Is Traditional Data-Driven Machine Learning Useful for Landfill Methane Predictions?

Landfills are one of the major sources of greenhouse gas emissions worldwide. Over the past few decades, gas collection and energy conversion systems have been increasingly implemented in landfills to enhance sustainability and profitability. Accurate methane (CH4) generation rate estimates are essential for designing and optimizing these systems. However, due to the heterogeneity of waste properties, site-specific conditions, and the complex interrelated thermo-hydro-bio-mechanical processes involved, obtaining precise CH4 generation predictions is challenging. Although laboratory and field monitoring data provide valuable insights, obtaining such data is expensive and is often site-specific and non-generalizable. Process-based numerical models, such as the coupled thermo-hydro-bio-mechanical (CTHBM) model developed at the University of Illinois Chicago, offer reliable estimates but are computationally intensive for optimization tasks. Recent advancements in machine learning (ML) based data-driven modeling show promise in handling complex nonlinear datasets. This study evaluates the effectiveness of several ML models, including random forests (RF), extreme gradient boosting (XGB), multi-layer perceptron (MLP), and linear regression (LR), in predicting landfill CH4 generation rates. The data used to train these ML models is generated using the CTHBM model by varying several waste properties. Results indicate that during the rapid methane generation phase, the MLP model demonstrated excellent performance, while XGB also showed reasonable accuracy. In contrast, RF and LR models did not accurately predict methane generation rates during this phase. This study is a preliminary proof of concept for applying ML techniques in landfill CH4 prediction. Further exploration of advanced techniques, such as physics-informed ML, is recommended for scalable and generalizable predictions.