Artificial intelligence forecasting and uncertainty analysis of meteorological data in atmospheric flows

This study investigates using the long short-term memory model, a recurrent neural network, for forecasting time series data in atmospheric flows. The model is specifically employed to handle the intrinsic temporal dependencies and nonlinear patterns in time series data related to wind, temperature, and relative humidity. The research incorporates preprocessing methodologies such as normalization and sequence generation to enhance the model's learning process and alignment with fluid dynamics characteristics. The study further examines strategies for optimizing model performance, including hyperparameter tuning and feature selection, while considering various data compositions that capture the complexities of atmospheric behavior. Key factors are analyzed to evaluate their impact on the model's ability to predict dynamic flow patterns. The model's effectiveness is evaluated using statistical and visual methods, highlighting its capabilities in accurately forecasting trends and variations within meteorological datasets. The findings indicate that the model can significantly improve predictive accuracy in meteorological applications, offering valuable insights into the dynamic nature of atmospheric flows and the importance of optimizing data inputs and modeling techniques.