Ionospheric parameter modelling and anomaly discovery by combining the wavelet transform with autoregressive models

Mandrikova, Oksana; Fetisova, Nadezda V.; Al-Kasasbeh, Riad Taha; Klionskiy, Dmitry M.; Geppener, V. V.; Ilyash, M.Yu.

doi:10.4401/ag-6729

Cited by 8 publications

(2 citation statements)

References 48 publications

(76 reference statements)

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“…Encoder: This module consists of a combination of three paired ConvLSTM layers and BatchNorm layers that work together to extract the spatiotemporal features of the input TEC map sequence. This module receives input samples, each containing 7 consecutive days of TEC map within the study area, with dimensions (84, 15,9), where 84 represents a total of 84 TEC maps within 7 days, and 15 and 9 represent the number of grid points in the study area. Then, the combination of ConvLSTM and BatchNorm is used to extract the features of the input TEC map sequence.…”

Section: Implementation Detailsmentioning

confidence: 99%

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Spatiotemporal Prediction of Ionospheric Total Electron Content Based on ED-ConvLSTM

Liu

et al. 2023

Remote Sensing

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Total electron content (TEC) is a vital parameter for describing the state of the ionosphere, and precise prediction of TEC is of great significance for improving the accuracy of the Global Navigation Satellite System (GNSS). At present, most deep learning prediction models just consider TEC temporal variation, while ignoring the impact of spatial location. In this paper, we propose a TEC prediction model, ED-ConvLSTM, which combines convolutional neural networks with recurrent neural networks to simultaneously consider spatiotemporal features. Our ED-ConvLSTM model is built based on the encoder-decoder architecture, which includes two modules: encoder module and decoder module. Each module is composed of ConvLSTM cells. The encoder module is used to extract the spatiotemporal features from TEC maps, while the decoder module converts spatiotemporal features into predicted TEC maps. We compared the predictive performance of our model with two traditional time series models: LSTM, GRU, a spatiotemporal mode1 ConvGRU, and the TEC daily forecast product C1PG provided by CODE on a total of 135 grid points in East Asia (10°–45°N, 90°–130°E). The experimental results show that the prediction error indicators MAE, RMSE, MAPE, and prediction similarity index SSIM of our model are superior to those of the comparison models in high, normal, and low solar activity years. The paper also analyzed the predictive performance of each model monthly. The experimental results indicate that the predictive performance of each model is influenced by the monthly mean of TEC. The ED-ConvLSTM model proposed in this paper is the least affected and the most stable by the monthly mean of TEC. Additionally, the paper compared the predictive performance of each model during two magnetic storm periods when TEC changes sharply. The results indicate that our ED-ConvLSTM model is least affected during magnetic storms and its predictive performance is superior to those of the comparative models. This paper provides a more stable and high-performance TEC spatiotemporal prediction model.

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Section: Implementation Detailsmentioning

confidence: 99%

“…The model incorporated various features, including solar extreme ultraviolet (EUV), geomagnetic activity, and so on. Mandrikova et al [15] proposed a TEC prediction model based on a combination of wavelet transform and ARIMA. Kutubuddin et al [16] used a linear time series model to predict TEC at ten sites in Japan.…”

Section: Introductionmentioning

confidence: 99%