Ionospheric Scintillation Prediction on S4 and ROTI Parameters Using Artificial Neural Network and Genetic Algorithm

Irregularities in electron density usually correlate with ionospheric plasma perturbations. These variations causing radio signal fluctuations, response, generate scintillations that frequently occur low-latitude regions. In this research, the combination of an artificial neural network (ANN) genetic algorithm (GA) was implemented to predict scintillations. The GA method considered for obtaining ANN model’s initial weights. This procedure applied GNSS observations at GUAM (13.58°E, 144.86°N, 201.922H) station daily prediction amplitude via predicting signal-to-noise ratio (S4) or rate TEC index (ROTI). Thirty-day modeling carried out three months January, March, and July, representing different seasons winter solstice, equinox, summer solstice during years, 2015, 2017, 2020, solar activities. models, along physical data, were used consequent day after modeling. results evaluated using S4 derived from station. designed model has ability accuracy about 81% 80% ROTI.