## 6 Conclusions  
The current research was conducted to predict ionospheric scintillation for GUAM station, with a longitude of 144.86\({}^{\circ}\) and a latitude of 13.58\({}^{\circ}\). For this purpose, the data from the GNSS permanent station and the data from the ionosonde were used to obtain the ionospheric physical data. This region is located in the magnetic equator region, which has a high ionospheric scintillation occurrence rate. In this research, five types of ionospheric physical data were used as the input data in the ANN integrated with the GA for modeling and predicting S4 values. We used the input data in five-minute time intervals to predict the occurrence of ionospheric scintillation using S4 and ROTI parameters at the consequent day after the 30-day modeling period. The use of data related to S4 and ROTI parameters is related to the type of intrinsic noise structure; thus, severe changes in their values in the short time intervals are inevitable, and modeling this type of data seems relatively complicated. However, using the ANN method and integrating it with the GA made it possible to create flexibility in modeling this dataset. The data were selected in three different months from different seasons of winter, spring equinox, and summer during three different years, 2015 (maximum solar activity), 2017 (moderate solar activity), and 2020 (minimum solar activity). The modeling performed for the S4 parameter in the 30-day intervals had a regression value greater than 91% and an RMS error of about 0.01. In addition, the regression of this parameter was more than 80% for the one-day prediction. For the ROTI parameter, the regression value of modeling was above 90%, and the RMS error was 0.03, although the regression of the daily prediction was more than 80%. According to the R-square and RMSE parameters for modeling and predicting the S4 and ROTI, when solar magnetic activity decreases, ionospheric irregularities reduce, and consequently, the accuracy of the predicted parameter is improved. This can be clearly seen from the results, where the accuracy of predicted values for 31 January, 31 March, and 31 July 2020 are higher than the same days in 2015 and 2017. The presented results are limited to the applied database but show that an operational evaluation of the recommended method  
Figure 6: The residual vectors of observed minus predicted ROTI values during 24 h on 31 January, 31 March, and 31 July in 2015, 2017, and 2020.  
would be worthwhile. Concluding, the ANN method integrated with the GA can detect and predict ionospheric scintillation occurrence near real time on a daily basis, which is critical for reducing the error of the ionospheric scintillation impact on satellite-based communication and navigation system performance. This method can play a precautionary role for alerting GNSS users that the observation epoch might be disturbed by ionospheric scintillation, and GNSS users can eliminate error-infected observations from the dataset.  
Conceptualization, M.A. and A.A.; methodology, M.A. and A.A.; software, A.A.; validation, A.A., M.A.; formal analysis, A.A.; investigation, A.A.; resources, M.A; data curation, A.A.; writing--original draft preparation, A.A.; writing--review and editing, A.A. and M.A.; visualization, A.A.; supervision, M.A. and H.S. and L.-C.T.; project administration, M.A.; funding acquisition, H.S. All authors have read and agreed to the published version of the manuscript.  
Parts of this study were accomplished under the bi-lateral project DEAREST (project number: SCHU 1103/15-1) funded by the German Research Foundation (DFG) and the Ministry of Science and Technology of Taiwan (MOST).  
The authors declare no conflict of interest.