Predicting mold severity in buildings using interpretable machine learning

doi:10.1016/j.jobe.2025.113901

Predicting mold severity in buildings using interpretable machine learning

2025-08-26

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DOI：10.1016/j.jobe.2025.113901

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• Machine learning predicts building wide mold-severity score using existing facility data. • Inspections and air exchange parameters emerge as most important predictors of mold. • Residential and storage structures show heightened susceptibility to mold issues. • Interpretable model results enable targeted intervention strategies without added monitoring. • Model offers quantitative approach complementing traditional moisture management.

Keywords:

machine learning

mold-severity score

facility data

interpretability

moisture management

Predicting mold severity in buildings using interpretable machine learning

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