Predicting earthquakes remains one of science’s most challenging tasks. However, modern technologies like artificial intelligence and machine learning are proving effective in analyzing seismic data. Over recent years, numerous promising studies have emerged in this field.
Seismic Algorithms from the Philippines
Researchers from the Graduate School of Polytechnic University of the Philippines, in collaboration with the University of the Philippines, published a study employing advanced machine learning models ranging from classical ARIMA to neural networks like CNN and LSTM. The research was based on earthquake magnitude data provided by the company DATA CUBE. Their approach significantly improved the accuracy of long-term forecasting.
The study was published on April 1, 2024, and the full results can be accessed on ResearchGate.
A Global Perspective: Iran and the USA
In June 2024, researchers from Shahid Beheshti University (Tehran) and Emory University (Atlanta) presented a joint study using SVM, ANN, and Random Forest algorithms to analyze seismic activity. The primary goal was to classify whether an earthquake is expected (class 1) or not (class 0).
Although the project faced challenges due to the limited dataset size, it demonstrated that meaningful results can still be achieved under such conditions.
Georgian Precision
Georgian researchers from the Institute of Geophysics at Tbilisi State University introduced an innovative approach to earthquake forecasting by analyzing changes in water levels in deep wells. Using Decision Tree and SVM algorithms, they successfully predicted earthquakes with magnitudes above 3.5 one day in advance.
This study highlights the importance of local data, such as water resource conditions, in accurately forecasting seismic activity.
An American Breakthrough
A team of researchers from the University of California (Berkeley and Santa Cruz) and the Technical University of Munich developed a scalable model called RECAST, capable of predicting earthquakes 14 days in advance. The team utilized synthetic data and real seismic data from Southern California.
RECAST’s unique feature is its ability to improve accuracy as the dataset grows, making it a promising tool for application in other regions.
The Future Belongs to Artificial Intelligence
All the presented projects demonstrate that the key to successful earthquake forecasting lies in data and the technologies used to analyze it. Methods like neural networks, decision trees, and deep learning not only reveal patterns in seismic processes but also provide early warnings for potential disasters.
The only question that remains is how much time and resources it will take to integrate such systems into the global seismic infrastructure.