## 1 Introduction
The effects of ionospheric disturbances on radio signals can be broadly categorized into three main groups. Initially, this phenomenon can reduce the quantity and quality of receiver measurements. In the second category, it may impact the measurements of permanent GNSS stations, influencing both the quantity and quality of observations. Additionally, it can affect satellite-based measurements relying on inter-satellite communication, particularly for low earth orbit (LEO) satellites, potentially leading to communication loss between satellites, especially in short-duration flights [14]. Ionospheric disturbances can result in the loss or increased noise levels of transmitted signals. Signal fading is a common occurrence, typically lasting less than one second. However, it may take several seconds for the receiver to resume tracking and utilizing the signals for its computations. Signal interruptions significantly affect the receiver's ability to smooth measurements and reduce noise effectively. Using carrier phase measurements and, subsequently, smoothing code observations, can help reduce noise [15]. When code smoothing is intermittently disrupted or locked onto a satellite due to ionospheric scintillation, errors in carrier phase measurements may occur, leading to the creation of local multi-Path errors, where thermal noise in observations can be magnified by up to \(10^{3}\) times compared to normal conditions. Moreover, ionospheric scintillations can increase high-frequency disturbances in phase measurements, hindering noise reduction [16]. In general, ionospheric scintillation phenomena primarily affect one or two satellites, causing short-term disruptions and increased noise. If signals are appropriately distributed to the receiver, the loss of one or two satellite signals will not significantly impact receiver performance, and processing can continue. However, if the receiver faces weak satellite coverage from the outset, even minimal ionospheric scintillation can cause significant disruptions in receiver observations. During intense ionospheric scintillation events, all satellites may be affected by disruptions originating from this phenomenon, even if the receiver has good satellite coverage. A strong scintillation event can lead to observations being discontinued [17]. Global navigation satellite system (GNSS) services have multiple reference stations. These systems are designed with a considerable number of high-density networks. Ideally, the arrangement of these stations should be such that the removal of one station has minimal impact on network performance. However, if multiple permanent observation stations are strongly affected by ionospheric scintillation in a particular region, they can disrupt the network in that area [18]. The devised statistical methods are based on mathematical and statistical approaches that create models to uncover relationships between input and output variables based on a large amount of input data. Statistical prediction methods include classical parametric time series prediction models, classical machine learning-based time series prediction methods, such as neural networks, and deep learning-based time series prediction methods [19]. Parametric model-based methods have faced challenges in training nonlinear features of data, and traditional neural network methods have struggled with the ability to generalize spatiotemporal features for a long-term time series. Model-based and prediction methods based on deep learning can automatically extract influential information from data and have the capability to learn linear and nonlinear hidden features in a set of high-dimensional input data [20]. Predicting ionospheric scintillation using mathematical models like deep learning, which can model and predict this phenomenon two-dimensionally for a time series, is valuable since a definitive solution for modeling this phenomenon using numerical methods has not been presented so far. Therefore, it can be a promising approach for predicting such phenomena.