###### Abstract  
Irregularities in electron density usually correlate with ionospheric plasma perturbations. These variations causing radio signal fluctuations, in response, generate ionospheric scintillations that frequently occur in low-latitude regions. In this research, the combination of an artificial neural network (ANN) with the genetic algorithm (GA) was implemented to predict ionospheric scintillations. The GA method was considered for obtaining the ANN model's initial weights. This procedure was applied to GNSS observations at GUAM (13.58 degE, 144.86 degN, 201.922H) station for the daily prediction of ionospheric amplitude scintillations via predicting the signal-to-noise ratio (S4) or via prediction of the rate of TEC index (ROTI). Thirty-day modeling was carried out for three months in January, March, and July, representing different seasons of the winter soli extend along magnetic field lines to low latitudes [4; 5]. This increase is related to an unstable process called Rayleigh-Taylor, which leads to disturbances in the ionosphere [6]. The depleted plasma, which contributes to the bubbling process, is combined with the condensed and non-reacted plasma produced in the equatorial ionosphere, creating a strong gradient in the ionospheric structure [7]. This process results in severe perturbations in the region and begins to increase the ionospheric scintillation in the equatorial regions of the ionosphere [8; 9; 10; 11]. Amplitude ionospheric scintillation can reduce the level of the transmitted signal's lock threshold or cause deep signal fades from the satellite in the case of severe ionospheric scintillation. During ionospheric scintillation, this phenomenon causes a signal deviation in random directions around the main axis of the wave propagation by creating disturbances in the refractive index of the medium. The effect of the ionospheric scintillation on the carrier phase causes cycle slips, disconnects the receiver signal, and leads to the loss of lock. Phase ionospheric scintillation can create a frequency shift in the signal and increase the amount of bandwidth, which locks the receiver's phase and reduces the accuracy of the receiver's ability to connect and lock on to the signal transmitted from the satellite [12]. Accordingly, this process increases the accuracy of the geometric dilution of the precision parameter and thus raises the error rate in determining the position of the receiver, consequently reducing the ability of the receiver to receive and track the transmitted signals [13]. Generally, modeling and predicting ionospheric scintillation can reduce the effects of its error in the observations of GNSS and, therefore, increase the accuracy of satellite communication and navigation application [14]. Thus far, different empirical models have been published to estimate the amount of amplitude and phase ionospheric scintillation [15; 16; 17; 18]. The prediction of this phenomenon using mathematical models is of great importance as these methods can be used to model these phenomena by empirical parameters. Although some empirical models have used parameters such as latitude and longitude, seasons, and time of observation as input, the ionospheric scintillation phenomenon is affected by physical parameters of the ionosphere, in addition to spatial parameters [17]. The percentage of an ionospheric scintillation occurrence was modeled by [PERSON] et al. [19] for an eleven-year period, and they computed a numerical value from the mean of the ionospheric scintillation of a day as an input to the model. In this model, an artificial neural network (ANN) was used, and parameters such as solar flux and the occurrence time were considered as other inputs of the model; accordingly, they developed a model for ionospheric scintillation. A model for predicting ionospheric scintillation events was developed [20] using the machine learning and decision tree methods, as well as considering most observations of ionospheric scintillation at Sao Jose dos Campos station with 1.5-h time intervals.