Atmospheric CO2 concentration prediction based on bidirectional long short-term memory

Introduction Accurate prediction of atmospheric CO 2 concentration is essential for evaluating local emission dynamics, supporting regional carbon management, and promoting carbon neutrality goals. However, CO 2 variations are highly influenced by complex interactions between meteorological conditions and anthropogenic activities, leading to highly nonlinear and time-dependent behavior that challenges conventional prediction methods. Methods To address this issue, a time series prediction framework based on Bidirectional Long Short-Term Memory (BILSTM) was developed for ground-based CO 2 concentration forecasting. Observations from four stations of the European Integrated Carbon Observation System (ICOS) were used. Correlation analysis was first conducted between CO 2 concentration and multiple meteorological variables to identify dominant driving factors. Two prediction schemes were then designed: one incorporating all meteorological variables and another using only highly correlated features. The proposed BILSTM model was evaluated against LSTM, GRU, and Support Vector Regression (SVR) models using RMSE, MAE, and R2 as performance metrics. Results The results indicate that correlation-based feature selection effectively improves prediction accuracy and model stability. Among all models, BILSTM consistently achieves superior performance in both short-term and long-term CO 2 prediction, exhibiting greater stability and robustness than LSTM, GRU, and SVR. Discussion The superior performance of BILSTM is attributed to its bidirectional recurrent structure, which enables effective learning of both past and future temporal dependencies in CO 2 time series. Differences in prediction accuracy among stations reflect varying environmental and anthropogenic influences.