Ionosphere

Physics

Geology

Magnetic field

Geodesy

Total electron content

Artificial intelligence

Earth's magnetic field

International Reference Ionosphere

Computer science

Global Positioning System

Quantum mechanics

Artificial neural network

Remote sensing

Telecommunications

Geophysics

IEEE Geoscience and Remote Sensing Letters

This letter proposes the implementation of ionospheric forecasting model based on the long short-term memory (LSTM) networks. Ionospheric region produces time delay for radio wave propagation of global positioning system (GPS) satellites. The ionospheric delays for GPS signals degrade the position accuracy in the measurements for precise navigation and positioning services. Utilizing the emerging artificial intelligence mathematical tools to forecast ionospheric disturbances using GPS-estimated total electron content (TEC) observations is decisive. In this letter, multi-input LSTM forecasting technique is investigated and tested for evaluating its capability in forecasting the ionospheric delays over Bengaluru station (16.26° N, 80.44° E) using eight years (2009-2016) of GPS measured vertical TEC (VTEC) time-series data. The assessment of the LSTM model performance during geomagnetic quiet and disturbed conditions is carried out in comparison with artificial neural networks model and International Reference Ionosphere (IRI-2016) model based on statistical parameters like root-mean-square error and coefficient of determination (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). The experimental analysis delineates that the proposed LSTM model has provided the correlation of 0.99 with the GPS-measured VTEC and with a forecasting error of 1-2 TEC units.

A Deep Learning-Based Approach to Forecast Ionospheric Delays for GPS Signals