machine learning

Climate Change And Harsh Winters

July 15, 2019 By EarthWise Leave a Comment

In recent years, there have been some unusually harsh winters in North America and Central Europe. This past January, the Midwestern US experienced extreme cold temperatures. We have all become familiar with the term ‘polar vortex’ and its role in sending cold air to middle latitudes and it is generally agreed that unusual behavior by the jet stream is the primary cause of the extreme winter weather.

For years, climate researchers around the world have been investigating the question as to whether the increasingly common wandering of the jet stream is a product of climate change or is a random phenomenon associated with natural variations in the climate system.

The jet stream is a powerful band of westerly winds over the middle latitudes that push major weather systems from west to east. These days, the jet stream is increasingly faltering. Instead of blowing along a straight course parallel to the equator, it sweeps across the Northern Hemisphere in massive waves, producing unusual intrusions of Arctic air into the middle latitudes.

Atmospheric researchers at the Alfred Wegener Institute in Germany have now developed a climate model that can accurately predict the frequently observed winding course of the jet stream. The breakthrough combines their global climate model with a new machine learning algorithm on ozone chemistry. Using their new combined model, they can now show that the jet stream’s wavelike course in winter and subsequent extreme weather outbreaks are the direct result of climate change. The changes in the jet stream are to a great extent caused by the decline in Arctic sea ice, according to the results of the investigations. The results are not surprising but there is now a detailed model to support the hypothesis.

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A warming Arctic produces weather extremes in our latitudes

Photo, posted January 11, 2011, courtesy of Carl Wycoff via Flickr.

Earth Wise is a production of WAMC Northeast Public Radio.

Fish And Ships

April 25, 2019 By EarthWise Leave a Comment

Solutions to overfishing of certain tuna and shark populations have been hindered by some significant unknowns: where the fishing is happening, and where the fish are. But researchers from Stanford University have recently shed some light on this mystery.

According to a paper recently published in the journal Science Advances, the researchers have developed a map of shark, tuna, and ship movements that could help ocean managers identify regions of the sea where vulnerable species may be at risk.

The researchers’ work builds on a 2018 study in which four years of satellite vessel movement data was used to develop a machine learning algorithm that mapped the footprint of 70,000 different fishing vessels around the world. In their paper, the researchers zeroed in on the activities of 900 vessels from 12 countries in the northeast Pacific Ocean to figure out to what degree fishing fleets, sharks, and tunas overlapped.

The researchers combined the vessel data with the ocean habitat preferences of sharks and tunas obtained from a decade-long tracking program called Tagging of Pacific Predators (or TOPP). According to the IUCN, most of the 876 individuals tagged in TOPP belong to species that are either threatened or near-threatened.

By synthesizing this data, researchers were able to map where sharks and tunas would have the highest overlap with commercial fishing vessels. Increasing the transparency of where fish meets fleets will allow ocean managers to determine where international protections may be needed.

The United Nations is currently developing the world’s first legally binding treaty to protect international waters. The Stanford University researchers hope their findings can help with this treaty’s formulation.

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Tunas, sharks and ships at sea

Photo, posted June 20, 2011, courtesy of Mike Baird via Flickr.

Earth Wise is a production of WAMC Northeast Public Radio.

A Breakthrough In Animal Identification

December 25, 2018 By EarthWise Leave a Comment

Researchers from the University of Wyoming have developed a computer model that can identify wild animals in camera-trap photographs with remarkable accuracy and efficiency.

This breakthrough in artificial intelligence (AI), detailed in a paper recently published in the scientific journal Methods in Ecology and Evolution, represents a significant advancement in the study and conservation of wildlife. According to the paper’s authors, “the ability to rapidly identify millions of images from camera traps can fundamentally change the way ecologists design and implement wildlife studies.”

This study builds on previous research from the university in which a computer model analyzed 3.2 million images captured by camera traps in Africa. The A-I technique called deep learning categorized animal images at a 96.6% accuracy rate. This was the same accuracy rate as teams of human volunteers achieved, but the computer model worked at a much more rapid pace.

In the latest study, UW researchers trained a deep neural network on a powerful computer cluster to classify wildlife species using 3.37 million camera-trap images of 27 different animal species. The model was tested on nearly 375,000 images at a rate of about 2,000 images per minute. It achieved a 97.6% accuracy rate, which is likely the highest accuracy to date in using machine learning for wildlife image classification.

Artificial intelligence has been used in environmental science in other ways as well. For example, AI has been used to increase agricultural yields in farm fields and to help predict extreme weather.

Maybe artificial intelligence can prove to be a game changer for the environment.

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Researchers Successfully Train Computers to Identify Animals in Photos

Photo, posted January 8, 2012, courtesy of the U.S. Fish and Wildlife Service via Flickr.

Earth Wise is a production of WAMC Northeast Public Radio.

Ebola And Bats

August 29, 2016 By WAMC WEB

Filoviruses have devastating effects on people and primates, as evidenced by the 2014 Ebola outbreak in West Africa. For nearly 40 years, preventing spillovers has been hampered by an inability to pinpoint which wildlife species harbor and spread the viruses.

[Read more…] about Ebola And Bats

Big Data + Technology = Improved Global Health

June 20, 2016 By WAMC WEB

Scientists are calling for the creation of a global early warning system for infectious diseases. Such a system would use computer models to tap into environmental, epidemiological, and molecular data – gathering the intelligence needed to forecast where disease risk is high and what actions could prevent outbreaks or contain epidemics.