ActiveRemediation: The Search for Lead Pipes in Flint, Michigan
| Autor: | Jacob Abernethy, Jared Webb, Eric M. Schwartz, Arya Farahi, Alex Chojnacki |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning media_common.quotation_subject Public policy Hazardous metals Machine Learning (stat.ML) 02 engineering and technology Statistics - Applications 01 natural sciences Machine Learning (cs.LG) Computer Science - Computers and Society 010104 statistics & probability Lead (geology) Statistics - Machine Learning 11. Sustainability Computers and Society (cs.CY) 0202 electrical engineering electronic engineering information engineering Applications (stat.AP) 0101 mathematics Environmental planning media_common Government Water infrastructure Work (electrical) Service (economics) Active learning 020201 artificial intelligence & image processing Business |
| Zdroj: | KDD |
| DOI: | 10.48550/arxiv.1806.10692 |
| Popis: | 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. Comment: 10 pages, 10 figures, To appear in KDD 2018, For associated promotional video, see https://www.youtube.com/watch?v=YbIn_axYu9E |
| Databáze: | OpenAIRE |
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