###### Abstract
This model used geophysical data such as solar flux, the magnetic activity parameter, vertical plasma drift velocity, and amplitude scintillation. In another study, a prediction model was presented by [PERSON] et al. [21] for ionospheric scintillation based on ionospheric height variations for the highest F2 layer altitude, and the authors believed that this parameter had the most significant effect on the creation of ionospheric plasma bubbles and thus used this parameter to predict the ionospheric scintillation phenomena. However, this model did not utilize other physical parameters related to the ionospheric scintillation, i.e., vertical drift velocity, solar flux, and geomagnetic index. Consequently, its prediction was limited only to the height variation parameter of the F2 layer. [PERSON] et al. [11] presented a novel adaptive reassignment time-frequency analysis method. Synthetic and real-time GNSS data examined the synchrosqueezing transform (SST) performance to investigate ionospheric scintillation effects in the low latitudes. SST contributes a time-frequency representation of ionospheric scintillation effects on GNSS signals with sufficient resolution, representing multiple localized oscillating features. A model was proposed by [PERSON] et al. [22] to describe the correlation of ionospheric scintillation with physical ionospheric data, as well as data associated with the sun, using the decision tree method. In this model, physical parameters such as solar flux, geomagnetic activity level, maximum F2 layer height, and velocity of this layer, and the amplitude ionospheric scintillation parameter were used. The time range of the ionospheric scintillation data was higher than that of the data used in [20]. Furthermore, the article mentioned used weak and moderate ionospheric scintillations from 18:30 to 22:00 for 38 days. Due to the correlation analysis and the relation between ionospheric scintillation data, their method obtained higher accuracy compared to previous research. In another study, [PERSON] et al. [22] applied an ANN to predict the amplitude of ionospheric scintillation of GPS signals, by using physical parameters. In this study, amplitude ionospheric scintillation was classified into two types of severe and non-severe hours for a 1.5-h interval, based on the S4. [PERSON] et al. [13] implemented an NN-based forecasting algorithm trained using the particle swarm optimization technique (NN-PSO) for forecasting the S4 values at Darwin station (12.4634'S, 130.8456'E), Australia. 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. The ANN method is a mathematical method with higher efficiency compared to other mathematical modeling methods [23] and is used for modeling and predicting nonlinear problems such as ionospheric parameters. Furthermore, this method is a suitable approach for modeling and predicting data containing noise, which have high variations over a short period, such as the amplitude ionospheric scintillation parameter. The paper is organized as follows: Section 2 describes ionospheric scintillation detection using S4 and ROTI values. Physical parameters affecting ionospheric scintillation are then presented. Section 3 introduces the research methodology, such as an ANN, the GA, and a combination of an artificial neural network with the genetic algorithm (GANN). In Section 4, the study area and the database used for modeling the S4 and ROTI parameters are described. Modeling and prediction results are presented and discussed in Section 5. The results of this study are evaluated in Section 5. Section 6 concludes and elaborates on the accuracy of modeling and predicting ionospheric scintillation.