Earthquakes are abrupt and highly destructive phenomena, presenting a persistent challenge for accurate forecasting. The unpredictable and intricate behavior of seismic events often limits the effectiveness of conventional geological prediction techniques. This paper proposes a data-driven approach for earthquake prediction using supervised machine learning algorithms trained on historical seismic datasets. The study focuses on binary classification to determine whether an earthquake is significant, defined as having a magnitude of 6.0 or higher. Data preprocessing steps included handling missing values, encoding categorical features, and normalizing inputs. Two models—Random Forest and Support Vector Machine (SVM)— were implemented and compared based on their ability to classify seismic events. The Random Forest model achieved a higher accuracy of 88.84%, along with better recall and F1-scores in identifying significant earthquakes. Evaluation metrics such as the confusion matrix, ROC-AUC score, and feature importance analysis affirmed the effectiveness of the proposed models. The findings demonstrate that machine learning techniques can play a vital role in enhancing early warning systems and seismic risk assessment by improving the prediction of earthquake severity. This approach has the potential to support decision-making for disaster preparedness and emergency response planning. Future enhancements could include integrating real-time geospatial data and applying deep learning architectures to further improve model performance.
Lankalapalli Sailaja (Thu,) studied this question.