How machine learning can help environmental regulators
How to locate potentially polluting animal farms has long been a problem for environmental regulators. Now, Stanford scholars show how a map-reading algorithm could help regulators identify facilities more efficiently than ever before.
Law Professor Daniel Ho, along with PhD student Cassandra Handan-Nader, have figured out a way for machine learning – teaching a computer how to identify and analyze patterns in data – to efficiently locate industrial animal operations and help regulators determine each facility’s environmental risk. The researchers’ findings are set to publish April 8 in Nature Sustainability.
“Our work shows how a government agency can leverage rapid advances in computer vision to protect clean water more efficiently,” said Ho, the William Benjamin Scott and Luna M. Scott Professor of Law, and a senior fellow at the Stanford Institute for Economic Policy Research.
According to the Environmental Protection Agency (EPA), agriculture is the leading contributor of pollutants into the nation’s water supply, with substantial pollution believed to be emanating from large-scale, concentrated animal feeding operations, known also as CAFOs.
But environmental monitoring efforts have been stymied by a basic problem: Regulators have no systematic way of determining where CAFOs are located, Ho said. The United States Government Accountability Office reports that no federal agency has reliable information on the number, size and location of large-scale agricultural operations.
While the Clean Water Act does require some federal permitting, it only applies to operations that actually discharge pollutants into U.S. waterways – not facilities that could potentially cause contamination – intentionally or not, Ho said.
With no definite list to turn to, efforts to monitor potentially polluting facilities are difficult and, in some cases, impossible.
“This information deficit stifles enforcement of the environmental laws of the United States,” Ho said.
Some environmental and public interest groups have tried to identify facilities themselves by scanning terrain manually or poring over aerial photos, but they have found it an incredibly time-intensive task. It took one environmental group over three years to look at images from just one state. Monitoring efforts like these could never scale or be done in real time, Ho said.
Ho and Handan-Nader, then a research fellow at Stanford Law School and now pursuing a doctorate in political science, turned their attention to a type of artificial intelligence called deep learning. A subset of machine learning, deep learning algorithms have revolutionized the ability to detect complex objects in imagery.
