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

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Overall accuracy reached 96%, demonstrating the predictive power of the SeismoCastNet framework.
Gradient Boosting showed the most consistent performance, significantly improving earthquake detection accuracy.
Hybrid approach employs five models, including Random Forest and SVM, to address limitations in traditional seismic prediction.
Findings highlight the potential benefits of ensemble learning strategies, which can effectively manage class imbalance.

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Observational analysis integrates machine learning models to enhance earthquake forecasting accuracy, suggesting improved disaster preparedness.

A. Shankar (2025) studied this question.

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Observational analysis integrates machine learning models to enhance earthquake forecasting accuracy, suggesting improved disaster preparedness.