Application of machine learning in earthquakes detection

This comparison analyzes seven research papers that explore the use of machine learning (ML) for earthquake detection, employing various algorithms including AdaBoost, Backpropagation, Artificial Neural Networks (ANN), Support Vector Machines (SVM), Decision Trees, Random Forests, and Probabilistic Neural Networks. The evaluation metrics used across these studies include accuracy, precision, recall, F-measure, sensitivity, specificity, false alert ratio, probability of detection, negative predictive error, positive predictive error, mean absolute error, and mean squared error. Each paper assesses the effectiveness of these algorithms in terms of their detection performance and predictive accuracy, providing a comprehensive overview of the strengths and limitations of different ML methods in earthquake detection.