Ionospheric activity prediction using convolutional recurrent neural networks (2018-10-31T00:00:00.000000Z)

TL;DR

This work proposes a method based on Deep Neural Networks (DNN) to forecast a sequence of global TEC maps consecutive to an input sequence of T EC maps, without introducing any prior knowledge other than Earth rotation periodicity.

Abstract

The ionosphere electromagnetic activity is a major factor of the quality of satellite telecommunications, Global Navigation Satellite Systems (GNSS) and other vital space applications. Being able to forecast globally the Total Electron Content (TEC) would enable a better anticipation of potential performance degradations. A few studies have proposed models able to predict the TEC locally, but not worldwide for most of them. Thanks to a large record of past TEC maps publicly available, we propose a method based on Deep Neural Networks (DNN) to forecast a sequence of global TEC maps consecutive to an input sequence of TEC maps, without introducing any prior knowledge other than Earth rotation periodicity. By combining several state-of-the-art architectures, the proposed approach is competitive with previous works on TEC forecasting while predicting the TEC globally.

Authors

Alexandre Boulch

2 papers

Noëlie Cherrier

1 papers

T. Castaings

1 papers

TL;DR

Abstract

Authors

References35 items

On Regression Losses for Deep Depth Estimation

Deep Sequence-to-Sequence Neural Networks for Ionospheric Activity Map Prediction

Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image

Learning Deep CNN Denoiser Prior for Image Restoration

Mask R-CNN

FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks

Improving ionospheric forecasting using statistical method for accurate GPS positioning over Malaysia

Deep Residual Learning for Image Recognition

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

U-Net: Convolutional Networks for Biomedical Image Segmentation

Adam: A Method for Stochastic Optimization

Study of ionospheric TEC short-term forecast model based on combination method

Very Deep Convolutional Networks for Large-Scale Image Recognition

Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation

Ionospheric single‐station TEC short‐term forecast using RBF neural network

Ionosphere TEC prediction based on Chaos

Observations of low‐elevation ionospheric anomalies for ground‐based augmentation of GNSS

A new global TEC model for estimating transionospheric radio wave propagation errors

Ionospheric TEC short-term forecasting in CHINA

Ionospheric Threat Parameterization for Local Area GPS-Based Aircraft Landing Systems

Modeling and prediction of ionospheric total electron content by time series analysis

Natural Image Denoising with Convolutional Networks

Total electron content (TEC) forecasting by Cascade Modeling: A possible alternative to the IRI‐2001

Forecasting total electron content maps by neural network technique

An investigation into the influence of tidal forcing on F region equatorial vertical ion drift using a global ionosphere‐thermosphere model with coupled electrodynamics

Coronal mass ejections: origins, evolution, and role in space weather

Long Short-Term Memory

Finding Structure in Time

Effects of Solar Activity on the Ionosphere and Radio Communications

FORECASTING IONOSPHERIC TOTAL ELECTRON CONTENT MAPS WITH DEEP NEURAL NETWORKS

Deep Learning

Development of algorithms and software for forecasting, nowcasting and variability of TEC

GradientBased Learning Applied to Document Recognition

Gradient-based learning applied to document recognition

Field of Study

Journal Information

Name

Volume

Venue Information

Name

Type

URL

Alternate Names