Ground-based telescopes face a significant challenge posed by atmospheric turbulence, resulting in acquired images appearing distorted and lacking sharpness. Adaptive optics technology is employed to mitigate this issue by effectively correcting wavefront aberrations through adjustments to the surface of a deformable mirror. Furthermore, the integration of neural networks into the control system has demonstrated notable enhancements in both atmospheric correction and turbulence prediction. Specifically, in this work, a 2D-LSTM network structure is utilized, which has shown good efficiency in slope prediction over a time sequence. The objective of this study is to address the prediction of turbulence data without the noise introduced by reading instruments. Through the experiments conducted in this research, it is demonstrated that such neural models are capable of learning to a certain extent the noise patterns of the system. Thus, the obtained data closely resemble real-world turbulence conditions.