has become a new
research hotspot. However, there are still some gaps, such as the prediction effect with …

In this paper, we proposed an innovative encoder‐decoder structure with a convolution long
short‐term memory (ED‐ConvLSTM) network to forecast global total electron content (TEC) …

Magnetohydrodynamics (MHD)-based global space weather models have mostly been
developed and maintained at academic institutions. While the “free spirit” approach of …

This paper applies the convolutional long short‐term memory (convLSTM)‐based machine
learning models to forecast global ionospheric total electron content (TEC) maps with up to …

This paper provides the application of deep learning models such as Long Short-Term
Memory (LSTM) and a recently proposed Gated Recurrent Unit (GRU) in forecasting the …

Water, a renewable but limited resource, is vital for all living creatures. Increasing demand
makes the sustainability of water resources crucial. River flow management, one of the key …

The total electron content (TEC) is an important parameter for characterizing the morphology
of the ionosphere. Modeling the ionospheric TEC accurately during the storm time could …