TL;DR
A long short-term memory (LSTM)-based framework for global three-dimensional electron density (Ne) modeling, using long-term multi-GNSS radio occultation observations with solar and geomagnetic activity indices is developed, with LSTM yielding the lowest overall error.
Abstract Ionospheric Total Electron Content (TEC) forecasting during geomagnetic storms is crucial for reliable Global Navigation Satellite System (GNSS) operations. Traditional single-frequency ionospheric broadcast correction models (Klobuchar and NeQuick-G models) are likely to have large ionospheric prediction erro...
The results have shown that it is possible to model nonlinear temporal dependencies in geomagnetic observations using recurrent architectures, and the use of traditional baselines along with repeated-seeds and ablation tests makes the evaluation more thorough.
Global solar radiation is a fundamental component of the Earth's energy balance and plays a critical role in integrating renewable energy into electrical systems. However, its pronounced variability, particularly in high-altitude regions, constrains energy stability and complicates planning processes. This study evalua...
Traditional deep learning models for the ionosphere often rely on space weather indices as inputs, offering predictive capability but limited physical interpretability. We present SRON2NN, a physics‐motivated artificial neural network (ANN) that incorporates two geophysically motivated features: effective solar rad...
The results obtained indicate that both forecasting models achieve exceptional accuracy in one-step-ahead solar radiation prediction, with correlation coefficients exceeding 97.2%.
Experimental results demonstrated that decomposing PWV into linear and nonlinear components via wavelet transform can enhance prediction accuracy and among all the schemes tested, ARIMA-KOA-CNN-GRU-Attention performed the best, achieving a 52% RMSE reduction in comparison with the ARIMA-KOA-GRU method.
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