The combined machine learning model SMOTER-GA-RF for methane yield prediction during anaerobic digestion of straw lignocellulose based on random forest regression

Simulating anaerobic digestion (AD) using a machine learning (ML) is important for guiding methane production in practice. But the performance of the ML is affected by operational conditions and microbial types and quantities, which lead to low accuracy of the traditional model. In this study, a combined ML algorithm SMOTER-GA-RF was constructed by expanding the number of genomic data, introducing oversampling technique (SMOTER) and genetic algorithm (GA) to increase prediction accuracy, which the test set R2 increased 36.22%. The normalized root mean square error and percent bias of the validation set (D2) were 8.92% (