## 4 Results and Discussion  
The data related to the modeling and prediction of ionospheric scintillation in this study include observations with time steps unified for use as input and target data in the ANN. For this purpose, the S4 data were derived from the GNSS observations at GUAM station in Guam, Dededo City, with a longitude of 144.8683 degrees, a latitude of 13.5893 degrees, and a height of 201.922 m higher than the mean sea level. The JAVAD GNSS receiver was used in this station. The RINEX data received from this station with an observation rate of 30 s can be downloaded from the ICS site at [[ftp://cddis.gsfc.nasa.gov](ftp://cddis.gsfc.nasa.gov)]([ftp://cddis.gsfc.nasa.gov](ftp://cddis.gsfc.nasa.gov)) (access on 27 March 2021). Figure 1 shows the location of the two stations with ionosonde and GNSS receivers in Guam near the equatorial ionization anomaly (EIA). In this research, an ANN is used and integrated with the genetic algorithm (GA) to obtain optimal values of initial weights by introducing preliminary data for modeling and, ultimately, predicting ionospheric scintillation. Additionally, combining the GA with an NN plays a vital role in increasing the accuracy of the modeling and, finally, the daily prediction of ionospheric scintillation. The initial layer has five neurons and includes the network inputs, which combine two subnetworks, while the first hidden layer contains five neurons that are connected to a single neuron in the second hidden layer, sequentially. The sigmoid function is implemented by the first and second hidden layers as a transfer function, while the second hidden layer employs a linear function. The last layer contains a single neuron, which is the output of this model. The implemented ANN uses the weights determined by the GA as the initial weight required by the network [52]. Therefore, for the GANN training process, 80% of the entire dataset was selected randomly to improve the models, and 20% of the whole dataset was applied for the testing process used for the performance evaluation of GANN. Figure 2 presents the architecture of the proposed neural network model for the prediction of ionospheric scintillation parameters (S4 and ROTI). The implemented ANN uses the weights determined by the GA as the initial weight required by the network [52]. In this designed network, we introduced five types of data as input. These data include the F2 peak electron density height, F2 vertical drift velocity, solar flux parameter (F10.7), sunspot number, and international geomagnetic index \(K_{P}\). On the other hand, the effect of ionospheric phase-amplitude scintillation, which can be expressed in terms of the parameter S4, is entered into the network as target data. The satellite elevation cut-off angle is assumed to be 20 degrees to reduce tracking errors such as multipath. In another approach, we enter these physical parameters as input data and enter the ROTI parameters as target data into the network. In the ANN, a set of data is entered as initial weights, which are introduced to the network with the primary training in the GA,  
Figure 1: Location of the two stations with ionosonde and GNSS receivers: Guam near the equatorial ionization anomaly (EIA). which was discussed in detail in Section 3.2. The average S4 and ROTI data are computed in five-minute time intervals, which is a conventional interval [24], for 24 h for three complete months in three different seasons of the winter solstice, equinox, and summer solstice in 2015 (maximum solar activity), 2017 (moderate solar activity), and 2020 (minimum solar activity). Input data in months January, March, and July in 2017 were used for the initial modeling of the ANN. The prediction was accomplished on the day after each 30-day period, i.e., on 31 January, 31 March, and 31 July. The results of modeling the averaged S4 and ROTI data indicate the application of 30-day modeling for this phenomenon.