Statistical Modeling of Lead Service Line Locations Using Open‐Source Data. Issue 1 (23rd January 2023)

• Record Type: Journal Article
• Authors: Feider‐Blazer, Estelle; Chen, Thomas Ying‐Jeh; Yazdekhasti, Sepideh; Daly, Craig Michael
• Is Part Of: Journal ‐ American Water Works Association. Volume 115:Issue 1(2023)
• Page Start: 48
• Page End: 58
• Publication Date: 2023-01-23
• Subjects: Modeling -- Service Lines -- Statistics -- Lead -- Records -- Water Systems -- Public Health -- Utilities -- Inspection -- Planning; Water-supply -- United States -- Periodicals; Water-supply engineering -- United States -- Periodicals; 363.61097305
• DOI: 10.1002/awwa.2036
• Languages: English
• ISSNs: 0003-150X

Abstract

Key Takeaways: Lead service lines in drinking water systems pose a serious public health threat, but many utilities lack complete records of their locations. Statistical models trained from freely available public data sources have the potential to provide an efficient way to prioritize service line inspection plans. Factors such as choice of machine learning algorithm and spatial distribution of parcel records must be considered to ensure precision when developing a predictive model.