AI Can Predict Earthquake with 70 Percent Accuracy
- Science HowStuffWorks
Jakarta – In a development, researchers at the University of Texas at Austin have harnessed the potential of Artificial Intelligence-powered earthquake forecasting, offering hope for mitigating the destructive impact of seismic events.
During a seven-month trial in China, an Artificial Intelligence or AI algorithm was trained to detect statistical anomalies in real-time seismic data and historical earthquake records.
This development is set to improve swift aid response after earthquakes, which is most definitely a huge milestone.
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The AI accurately predicted 70% of earthquakes a week in advance, with 14 forecasts coming true within 200 miles of their estimated locations and matching their anticipated magnitudes.
However, it issued eight false warnings and missed one earthquake, as quoted from OAG site.
The results represent a significant step forward in pursuing AI-driven earthquake forecasting, a long-standing challenge.
Sergey Fomel, a research team member, referred to earthquake prediction as the “holy grail” and noted that while global forecasts are not yet within reach, the study demonstrates the problem’s solvability in principle.
The AI developed by the University of Texas took first place among 600 other designs in an international competition in China. Yangkang Chen, a seismologist and the AI’s lead developer led UT’s winning entry. Their findings have been published in the Bulletin of the Seismological Society of America.
Earthquakes strike suddenly, leaving no time for preparation. Despite the challenges, a 70% prediction accuracy could substantially reduce economic and human losses, significantly enhancing global earthquake preparedness.
Researchers believe that in regions with robust seismic monitoring networks, such as California, Italy, Japan, Greece, Turkey, and Texas, the AI’s success rate could improve, providing more precise predictions within a few tens of miles.
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The researchers plan to test the AI in Texas, which experiences frequent minor and moderate-magnitude earthquakes.
The state’s comprehensive seismic network and extensive records make it an ideal testing ground. Ultimately, the team aims to combine physics-based models with data-driven methods to create a generalised earthquake prediction system applicable worldwide.
