Predicting Microbiologically Influenced Corrosion Risk from Quorum Sensing Biofilm Community Features: A Random Forest-SHAP Approach
This study introduces a Random Forest-SHAP machine learning framework that successfully predicts microbiologically influenced corrosion risk by integrating novel quorum sensing biofilm community features with traditional environmental and microbial parameters, achieving an F1 score of 0.762 and demonstrating the predictive value of bacterial communication signals in corrosion assessment.