Ensemble machine learning of random forest, AdaBoost and XGBoost for vertical total electron content forecasting
Space weather describes varying conditions between the Sun and Earth that can degrade
Global Navigation Satellite Systems (GNSS) operations. Thus, these effects should be …
Global Navigation Satellite Systems (GNSS) operations. Thus, these effects should be …
Long short‐term memory neural network for ionospheric total electron content forecasting over China
An increasing number of terrestrial‐and space‐based radio‐communication systems are
influenced by the ionospheric space weather, making the ionospheric state increasingly …
influenced by the ionospheric space weather, making the ionospheric state increasingly …
New capabilities for prediction of high‐latitude ionospheric scintillation: A novel approach with machine learning
As societal dependence on transionospheric radio signals grows, space weather impact on
these signals becomes increasingly important yet our understanding of the effects remains …
these signals becomes increasingly important yet our understanding of the effects remains …
Predicting interplanetary shock occurrence for solar cycle 25: Opportunities and challenges in space weather research
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 …
well with solar activity, being more numerous during times of high sunspot numbers. Earth …
A storm-time ionospheric TEC model with multichannel features by the spatiotemporal ConvLSTM network
X Gao, Y Yao - Journal of Geodesy, 2023 - Springer
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 …
of the ionosphere. Modeling the ionospheric TEC accurately during the storm time could …
Using TensorFlow-based Neural Network to estimate GNSS single frequency ionospheric delay (IONONet)
RO Perez - Advances in Space Research, 2019 - Elsevier
In the last 20 years, and in particular in the last decade, the availability of propagation data
for GNSS has increased substantially. In this sense, the ionosphere has been sounded with …
for GNSS has increased substantially. In this sense, the ionosphere has been sounded with …
Implementation of hybrid ionospheric TEC forecasting algorithm using PCA-NN method
IL Mallika, DV Ratnam, Y Ostuka… - IEEE Journal of …, 2018 - ieeexplore.ieee.org
Forecasting the ionospheric space weather is crucial for improving the accuracy of the
global navigation satellite systems (GNSS). Nonetheless, comprehending the …
global navigation satellite systems (GNSS). Nonetheless, comprehending the …
Deep learning‐based prediction of global ionospheric tec during storm periods: Mixed cnn‐bilstm method
X Ren, B Zhao, Z Ren, Y Wang, B **ong - Space Weather, 2024 - Wiley Online Library
The application of deep learning in high‐precision ionospheric parameter prediction has
become one of the focus in space weather research. In this study, an improved model called …
become one of the focus in space weather research. In this study, an improved model called …
Machine learning based storm time modeling of ionospheric vertical total electron content over Ethiopia
Geomagnetic storms can cause variations in the ionization levels of the ionosphere, which is
commonly studied using the total electron content (TEC). TEC is a crucial parameter to …
commonly studied using the total electron content (TEC). TEC is a crucial parameter to …
[HTML][HTML] Ionospheric TEC Prediction in China during Storm Periods Based on Deep Learning: Mixed CNN-BiLSTM Method
X Ren, B Zhao, Z Ren, B **ong - Remote Sensing, 2024 - mdpi.com
Applying deep learning to high-precision ionospheric parameter prediction is a significant
and growing field within the realm of space weather research. This paper proposes an …
and growing field within the realm of space weather research. This paper proposes an …