## 4 Results and Discussion
The input data for the designed GANN contain a two-dimensional matrix where each column represents the five types of geophysical parameters for the 8640 time step. The network then integrates these data into the training process and performs the modeling based on the GA integrated into the ANN (GANN). In the first step, we compared the results of S4 modeling by GANN for the modeling period of 30 days with the observations of these data at the same time interval. In order to measure the accuracy of the modeling, the parameters of root mean square error (RMSE) and R-square were used for measuring error rates and calculating regression, respectively. In addition, another parameter can be used to evaluate the accuracy of the model trained by the GANN model, by drawing the absolute vector of residuals between the performed modeling and the observational data. Table 1 presents the values of these parameters for the modeled S4 values. Additionally, the percentage of occurrence of ionospheric scintillation was used during the 30-day modeling period, which indicates the number of epochs with S4 > 0.2, thus indicating the occurrence of ionospheric scintillation. According to the values in Table 1, it can be seen that percentage of ionospheric scintillation that occurred in 2015 (maximum solar activity) is more than that in 2017 (moderate solar activity) and 2020 (minimum solar activity). S4 observation values for the 30-day interval of January, March, and July show that the percentage of ionospheric scintillation occurrence in March is higher than in January and July. According to the obtained values from modeling in Table 1, by considering RMSE and R-square values, it can be seen that the accuracy of modeling the 30-day interval of July is better than that of the other two 30-day intervals of January and March. The lowest modeling accuracy was observed for the 30 days of March 2020, and the highest accuracy was observed for the 30 days of July 2015. Concerning the modeling values implemented in three months in three years with different solar activities, it can be shown that the modeling presented with GANN was less accurate at times when strong ionospheric scintillations occurred. When there are more irregularities, there are more changes in physical parameters that are used as the input of the GANN, and as a result, the modeling accuracy is reduced. However, in times with less ionospheric perturbations, the physical parameter change is less which  
Figure 2: The architecture of the proposed neural network model for the prediction of ionospheric scintillation parameters (§4 and ROTI). increases the modeling accuracy. For example, 2020 is more accurate than the other years, and July is more accurate than the other months of that year. Additionally, in 2015 and March, each year has less accuracy in modeling. In the second step, we predict the S4 for the first day after the modeling period, i.e., for the 31 January, 31 March, and 31 July in 2015, 2017, and 2020, by using the GANN model. Table 2 presents the difference between the predicted and observed S4 values. According to [23], S4 values greater than 0.2 are considered as an ionospheric scintillation occurrence. Figure 3 depicts the S4 value for three specified days for detecting ionospheric scintillation. In this plot, the threshold limit of S4 = 0.2 was used to determine the occurrence time of ionospheric scintillation. Table 2 shows that the percentage of ionospheric scintillation in 2015 is clearly more than that in 2017 and 2020. The S4 values for 31 March are higher than those for 31 January, and also 31 July has the lowest S4 value during the 24-h prediction period in these three years. As it can be seen in Table 2, the highest percentage of ionospheric scintillation was in 2015 and on 31 March. For 31 January and 31 July 2015, it can be seen that ionospheric scintillation occurs during about half of the day. In 2017, the percentage of ionospheric scintillation and the average and maximum S4 decrease compared to 2015.