Leading machine learning model achieves 97.97% accuracy in predicting earthquakes in Los Angeles

Cemil Emre Yavas – Principal Researcher

Photo of Cemil Yavas, lead researcher on the earthquake prediction study

STATESBORO, Georgia, Oct. 25, 2024 (GLOBE NEWSWIRE) — Semel Emre Yavash and his research team have achieved a remarkable 97.97% accuracy in predicting earthquakes in Los Angeles, a high-risk seismic area, by incorporating advanced machine learning algorithms into seismic prediction.

The team's pioneering study was published in Scientific Reports by Nature, one of the leading journals in the field (https://www.nature.com/articles/s41598-024-76483-x). This publication highlights important advances in using machine learning to accurately predict seismic activities, and showcases the global importance and impact of their work.

Their success has also received global recognition, including recognition from the United Nations, which showcased the research on its core disaster risk reduction platform, PreventionWeb (https://www.preventionweb.net/publication/improving-earthquake-prediction-accuracy-los). – Angeles-machine learning). This recognition highlights the potential of this breakthrough to enhance public safety and disaster preparedness in seismic regions.

Experts believe this pioneering research will change the understanding of natural disasters, provide a critical tool for managing disaster risks and enhancing our ability to prepare for and mitigate earthquake risks.

Reflecting on this achievement, Cemil Emre Yavash commented: “Our model’s 97.97% accuracy represents a significant improvement over traditional methods, providing important insights that can save lives and reduce property damage in high-risk areas.”

“This research opens new doors for the application of machine learning in disaster risk management, offering predictive tools that can make a real difference in preparedness efforts,” Professor Li Chen reiterated.

“Integrating advanced machine learning algorithms such as Random Forest and neural networks has allowed us to break new ground in earthquake prediction,” Professor Yiming Ji added.

Professor Christopher Kadlec commented: “Our team's work not only pushes the boundaries of earthquake prediction, but also paves the way for future advances in applying machine learning to other natural disaster prediction models. The implications for improving public safety and emergency response are enormous.”

There is no doubt that this achievement will revolutionize how we deal with disaster preparedness, especially in the field of earthquake prediction.

The image accompanying this announcement is available at https://www.globenewswire.com/NewsRoom/AttachmentNg/a328a326-52a3-49a9-88e8-ac053c5a257e

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Contact: Contact information: Research Leader Cemil Emre Yavas, Georgia Southern University Email: [email protected]

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