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Earthquake Prediction Using Machine Learning Techniques

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The Random Forest model achieved an accuracy of 88.84% in predicting significant earthquakes.
Evaluation metrics like confusion matrix and ROC-AUC score confirmed the effectiveness of the machine learning models.
Data preprocessing, including handling missing values and feature normalization, was vital for model performance.
Implementing these techniques highlights the potential for improving disaster preparedness and emergency response planning.

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Data-driven models improve earthquake prediction accuracy in analyzing seismic events, suggesting enhanced early warning systems.

Lankalapalli Sailaja (2025) studied this question.

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