SeismoCastNet: A Multi-Model Synergetic Framework For Predicting High-Magnitude Earthquakes With Spatiotemporal Precision

This research explores the utilization of machine learning techniques to enhance short-term earthquake forecasting, contributing to improved disaster preparedness and risk reduction. A detailed review of both conventional and data-driven seismic prediction methods was conducted, revealing notable limitations in current systems. To address these challenges, the proposed framework—SeismoCastNet—employs a hybrid approach by integrating five classification models, including Random Forest, Gradient Boosting, and Support Vector Machine (SVM). The models were trained and validated using historical earthquake datasets encompassing attributes such as magnitude, depth, and geographical coordinates. Experimental outcomes demonstrate that Gradient Boosting delivers the most consistent and reliable performance, achieving an overall accuracy of 96%, a minor class F1-score of 0.979, and a major class F1-score of 0.545. While SVM attained the highest precision for minor class predictions (99%), its performance for major seismic events was relatively lower. The findings underscore the potential of ensemble learning strategies to effectively handle class imbalance and improve the predictive capability of earthquake detection systems.