An Investigation into the Effectiveness of MachineLearning Algorithms for earthquake Magnitude Prediction using Seismic Data

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The present study aimed to evaluate the performance of six different regression models for earthquake magnitude prediction using seismic data, a novel approach in the field of earthquake prediction. The selected models were K-Nearest Neighbors (KNN), linear regression, decision trees, random forest, support vector regression, neural networks, and gradient boosting. The evaluation of the models was based on various metrics such as mean squared error (MSE), mean absolute error (MAE), R-squared score, and explained variance score. The data was split into training and testing sets to ensure that the models were being evaluated on unseen data and to avoid overfitting. The results showed that the random forest and gradient boosting models performed the best in predicting earthquake magnitudes using seismic data, which is a significant finding in the field of earthquake prediction. These models had the highest R-squared scores, which indicates that they captured a significant portion of the variance in the target variable, magnitude. On the other hand, the neural networks and support vector regression models performed poorly, with negative R-squared scores, suggesting that they were not a good fit for the data. This study provides a novel contribution to the field of earthquake prediction, as it provides valuable insights into the effectiveness of machine learning algorithms for earthquake magnitude prediction using seismic data. The results clearly demonstrate that the random forest and gradient boosting models are the most effective models for this task, which has practical implications for earthquake hazard assessment and disaster management. The results of this study contribute to the growing body of knowledge in the field of earthquake prediction and have the potential to inform future research in this area.