Methane Concentration Inversion Based on Multi-Feature Fusion and Stacking Integration - Yanling Han; Wei Li; Congqin Yi; Ge Song; Yun Zhang (Sensors)
To address the issue of relatively simple features and methods used in methane concentration inversion, which leads to low overall accuracy, this study proposes a methane concentration inversion method based on multi-feature fusion and Stacking ensemble learning. The method leverages the series-parallel cascade structure between multiple base models and meta-models to learn different feature representations and patterns in the original data, fully exploring the intrinsic relationships between various feature factors and methane concentration. This approach improves inversion accuracy and generalization capability. Finally, the research team conducted experimental validation in the eastern region of Xinjiang. The experimental results show that, compared with other typical methods, the Stacking ensemble model proposed in this study achieves the best inversion performance, with R2, RMSE, and MAE values of 0.9747, 2.8294, and 1.5299, respectively. In terms of seasonal distribution, methane concentration in eastern Xinjiang typically shows lower average values in the spring and autumn and higher average values in the summer and winter.