Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

‘Pb-predict’: using machine learning to locate lead plumbing in a large public water system

Gurewitsch, Raanan (2019) ‘Pb-predict’: using machine learning to locate lead plumbing in a large public water system. Undergraduate Thesis, University of Pittsburgh. (Unpublished)

This is the latest version of this item.

Download (1MB) | Preview


Struggling to respond to elevated lead levels in residential tap water, cities like Flint, MI and Pittsburgh, PA are undergoing large-scale efforts to remove the lead pipes that bring water service to their customers. However, limited geographic data on plumbing materials throughout housing stocks represents a logistical challenge for local authorities to locate and replace lead service lines. This study tests whether available geographic data on housing conditions and plumbing materials can effectively inform risk assessment and thus, expedite replacement programs and help prevent exposure to lead. To do so, we train and compare multiple types of machine learning classification algorithms to predict the presence or absence of lead service lines at properties in Pittsburgh. The results show that the probability of having a lead service line increases for houses built before 1930 and demonstrate the significance of parcel age, spatial proximity and other housing characteristics as predictive features for locating lead in water hazards. Accurate targeting of high-risk housing units may inform the strategy of decision-makers working to ensure that residents of aging American homes have safe drinking water. Therefore, the results are mapped to simulate the prevalence of lead service lines throughout the City of Pittsburgh and a framework for other cities is discussed.


Social Networking:
Share |


Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Gurewitsch, Raananrsg36@pitt.edursg360000-0002-2021-612X
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairKarimi,
Committee MemberPyne,
Committee MemberBlackhurst,
Committee MemberGoovaerts,
Date: 22 April 2019
Date Type: Publication
Defense Date: April 2019
Approval Date: 22 April 2019
Submission Date: 15 April 2019
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 37
Institution: University of Pittsburgh
Schools and Programs: David C. Frederick Honors College
School of Computing and Information > Information Science
Degree: BPhil - Bachelor of Philosophy
Thesis Type: Undergraduate Thesis
Refereed: Yes
Uncontrolled Keywords: Machine Learning, Lead in Water
Date Deposited: 22 Apr 2019 16:18
Last Modified: 22 Apr 2019 16:18

Available Versions of this Item


Monthly Views for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item