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Using AI To Determine The Possible Extent Of Nuclear Fallout | Touchstone Words

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Using AI to Determine the Possible Extent of Nuclear Fallout

By Katie Lee on 2018-12-18

Researchers have created a machine-learning-based tool that can predict where radioactive emissions from nuclear power plants will disperse. After training using extensive data on previous weather patterns, the tool consistently achieved over 85 percent predictive accuracy, and up to 95 percent in winter when large and predictable weather systems dominate. This tool can aid immediate evacuation in the aftermath of disasters like those at Fukushima and Chernobyl.

From the nuclear wastes of the video game Fallout, people have come to believe that the world will be totally wrecked in the event of a nuclear war or even a power plant accident. But this may not be the case. Researchers are now using AI to determine the likely distribution of radioactivity in the event of such an event.

When a nuclear power plant accident occurs and radioactive material is released, it is vital to evacuate people in the vicinity as quickly as possible. This is because radioactive particles can cause a wide array of mutations and can, of course, lead to death if a person were to become too irradiated. Said mutations may lead to simple sicknesses rivaling that of the common cold among other effects. Further, high concentrations of radiation can lead to harsh and very painful burns at the site of exposure. Moreover, if radiation were to pollute our waterways, this can lead to an epidemic of sickness and death of people who use or consume this water. However, it can be difficult to immediately predict where the emitted radioactivity will settle, making it impossible to prevent the exposure of large numbers of people.                                                                                                                

A means of overcoming this difficulty has been presented in a new study reported in the journal Scientific Reports by a research team at The University of Tokyo Institute of Industrial Science. The team has created a computer program that can accurately predict where radioactive material that has been emitted will eventually land, over 30 hours in advance. This is computed by taking in a plethora of factors including wind speed, humidity, precipitation, size of the particles and the plant’s proximity to water sources such as rivers and bays. This tool enables evacuation plans and other health-protective measures to be implemented if another nuclear accident like in 2011 at the Fukushima Daiichi Nuclear Power Plant were to occur.

This latest study was prompted by the limitations of existing atmospheric modeling tools in the aftermath of the accident at Fukushima; tools considered so unreliable that they were not used for planning immediately after the disaster. In this context, the team created a system based on a form of artificial intelligence called machine learning, which can use data on previous weather patterns to predict the route that radioactive emissions are likely to take.

"Our new tool was first trained using years of weather-related data to predict where radioactivity would be distributed if it were released from a particular point," lead author Takao Yoshikane says. "In subsequent testing, it could predict the direction of dispersion with at least 85% accuracy, with this rising to 95% in winter when there are more predictable weather patterns."

"The fact that the accuracy of this approach did not decrease when predicting over 30 hours into the future is extremely important in disaster scenarios," Takao Yoshikane says. "This gives authorities time to arrange evacuation plans in the most badly affected areas, and to issue guidance to people in specific areas about avoiding eating fresh produce and taking potassium iodide, which can limit the absorption of ingested radioactive isotopes by the body."

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