## 1 Introduction
Deep learning is a subset of machine learning that uses multiple layers for linear transformations to process observational signals such as two-dimensional images or one-dimensional signals [21]. The term \"deep learning\" generally refers to deep neural networks (DNN). In the context of deep neural networks, the term \"deep\" refers to the depth of layers within the neural network, considering the method used in the realm of deep learning [22]. Convolutional Neural Networks (CNN) share many similarities with Artificial Neural Networks (ANN). These networks consist of neurons with trainable weights and biases. Each neuron receives multiple inputs, calculates the product of weights and inputs, and finally, using a non-linear activation function, provides an output [23]. Another type of deep neural network, Recurrent Neural Networks (RNN), is useful for processing sequential or time-series data. In RNNs, each neuron or processing unit can maintain an internal state or memory to preserve information related to previous inputs [24]. Long Short-Term Memory (LSTM) networks are a subtype of recurrent neural network designed to address the challenges faced by traditional RNNs in storing information over long time intervals [25]. Gated Recurrent Unit (GRU) networks are considered modified versions of LSTM, utilizing reset and update gates to facilitate the storage and filtering of information from previous time steps more effectively [26]. Over the past two decades, researchers have extensively utilized observations from the GNSS for computing parameters such as ionospheric scintillation amplitude and phase, as well as other parameters like electron density rate of change, to predict ionospheric scintillations. For instance, [PERSON] et al. [27] employed a neural network modeling approach to estimate the daily occurrence percentage of ionospheric scintillations over eleven years,using the daily scintillation averages. [PERSON] et al. [PERSON] et al. (2018) developed a model for predicting the occurrence of ionospheric disturbances based on decision tree methodology and utilizing extensive ionospheric scintillation observations at Sao Jose dos Campos station during hourly intervals from 20:00 to 24:00 for each day. [PERSON] et al. [PERSON] et al. (2019) presented a prediction method for ionospheric scintillations using statistical and probability-based approaches, relying on phase carrier observations at high geographic latitudes from GNSS stations. [PERSON] et al. [PERSON] et al. (2019) created a multi-layer perceptron model to forecast ionospheric scintillations based on observations from a single station within the time range of 17:30 to 19:00 UTC, targeting periods with higher scintillation occurrences in equatorial geographic latitudes. In this study, scintillation parameters were categorized into severe and non-severe, based on the S4 index, and a classification model was developed for a 1.5-h interval. [PERSON] et al. [PERSON] et al. (2019) introduced a prediction method for daily ionospheric scintillations, combining a multi-layer perceptron model with a genetic algorithm for single-station observations. Previous work has employed deep learning techniques for the spatiotemporal modeling of ionospheric irregularity parameters such as S4 and ROTI (Rate of TEC index). [PERSON] et al. [PERSON] et al. (2019) introduced a predictive model for high-latitude ionospheric phase scintillation, employing a machine learning algorithm known as the support vector machine (SVM). The model, which utilizes a large volume of data from GNSS and sophisticated data-driven discovery tools, showed considerable improvement over the persistence method, particularly for a 1-h lead time. This advancement is crucial to understanding the impact of space weather on trans-ionospheric radio signals, especially at high latitudes where effects are most direct and no reliable predictive capability previously existed. [PERSON] et al. [PERSON] et al. (2019) focused on a hybrid deep learning model combining LSTM and CNN for forecasting ionospheric TEC.