Atmospheric CO2 concentration prediction based on bidirectional long short-term memory - Qiao, Yina; Yang, Hui; Cui, Liu; Zhang, Yuan; Feng, Gefei; Fan, Huaiwei; Lv, Qingzhou; Yao, Yuejing (Frontiers in Earth Science, 2026)
IntroductionAccurate prediction of atmospheric CO2 concentration is essential for evaluating local emission dynamics, supporting regional carbon management, and promoting carbon neutrality goals. However, CO2 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.MethodsTo address this issue, a time series prediction framework based on Bidirectional Long Short-Term Memory (BILSTM) was developed for ground-based CO2 concentration forecasting. Observations from four stations of the European Integrated Carbon Observation System (ICOS) were used. Correlation analysis was first conducted between CO2 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.ResultsThe 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 CO2 prediction, exhibiting greater stability and robustness than LSTM, GRU, and SVR.DiscussionThe superior performance of BILSTM is attributed to its bidirectional recurrent structure, which enables effective learning of both past and future temporal dependencies in CO2 time series. Differences in prediction accuracy among stations reflect varying environmental and anthropogenic influences.