Implementation of Random Forest Classifier for Real-time Earthquake Detection System

Abstract

An earthquake is one disaster that happened unpredictably and in some cases, it harms humanity. There are lots of research that studies earthquake vibrations using machine learning algorithms. However, implementing it in real-time application systems such as early warning systems is quite challenging due to the similarity of earthquake vibrations and non-earthquake vibrations (human activities and noises). Therefore, this study proposed an earthquake detection with Random Forest Classifier to distinguish earthquake and non-earthquake vibrations in a real-time application earthquake detection system. This study shows that Random Forest Classifier in a detection device is capable of classifying non-earthquake vibrations very well while it can classify earthquake vibrations with a success rate of 78.89%.