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

Accurate modeling of methane (CH ₄ ) and sulfide (H ₂ S) 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 CH ₄ and H ₂ S 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 CH ₄ and H ₂ S production (decreased ∼ 80 % and 90 %). The integrated model not only improves the accuracy of CH ₄ and H ₂ S predictions but also offers a valuable tool for effective management strategies for sewer systems.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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