Strike twice in the same place? AI can now predict where lightning will strike
Deccan Chronicle | DC Correspondent
Researchers have developed a simple and inexpensive system that can predict when lightning will strike within a 30km radius.
The system uses a combination of standard meteorological data and artificial intelligence. (Photo: ANI)
Since lightning is considered one of the most unpredictable natural occurrences of nature, researchers found a method of predicting when lightning may strike.
At EPFL's School of Engineering, researchers in the Electromagnetic Compatibility Laboratory, led by Farhad Rachidi, have developed a simple and inexpensive system that can predict when lightning will strike to the nearest 10 to 30 minutes, within a 30-kilometre radius.
The system uses a combination of standard meteorological data and artificial intelligence.
The research paper published in -- Climate and Atmospheric Science -- a Nature partner journal. The researchers are now planning to use their technology in the European Laser Lightning Rod project.
"Current systems are slow and very complex, and they require expensive external data acquired by radar or satellite," explained Amirhossein Mostajabi, the PhD student who came up with the technique.
"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," added Mostajabi.
What's more, because the data can be acquired easily and in real-time, predictions can be made very quickly -- and alerts can be issued even before a storm has formed.
The EPFL researchers' method uses a machine-learning algorithm that has been trained to recognise conditions that lead to lightning.
To carry out the training, the researchers used data collected over a ten-year period from 12 Swiss weather stations, located in both urban and mountainous areas.
Four parameters were taken into account: atmospheric pressure, air temperature, relative humidity, and wind speed. Those parameters were correlated with recordings from lightning detection and location systems.
Using that method, the algorithm was able to learn the conditions under which lightning occurs.
Once trained, the system made predictions that proved correct almost 80% of the time.
This is the first time that a system based on simple meteorological data has been able to predict lightning strikes through real-time calculations. The method offers a simple way of predicting a complex phenomenon.