2018/06/10 by Jacob Abernethy, Alex Chojnacki, Arya Farahi +3 · 2 voices · 21 citations
Computer Science · Decision Sciences · Engineering · Mathematics · #Business #Computer science #Data Quality and Management #Data science #Engineering #Focus (optics) #Geology #Government (linguistics) #Hazardous waste #Lead (geology) #Machine Learning and Algorithms #Marketing #Service (business) #Statistical analysis #Transport engineering #Waste management #Water Systems and Optimization #Water pipe #Work (physics) #cs.CY #cs.LG #stat.AP #stat.ML
paper · pdf · doi:10.1145/3219819.3219896
10 pages, 10 figures, To appear in KDD 2018, For associated promotional video, see https://www.youtube.com/watch?v=YbIn_axYu9E
arxiv published 2018/06/10 · openalex publication_date 2018/07/19 · arxiv created 2018/08/17 · arxiv updated 2018/08/20 · openalex created_date 2022/08/04 · openalex updated_date 2026/07/29
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.