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Equatorial Plasma Bubbles (EPBs) are large-scale depletion structures characterized by significantly reduced electron density, which frequently emerge in the low-latitude ionosphere during post-sunset hours. These dynamic plasma irregularities play a crucial role in space weather phenomena, as their evolution can induce severe amplitude and phase scintillations in radio signals, leading to disruptions in satellite communications, global navigation systems, and radar operations. Given their substantial impact on technological systems, accurate prediction of EPB evolution has become a critical challenge in both space physics research and operational space weather forecasting. To address this challenge, this study introduces a novel data-driven approach for EPB evolution prediction by leveraging the SimVP (Simpler yet Better Video Prediction) framework, an advanced deep learning architecture designed for spatiotemporal sequence forecasting. The proposed model learns the complex nonlinear dynamics of EPB structures from historical airglow image sequences, capturing both their morphological transformations and drift patterns. Through extensive experimentation, we systematically evaluate the influence of key parameters—including time resolution, input/output sequence length, and environmental noise—on prediction performance. Our findings demonstrate that an optimal configuration with a 3 min temporal resolution and a 6-frame input/output structure achieves superior predictive accuracy, as evidenced by high Structural Similarity (SSIM=0.989) and Peak Signal-to-Noise Ratio (PSNR=34.704) metrics. Further analysis reveals that the spatial complexity of EPB structures, such as bifurcation events and irregular boundary deformations, significantly affects prediction fidelity, whereas the impact of light pollution—a common issue in ground-based airglow observations—is comparatively minor. The model proposed in this paper demonstrates robust cross-station applicability. Beyond forecasting, the model also exhibits potential for reconstructing corrupted airglow data, offering a computational solution to enhance observational datasets affected by atmospheric or instrumental noise. This work not only establishes a robust, machine learning-based tool for EPB evolution analysis but also contributes to the broader development of Artificial Intelligence (AI) applications in space weather modeling and ionospheric research.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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