Students develop AI that calculates when and where lightning will strike -with ...

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Students develop AI that calculates when and where lightning will strike - and the technology can predict a bolt 30 MINUTES before it happens with 80% accuracy System uses meteorological data and artificial intelligence to predict lightning Looks at atmospheric pressure, air temperature, humidity and wind speed Can predict lightning 30 minutes before and within an 18 mile radius 

By Stacy Liberatore For Dailymail.com

Published: 18:58 GMT, 8 November 2019 | Updated: 19:00 GMT, 8 November 2019

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Lightning has been deemed 'the most unpredictable phenomena in nature' - until now.

By combining meteorological data and artificial intelligence, students have determined when and where lightening will strike within 10 to 30 minutes of it happening - and with an 80 percent accuracy

The technology is set to work as an early warning system to prevent effects of lightning strikes to critical infrastructure, sensitive equipment and outdoor facilities.

The system, developed by students at École polytechnique fédérale de Lausanne (EPFL School of Technology), is capable of predicting when and where lighting will strike to the nearest 10 to 30 minutes, within an 18 mile radius.

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Lightning has been deemed 'the most unpredictable phenomena in nature' -until now. By combining meteorological data and artificial intelligence, students have determined when and where lightening will strike within 10 to 30 minutes

Lightning has been deemed 'the most unpredictable phenomena in nature' -until now. By combining meteorological data and artificial intelligence, students have determined when and where lightening will strike within 10 to 30 minutes

Amirhossein Mostajabi, the Ph.D. student who came up with the technique, said, 'Current systems are slow and very complex, and they require expensive external data acquired by radar or satellite.'

'Our method uses data that can be obtained from any weather station. That means we can cover remote regions that are out of radar and satellite range and where communication networks are unavailable.'

The EPFL researchers' method uses a machine-learning algorithm that has been trained to recognize conditions that lead to lightning.

It analyzes atmospheric pressure, air temperature, relative humidity and wind speed. 

The EPFL researchers' method uses a machine-learning algorithm that has been trained to recognize conditions that lead to lightning. It analyzes atmospheric pressure, air temperature, relative humidity and wind speed

The EPFL researchers' method uses a machine-learning algorithm

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