Microbial-Guided prediction of methane and sulfide production in Sewers: Integrating mechanistic models with Machine learning.

Accurate modeling of methane (CH4) and sulfide (H2S) production in sewer systems was constrained by insufficient consideration of microbial processes under dynamic environmental conditions. This study introduces a microbial-guided machine learning (ML) framework (Micro-ML), which integrates microbial process representations from mechanistic models (microbial information) with ML models. Results indicate that Micro-ML model enhanced predictions of CH4 and H2S production, where microbial information provides more information for model optimization. The feature importance of microbial information performed comparable weightings for 58.12 % and 55.16 %, respectively, but their relative significance in influencing Micro-ML model performance varies considerably. The application of Micro-ML performed great potential in reducing CH4 and H2S production (decreased ∼ 80 % and 90 %). The integrated model not only improves the accuracy of CH4 and H2S predictions but also offers a valuable tool for effective management strategies for sewer systems.