High resolution data visualization and machine learning prediction of free chlorine residual in a green building water system.

ABSTRACT: People spend most of their time indoors and are exposed to numerous contaminants in the built environment. Water management plans implemented in buildings are designed to manage the risks of preventable diseases caused by drinking water contaminants such as opportunistic pathogens (e.g., Legionella spp.), metals, and disinfection by-products (DBPs). However, specialized training required to implement water management plans and heterogeneity in building characteristics limit their widespread adoption. Implementation of machine learning and artificial intelligence (ML/AI) models in building water settings presents an opportunity for faster, more widespread use of data-driven water quality management approaches. We demonstrate the utility of Random Forest and Long Short-Term Mem

SUBMITTER: Wei S 

PROVIDER: S-EPMC11345929 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature