## 1 Introduction
This model effectively extracts local spatial and temporal features from TEC datasets, showcasing the potential of hybrid deep-learning approaches in atmospheric science. [PERSON] et al. [PERSON] et al. (2019) discussed the IONISE (Ionospheric Observational Network for Irregularity and Scintillation in East and Southeast Asia) project. They reported preliminary results of ionospheric disturbances and irregularities, including equatorial plasma bubbles and related scintillations, traveling ionospheric disturbances from middle to low latitudes, and the drift of strong, sporadic E structures. This research utilized a convolutional neural network on a five-day time series of geomagnetic data to predict TEC values in Beijing, while [PERSON] et al. [PERSON] et al. (2019) used the ConvLSTM network with a custom-designed loss function to forecast the ROTI maps over high latitudes. This study presented a short-term prediction algorithm based on a convolutional neural network for high-latitude ionospheric irregularities during storm events, using GNSS-derived ROTI maps. In recent years, spatiotemporal learning has garnered attention for time series data, surpassing the traditional unidimensional learning for a time series, as it effectively represents complex spatiotemporal phenomena. [PERSON] et al. [PERSON] et al. (2019) proposed a combination of an LSTM network with a convolutional model, termed ConvLSTM, for spatiotemporal data. In this approach, a convolutional neural network is combined with a recurrent neural network, achieving high accuracy in predicting rainfall using radar images. One advantage of ConvLSTM over the CNN and LSTM methods lies in its ability to simultaneously train the network using the temporal and spatial correlations of the data. However, ConvLSTM has a high number of parameters and can easily overfit the data. Considering this issue, the Convolutional Gated Recurrent Unit (ConvGRU) method can be employed as a multi-step spatiotemporal prediction approach, leveraging a multi-step prediction strategy for multiple input and output tensors. To achieve this, a two-step model of an Encoder and Forecaster is used to generate a tensor from an input matrix to an output matrix with consistent dimensions, ensuring accurate prediction results [PERSON] et al. (2019). In this study, a combined approach of deep learning networks was employed for spatiotemporal prediction in a time series. The use of the ConvGRU method was found to be more suitable for datasets such as the ionospheric scintillation parameter (S4), which utilizes spatial and temporal data for network training and subsequent observational-parameter prediction, compared to other methods. Establishing a real-time or near-real-time alert system for events like ionospheric disturbances, including ionospheric scintillation, has extensive applications in both non-military and military sectors. It can significantly impact industries such as aircraft navigation, modern agriculture, mapping, and ground and satellite radio communications. Despite recent advancements in identifying and mitigating the effects of ionospheric disturbances and climatology on ionospheric scintillations, predicting the occurrence and intensity of ionospheric disturbances leading to scintillations remains challenging. Therefore, modeling and forecasting ionospheric disturbances, such as ionospheric scintillations, can reduce the impact of ionospheric errors in observations from GNSS satellites, ultimately enhancing the accuracy of satellite communication and navigation systems [38]. Our research commences by delineating the parameters for ionospheric scintillations derived from GNSS ground-based data, utilized to detect and analyze ionospheric disturbances. We introduce spatiotemporal ConvGRU-based models in the methodology section, specifically engineered for predicting these disturbances over both spatial and temporal scales.