Applying a New Feature Selection Method for Accurate Prediction of Earthquakes Using a Soft Voting Classifier

Earthquakes are among the most hazardous natural disasters, posing significant threats to infrastructure, property and human life. This is primarily due to the sudden nature of earthquakes, which often provide little to no time for preparation. Consequently, the issue of earthquake prediction is crucial for human safety. Developing a reliable and highly accurate earthquake prediction model using machine learning (ML) methods can enhance our understanding of these complex natural phenomena, ultimately aiding in preserving lives and mitigating earthquake-related damage. In this study, we propose a novel feature selection approach that integrates two methods: normalisation based on analysis of variance and the Chi-squared technique, along with correlation based on Logistic Regression (CLR-AVCH). This approach aims to identify the most relevant features to expedite model training, minimise errors and optimise outcomes. We employ three algorithms (Support Vector Machine, Decision Tree and Random Forest) to uncover and identify patterns in the collected data. A soft voting classifier is then constructed, combining the best-performing models (Decision Tree and Random Forest) to create a unified model that leverages both strengths, improving prediction accuracy. The proposed methodology achieves high-performance metrics, including accuracy, F1 score, recall and precision (0.99, 0.98, 0.98 and 0.98, respectively). Future work will focus on implementing new feature selection techniques alongside hybrid algorithms with soft voting classifiers to enhance diagnostic capabilities.