Thursday, February 16, 2023

Can Artificial Intelligence Save the Bay?

I doubt it, but here are two article on research which suggests artificial intelligence to blah, blah, blah.  Dredgewire, Could artificial intelligence hold the key to saving the Chesapeake Bay?

Researchers at the Chesapeake Conservancy in Annapolis last month announced that they created a neural network that learned how to identify wetlands by sifting through satellite images — and spotting places covered by parking lots, roads and farm fields where they once existed.

A new paper from the Virginia Institute for Marine Studies uses similar deep learning to identify man-made shoreline structures around the bay. Scientists at the University of Maryland Center for Environmental Science are about to publish the results of an artificial intelligence study of state forestry lands in Pennsylvania that evaluates the most effective management techniques.

AI research is being published on determining water quality, studying fish populations and mapping land cover, to name just a few applications.

Artificial intelligence is changing environmental science, and that will affect everything from land use decisions to best farming practices across the bay. It will change how we deal with climate change, the impact of expanding development, and finally, perhaps, help create a healthy Chesapeake. As more and more groups utilize this technology over the next few years, this will be a revolution.

“Taking all this information and translating it in a way that communities can make informed decisions, that’s a direct product of artificial intelligence,” said Kandis Boyd, director of the EPA’s Chesapeake Bay Program.

AI is helping supercharge progress, she said, on reaching restoration goals in the 2014 Chesapeake Bay watershed agreement.

This moment didn’t just arrive. AI has been a topic of discussion in environmental science since the 1990s. Three recent developments, however, make this a turning point for researchers.

First, artificial intelligence has advanced to the point where it can accurately make predictions. There are a bunch of complex terms used in talking about this technology, including “machine learning” or “deep learning.” The bottom line is that humans can write programs that computers use to teach themselves how to accurately predict answers to complex questions.

Five or 10 years ago, you had to be affiliated with a research university or the federal government to have access to the kind of supercomputing clusters needed for this work. It involves processing huge amounts of complex data. Now, many smaller research groups have access.

Researchers can now use these tools to take advantage of an explosion in continually updated, remote sensing information — think satellite images — with detail so fine that the result is a level of unprecedented accuracy. Imaging that once detailed a city block can now zoom down to a square meter.

“Being able to process large quantities of images, for example, to better characterize the land use in the watershed, to better characterize where submerged vegetation is in the bay, an important environmental outcome — those to me are really going to be turning points … into how we do science in the bay,” said Isabella Bertani, an assistant research scientist with the bay program.

That’s exactly what the Chesapeake Conservancy research project shows, demonstrating how small organizations can leverage the democratization of artificial intelligence, computing power and big data.

Here’s what researchers at the conservancy did.

They gathered free satellite imagery for three small areas: Mille Lacs County, Minnesota; Kent County, Delaware; and St. Lawrence County, New York. They filtered that data through something called a convolutional neural network — layers of algorithms or equations known as deep learning. It processed images of these three areas, assigned importance to some of the details and then separated them from others.

As it progresses and more data is added, the network learns what is accurate — teaching itself what a wetland area is through repetition.

“A neural network is stacking layers upon layers of those equations, and combining outputs from each equation as an input to the next set of equations,” said Mike Evans, senior data scientist at the conservancy. “When we layer these together and allow them to interconnect and interact, it is a system of equations that can learn way more complicated and intricate patterns in the data than you could by simply combining all your variables into one equation.”

The result, in this case, is maps that predicted the contours of wetlands with 94% accuracy. In some cases, the results corrected maps that hadn’t been updated in decades.

OK, it's mildly interesting, but "finding" wetlands not on the map is not that big a deal. Every environmental impact study ever looks for new (or recovered) wetlands, and given that the definition of wetland is some vague, how do we know that the AI "corrected" existing maps?

My worry with AI is the issue of training. As we've learned from  ChatGPT, the AI can soak up the biases of the people who training. Looking for wetlands? We'll show you wetlands! The whole world is wetlands!

The other article is even less sensible, but that's to be expected from CBS: Artificial intelligence being used to answer questions about sea level rise

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