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

Abstract

Forecasting of total electron content (TEC)/global positioning system (GPS) signal delays in storm conditions is considered the most challenging task for accurate position estimation, especially in critical applications. Therefore, a storm-time ionospheric model is proposed to forecast TEC based on the artificial neural network (ANN) using singular spectrum analysis (SSA). The study area covers four Global Navigation Satellite System (GNSS) stations located in the low-latitude and two GNSS stations located in the mid-latitude ionosphere. The geographical area extends between 11–43°N latitude and 77–93°E longitude. The selection of GPS–TEC data is based on the storm criterion of Dst ≤ −50 nT, and the storm day data sets from 2009 to 2017 period are used for model development. The proposed algorithm is tested with the GPS–TEC data sets of three geomagnetic storm days (i) severe, (ii) moderate storm and (iii) strong at low and mid-latitudes. The average precision and mean absolute error of the proposed SSA-ANN model is 1.41 and 1.06 TECU (strong storm), respectively. The prediction performance of the proposed SSA-ANN model is compared with the standard principal component analysis-ANN model. The improvement factor of the SSA-ANN is improved by 43.82%.