Machine learning predicts which rivers, streams, and wetlands the Clean Water Act regulates
成果类型：
Article
署名作者：
Greenhill, Simon; Druckenmiller, Hannah; Wang, Sherrie; Keiser, David A.; Girotto, Manuela; Moore, Jason K.; Yamaguchi, Nobuhiro; Todeschini, Alberto; Shapiro, Joseph S.
署名单位：
University of California System; University of California Berkeley; University of California System; University of California Berkeley; Resources for the Future; California Institute of Technology; Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT); University of Massachusetts System; University of Massachusetts Amherst; Iowa State University; National Bureau of Economic Research; University of California System; University of California Berkeley; United States Department of Energy (DOE); University of California System; University of California Berkeley; University of California System; University of California Berkeley
刊物名称：
SCIENCE
ISSN/ISSBN：
0036-10803
DOI：
10.1126/science.adi3794
发表日期：
2024-01-26
页码：
406-412
关键词：
摘要：
We assess which waters the Clean Water Act protects and how Supreme Court and White House rules change this regulation. We train a deep learning model using aerial imagery and geophysical data to predict 150,000 jurisdictional determinations from the Army Corps of Engineers, each deciding regulation for one water resource. Under a 2006 Supreme Court ruling, the Clean Water Act protects two-thirds of US streams and more than half of wetlands; under a 2020 White House rule, it protects less than half of streams and a fourth of wetlands, implying deregulation of 690,000 stream miles, 35 million wetland acres, and 30% of waters around drinking-water sources. Our framework can support permitting, policy design, and use of machine learning in regulatory implementation problems.