MGGTSP‐CAT: Integrating Temporal Convolution and LSTM for Multi‐Scale Greenhouse Gas Time Series Prediction via Cross‐Attention Mechanism

Abstract Greenhouse gas‐induced global warming represents one of the most pressing environmental challenges currently faced, with methane, a primary constituent of greenhouse gases, exerting a more potent greenhouse effect than carbon dioxide. Precise prediction of methane concentrations is therefore of paramount importance. Owing to the complex interplay of factors affecting methane levels, existing forecasting methodologies often fall short in terms of accuracy and long‐term applicability. In response, this article leverages a multiscale time series to propose an attention‐based deep learning model. This approach synthesizes data across varied time spans, enhancing the extraction of periodic patterns and inter‐scale feature correlations. The model employs a hybrid framework and attention mechanisms to adaptively discern both short‐ and long‐term dependencies, as well as spatiotemporal correlations, capturing the intricate coupling of change mechanisms in the environment. Focusing on the Yanqing district of Beijing, a dataset integrating multiple features is developed and conduct forecasts 48 h in advance for both surface methane concentrations and the average molar fraction of total methane columns, comparing our model against several benchmarks. The model demonstrates superior performance, achieving an RMSE of 5.6123, MAE of 3.4959, and an R 2 of 0.9148, which significantly exceeded the accuracy and generalizability of all baseline models.