biostudies-literatureUnknown24Wei SPeople 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., <i>Legionella</i> spp<i>.</i>), 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 MemWater research X100244https://www.ebi.ac.uk/biostudies/studies/S-EPMC11345929biostudies-literatureHigh resolution data visualization and machine learning prediction of free chlorine residual in a green building water system.PMC11345929Boyer THWei SHogue DXu THamilton KARichard RMondal IfalseHigh resolution data visualization and machine learning prediction of free chlorine residual in a green building water system.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., <i>Legionella</i> spp<i>.</i>), 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 Mem2024-01-01T00:00:00Z2024 Sep2025-04-18T16:54:08.597Z2025-04-07T04:24:20.032ZS-EPMC113459293918832810.1016/j.wroa.2024.100244