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UMass presents statewide predictive model to map unsewered parcels; staff warns model is a gap-filling tool, not a regulatory list
Summary
University of Massachusetts researchers described a two-stage machine-learning model that predicts whether land parcels need sanitation infrastructure and whether service is by sewer or on-site systems; staff cautioned the model will be used to fill data gaps and not for regulatory enforcement or to publish parcel-level data publicly.
Researchers from the University of Massachusetts presented a machine-learning model designed to identify unsewered areas and approximate whether a parcel is served by sewer or an on-site system. UMass described the model as a statewide gap-filling tool to support the wastewater needs assessment where complete ground-truth data are not available.
UMass explained the model uses parcel-level features (building density, road network, census variables, parcel value, distance to treatment facilities…
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