Journal
KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING
Volume -, Issue -, Pages 5-14Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3219819.3219896
Keywords
Water Infrastructure; Flint Water Crisis; Risk Assessment; Machine Learning; Active Learning; Public Policy
Categories
Funding
- Michigan Institute for Data Science (MIDAS)
- U-M's Ross School of Business
- Google.org
- National Science Foundation CAREER grant [IIS 1453304]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available