Classifying and Forecasting Seismic Event Characteristics Using Artificial Intelligence 

Abstract

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 forecast their magnitude. The seismic activities categorized into groups by magnitude to enhance the understanding of these phenomena. Location-Based and Seismic Characteristics Features are utilized in seven machine learning models: Rule-Based Classifier, K-mean Classifier, Decision Trees, Random Forest, Support Vector Machines (SVM), k-Nearest Neighbors (kNN), and Logistic Regression. This approach aims to provide valuable insights into seismic activities, contributing to the development of more nuanced disaster analysis and early warning systems.