Classifying seismic event parameters using artificial intelligence

Seismic events present a significant global threat, underscoring the need for effective models to provide insights into these natural disasters. This paper addresses the critical need for advanced seismic event analysis by combining traditional data analysis with cutting-edge machine learning models. The primary objective is to develop models that classify seismic events into different types based on their geological and seismic characteristics and their magnitude. Seismic activities are categorized into groups by magnitude to enhance understanding of these phenomena. Location-based and seismic characteristic features are utilized in seven machine learning models: rule-based classifier, K-mean classifier, decision trees, Random Forest, Support Vector Machine (SVM), k-nearest Neighbors (kNN), and logistic regression. This approach aims to provide valuable insights into seismic activities, contributing to developing more nuanced disaster analysis.