Interplanetary (IP) shocks are perturbations observed in the solar wind. IP shocks correlate
well with solar activity, being more numerous during times of high sunspot numbers. Earth …

To further improve the short-term forecasting ability of the critical frequency of the
ionosphere F2 layer (foF2), a sample entropy (SE) optimized deep learning (DL) long-short …

To achieve accurate forecasting of the peak height of the ionospheric F2 layer (hmF2), we
propose a hybrid deep learning model of improved seagull optimization algorithm (ISOA) …

In this work, we present an Artificial Neural Network for operational forecasting of the SYM‐H
geomagnetic index up to 2 hr ahead using the Interplanetary Magnetic Field, the solar wind …

Since ionospheric variability changes dramatically before the major earthquakes (EQ), the
detection of ionospheric anomalies for EQ forecasting has been a hot topic for modern-day …

Over the past 10 years, a paradigm shift has happened in the world of science and
information technology. Open science is becoming the de facto standard, as underlined by …

Abstract We propose a Multi-Network-based Hybrid Long Short Term Memory (N-LSTM)
model for ionospheric anomaly detection to forecast highly irregular data of the ionospheric …

The transmission of high-frequency signals over long distances depends on the
ionosphere's reflective properties, with the selection of operating frequencies being closely …

Abstract Total Electron Content (TEC) forecasting using machine learning has been
extensively preferred in characterizing the spatio-temporal variability of the ionosphere, to …

The total electron content (TEC) of the ionosphere at low latitudes is significantly influenced
by solar-geomagnetic activity and seasonal variations. Traditional ionospheric models often …