research-article

ActiveRemediation: The Search for Lead Pipes in Flint, Michigan

Authors:

Jacob Abernethy,

Alex Chojnacki,

Eric Schwartz, and

Jared WebbAuthors Info & Claims

KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

July 2018

Pages 5 - 14

https://doi.org/10.1145/3219819.3219896

Published: 19 July 2018 Publication History

Abstract

We detail our ongoing work in Flint, Michigan to detect pipes made of lead and other hazardous metals. After elevated levels of lead were detected in residents' drinking water, followed by an increase in blood lead levels in area children, the state and federal governments directed over $125 million to replace water service lines, the pipes connecting each home to the water system. In the absence of accurate records, and with the high cost of determining buried pipe materials, we put forth a number of predictive and procedural tools to aid in the search and removal of lead infrastructure. Alongside these statistical and machine learning approaches, we describe our interactions with government officials in recommending homes for both inspection and replacement, with a focus on the statistical model that adapts to incoming information. Finally, in light of discussions about increased spending on infrastructure development by the federal government, we explore how our approach generalizes beyond Flint to other municipalities nationwide.

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References

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Cited By

Anjum RParvin FAli S(2023)Machine Learning Applications in Sustainable Water Resource Management: A Systematic ReviewEmerging Technologies for Water Supply, Conservation and Management10.1007/978-3-031-35279-9_2(29-47)Online publication date: 26-Jul-2023
https://doi.org/10.1007/978-3-031-35279-9_2
Goovaerts P(2023) Geospatial model of composition of water service lines in Flint, Michigan : Validation using excavation data and a new compositional geostatistical approach AWWA Water Science10.1002/aws2.13315:2Online publication date: 24-Mar-2023
https://doi.org/10.1002/aws2.1331
Slavitt I(2022)Prioritizing municipal lead mitigation projects as a relaxed knapsack optimization: a method and case studyInternational Transactions in Operational Research10.1111/itor.1321230:6(3719-3737)Online publication date: 30-Sep-2022
https://doi.org/10.1111/itor.13212
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ActiveRemediation: The Search for Lead Pipes in Flint, Michigan

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cover image ACM Other conferences

KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

July 2018

2925 pages

ISBN:9781450355520

DOI:10.1145/3219819

General Chairs:
Yike Guo
Imperial College London
,
Faisal Farooq
IBM

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 19 July 2018

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The Michigan Institute for Data Science (MIDAS)

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KDD '18

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KDD '18: The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 19 - 23, 2018

London, United Kingdom

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KDD '18 Paper Acceptance Rate 107 of 983 submissions, 11%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

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https://doi.org/10.1007/978-3-031-35279-9_2
Goovaerts P(2023) Geospatial model of composition of water service lines in Flint, Michigan : Validation using excavation data and a new compositional geostatistical approach AWWA Water Science10.1002/aws2.13315:2Online publication date: 24-Mar-2023
https://doi.org/10.1002/aws2.1331
Slavitt I(2022)Prioritizing municipal lead mitigation projects as a relaxed knapsack optimization: a method and case studyInternational Transactions in Operational Research10.1111/itor.1321230:6(3719-3737)Online publication date: 30-Sep-2022
https://doi.org/10.1111/itor.13212
Zhang MLi TYu YLi YHui PZheng Y(2022)Urban Anomaly Analytics: Description, Detection, and PredictionIEEE Transactions on Big Data10.1109/TBDATA.2020.29910088:3(809-826)Online publication date: 1-Jun-2022
https://doi.org/10.1109/TBDATA.2020.2991008
Vijayashanthar VSmall MVanBriesen J(2022)Assessment of Lead in Drinking Water from Multiple Drinking Water Sampling Programs for a Midsize CityEnvironmental Science & Technology10.1021/acs.est.2c0661457:1(842-851)Online publication date: 23-Dec-2022
https://doi.org/10.1021/acs.est.2c06614
Weintraut ZNewhart KThompson KRoostaei J(2022)Are You Ready for Big Data?Journal AWWA10.1002/awwa.2021114:10(78-82)Online publication date: 7-Dec-2022
https://doi.org/10.1002/awwa.2021
Isenegger KBirhane YOjha VCoxe CLewis C(2021)Understanding and Expanding College Students' Perceptions of Computing's Social Impact2021 Conference on Research in Equitable and Sustained Participation in Engineering, Computing, and Technology (RESPECT)10.1109/RESPECT51740.2021.9620589(1-10)Online publication date: 23-May-2021
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Lobo GKalyan BGadgil A(2021)Predicting childhood lead exposure at an aggregated level using machine learningInternational Journal of Hygiene and Environmental Health10.1016/j.ijheh.2021.113862238(113862)Online publication date: Sep-2021
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