Ionospheric activity prediction using convolutional recurrent neural networks

Boulch, Alexandre; Cherrier, Noëlie; Castaings, Thibaut

doi:10.48550/arxiv.1810.13273

Cited by 6 publications

(5 citation statements)

References 26 publications

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“…Taking into account two closely related parameters: F10.7 and Ap, Sun et al (2017) proposed a model based on long short-term memory (LSTM) to predict ionospheric vertical TEC of Beijing. Boulch et al (2018) propose a Deep Neural Network based method to forecast a sequence of global TEC maps through inputting consecutive sequence of TEC maps without introducing any prior knowledge other than rotation periodicity of Earth. Using six input parameters (including Kp index, solar flux, longitude and latitude, day of year, and time of day), Pérez (2019) constructed a global TEC prediction model on account of multi-layer perceptron to forecast the global TEC in the next one to several days.…”

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confidence: 99%

Prediction of Global Ionospheric TEC Based on Deep Learning

Zhou

Liao

et al. 2022

Space Weather

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The accurate prediction of ionospheric Total Electron Content (TEC) is important for global navigation satellite systems (GNSS), satellite communications and other space communications applications. In this study, a prediction model of global IGS‐TEC maps are established based on testing several different long short‐term memory (LSTM) network (LSTM)‐based algorithms to explore a direction that can effectively alleviate the increasing error with prediction time. We find that a Multi‐step auxiliary algorithm based prediction model performs best. It can effectively predict the global ionospheric IGS‐TEC in the next 6 days (the mean absolute deviation (MAD) and root mean square error (RMSE) are 2.485 and 3.511 TECU, respectively) compared to the IRI (the MAD and RMSE are 4.248 and 5.593 TECU). The analyses of four geomagnetic storm events are completely separate from the time range of the training set, so as to further validate the performance of the model. The International Reference Ionosphere model is used as a reference for the performance of our predictive model, and a rotated persistence is estimated by time‐shift algorithm of IGS‐TEC. The result suggests that the Multi‐step auxiliary prediction model has a good generalization performance and can have a relatively good stability and low error during a geomagnetic storm and quiet time.

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confidence: 99%

Prediction of Global Ionospheric TEC Based on Deep Learning

Zhou

Liao

et al. 2022

Space Weather

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“…Hitherto, attempts to develop such models have usually been either constrained to particular geographic regions [7], or limited by computational complexity and difficulties in achieving convergence [8], [9], [10]. To address the former issue, we observe that an aggregated data source, such as NASA CDDIS' [11] holdings of final International GNSS Service (IGS) ionospheric products [12], could provide adequate spatial and temporal coverage to train a model over the whole Earth map.…”

Section: Introductionmentioning

confidence: 99%

Improvements to global ionospheric forecasting with a recurrent convolutional neural network

Dailey,

Pham

2024

Sensors and Systems for Space Applications XVII

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“…They integrated the convolutional operator into the LSTM network to learn the spatial and temporal features of input data. Then, some studies applied this architecture to forecast TEC maps (Boulch et al 2018;Liu et al 2022;Gao and Yao. 2023).…”

Section: Introductionmentioning

confidence: 99%

Effects of Different Feature Representations on Global Ionospheric TEC Model Based on Spatiotemporal Convolutions

Gao

Yao

Wang

2023

Preprint

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The Total Electron Content (TEC) is a commonly used parameter for characterizing the morphology of the ionosphere. In this paper, we propose a novel feature representation of the time, called the Day and Night Map (DNM), for improving the global ionospheric TEC model. The specific effects of different feature variables (e.g., TEC, DNM, solar and geomagnetic indices) on forecasting global TEC maps are also analyzed. We apply factorized 3D convolutions, a new form of spatiotemporal convolutions, to construct the global TEC model. The global ionospheric TEC maps with a 2-hour temporal resolution are provided by the Center for Orbit Determination in Europe (CODE). The data sets that are resampled by a data segmentation strategy are used to train and evaluate the model. Results show that the DNM has a significant improvement in forecasting global TEC maps compared to the model with a single TEC feature, especially in the middle and low latitudes where large-scale ionospheric TEC anomalies occur frequently. Solar and geomagnetic indices facilitate the prediction capability of the global TEC model, but more indices are not always better. We demonstrate that matching appropriate feature variables with the structural characteristic of the deep learning algorithm is significantly important.

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Ionospheric activity prediction using convolutional recurrent neural networks

Cited by 6 publications

References 26 publications

Prediction of Global Ionospheric TEC Based on Deep Learning

Prediction of Global Ionospheric TEC Based on Deep Learning

Improvements to global ionospheric forecasting with a recurrent convolutional neural network

Effects of Different Feature Representations on Global Ionospheric TEC Model Based on Spatiotemporal Convolutions

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