Researchers in South Georgia break new ground in earthquake prediction

From heartbreak to heroism, Jamil Yavas turned his disaster survival story into a revolutionary breakthrough, an AI model that can predict Los Angeles earthquakes a month in advance with an astonishing 98% accuracy.

Cemil Yavas knows firsthand the devastation that earthquakes can cause to communities, which has led him to research how to predict this phenomenon for many years.

“My interest in earthquake prediction research stems from deep personal experience,” Yavas said. “I was in Istanbul during the devastating earthquake of August 17, 1999, a disaster that claimed nearly 18,000 lives and left hundreds of thousands homeless. Among those affected were some of my close friends. “The chaos that ensued – the limited resources, the strained rescue efforts, and the haunting sound of people trapped under the rubble – left a lasting impression on me.”

Jamil Yavas, in collaboration with Yiming Ji, Ph.D., Li Chen, Ph.D., and Christopher Kadlec, Ph.D., faculty and researchers in the Department of Information Technology in the Allen Paulson School of Engineering and Computing used his personal motivations to build on previous research and enhance earthquake prediction models.

Using leading machine learning algorithms and neural network techniques, they can now predict the highest magnitude potential earthquakes in Los Angeles for up to a month with up to 97.97% accuracy.

“The inspiration for this research stems from the urgent need to improve the accuracy of earthquake prediction in seismically active urban areas like Los Angeles,” Yavas said. “Earthquakes pose significant risks to densely populated areas, affecting public safety, infrastructure and the economy.”

In a joint 2023 report by the USGS and the Federal Emergency Management Agency, damage from earthquakes estimated the United States at $14.7 billion annually, with California bearing nearly two-thirds of that burden, about $9.6 billion annually.

Yavas and his mentors are optimistic that innovation in forecasting techniques, such as machine learning, specifically neural networks — software that aims to mimic human decision-making — can mitigate its devastating effects. Using a multi-layered, connected architecture similar to neurons in our brains, the network can detect patterns, weigh options and reach conclusions.

These techniques may not be top of mind when studying and predicting earthquakes. According to Gee, seismology was limited to geography and other earth sciences until recently.

“At its core, machine learning involves teaching computers how to find patterns in data, often revealing insights that may be too subtle or complex for humans to easily detect,” Ji said. “In seismology, this means we can analyze huge amounts of earthquake data – from ground vibrations to atmospheric conditions – and detect patterns that may indicate when and where future earthquakes are likely to occur.

“By feeding seismic data into machine learning models, we enable these systems to ‘learn’ from past earthquakes and make predictions about the magnitude and probability of future seismic events,” Ji continued.

This is not the first time the team has dealt with earthquake prediction. They used similar techniques in previous research and achieved an accuracy rate of 69.14%. Subsequently, their work expanded to other seismically active sites such as Istanbul and San Diego, where their results improved dramatically. They obtained rates of 91.65% and 98.53%, respectively.

Their years of experience and previous attempts have enhanced their current research.

“Our team has formally come together to focus on this research over the past year, but the journey began much earlier than that,” Chen said. “We both have a significant background in machine learning. Before starting this project, we independently explored earthquake prediction methods, gaining insights that ultimately contributed to our collaborative approach. This research represents the culmination of those years of groundwork, enriched by the collective expertise provided by All of us at the table.

The project, funded in part by the National Science Foundation's S-STEM program and supported by the Allen E. Paulson College of Engineering and Computing, allowed the quartet to build on momentum. They adopted a comprehensive dataset that included all earthquakes since 2012 and used well-established variables in earthquake prediction.

Ultimately, they developed a feature matrix and evaluated 16 different machine learning algorithms and neural networks for their accuracy in identifying the highest magnitude of potential earthquakes within 30 days. The “random forest” model emerged as the best performer, achieving an accuracy level of 97.97%.

This research was recently published in the multidisciplinary journal Nature.

Although the research is limited to the Los Angeles area, according to Kadlec, it will likely improve forecasting methods in other areas.

“While we trained our prototype on Los Angeles-specific data, the techniques and methodologies we developed are versatile,” Kadlec said. “Thanks to local data – such as geological, atmospheric and seismic information from other regions – our model can be adapted and retrained to predict earthquakes elsewhere.”

He added something that signified the team's larger goal.

“Ultimately, our goal is to make machine learning a central tool in earthquake prediction and to inspire continued progress in AI applications for natural disaster preparedness around the world,” Kadlec said. “This research is just the beginning, and we hope it will motivate others to move forward in developing the tools needed to keep communities safe.”

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