Implementation of storm‐time ionospheric forecasting algorithm using SSA–ANN model

Dabbakuti, J. R. K. Kumar; Peesapati, Rangababu; Yarrakula, Mallika; Anumandla, Kiran Kumar; Madduri, Sasi Vardhan

doi:10.1049/iet-rsn.2019.0551

Cited by 19 publications

(1 citation statement)

References 28 publications

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“…Gampala et al [14] used synchrosqueezing transform (SST) to improve the autoregressive moving average (ARMA) model for ionospheric TEC forecasting. Dabbakuti et al [15] used singular spectrum analysis to preprocess the input data, which were then fed into an ANN for ionospheric TEC prediction. Meanwhile, they developed a prediction system based on the combination of variational mode decomposition (VMD) and kernel extreme learning machine (KELM) for an ionospheric analysis of the internet of things [16].…”

Section: Introductionmentioning

confidence: 99%

An Ionospheric TEC Forecasting Model Based on a CNN-LSTM-Attention Mechanism Neural Network

Tanida

Ding

et al. 2022

Remote Sensing

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Ionospheric forecasts are critical for space-weather anomaly detection. Forecasting ionospheric total electron content (TEC) from the global navigation satellite system (GNSS) is of great significance to near-earth space environment monitoring. In this study, we propose a novel ionospheric TEC forecasting model based on deep learning, which consists of a convolutional neural network (CNN), long-short term memory (LSTM) neural network, and attention mechanism. The attention mechanism is added to the pooling layer and the fully connected layer to assign weights to improve the model. We use observation data from 24 GNSS stations from the Crustal Movement Observation Network of China (CMONOC) to model and forecast ionospheric TEC. We drive the model with six parameters of the TEC time series, Bz, Kp, Dst, and F10.7 indices and hour of day (HD). The new model is compared with the empirical model and the traditional neural network model. Experimental results show the CNN-LSTM-Attention neural network model performs well when compared to NeQuick, LSTM, and CNN-LSTM forecast models with a root mean square error (RMSE) and R2 of 1.87 TECU and 0.90, respectively. The accuracy and correlation of the prediction results remained stable in different months and under different geomagnetic conditions.

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Section: Introductionmentioning

confidence: 99%