Estimating the methane flux of the Dajiuhu subalpine peatland using machine learning algorithms and the maximal information coefficient technique

The eddy covariance (EC) technique has emerged as the most widely used method for long-term continuous methane flux (FCH4) observations. However, the completeness of the FCH4 time series is limited by instrumental failures and data quality issues, resulting in missing data gaps ranging from 20 % to 90 %. In this situation, the excellent performance of machine learning (ML) algorithms in filling missing FCH4 data has provided a foundation for developing regional-scale FCH4 models. In this study, we established estimation models for FCH4 utilizing random forest (RF), support vector machine (SVM), back propagation (BP) and nonlinear multiple regression (MLR) algorithms. The maximal information coefficient (MIC) technique was employed to identify and rank the environmental factors that were correlated with FCH4. Our findings revealed that soil temperature (Ts), soil water content (SWC) and air temperature (Ta) were the primary environmental factors influencing FCH4. Among the four algorithms, from perspectives of model accuracy and relatively small number of driving factors, the RF models exhibited the best performance, followed by BP and SVM, whereas MLR demonstrated the lowest performance. Among the 144 RF models established using nine datasets, RF model with 8 driving factors in all-year (RFall-year8) could capture seasonal variations. Ultimately, we recommend (RFall-year8 as the optimal model for estimating FCH4 in the Dajiuhu subalpine peatland.