Using Soft Voting Classifier of Machine Learning Algorithms for Earthquake Prediction

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Earthquakes are among the most dangerous natural disasters that can cause major losses to buildings and threaten human lives. The research community is very interested in the topic of earthquakes because they occur suddenly and predicting them is very important for human safety. Creating accurate earthquake prediction techniques by applying machine learning (ML) approaches will help save people's lives and prevent damage. To identify important features and analyze the correlation between these features before submitting them to classification models, we proposed a new feature selection approach in this paper which combines two filtering ways: Normalization which is based on the Chi-square approach and analysis of variance, and the correlation approach based on the logistic regression technique (CLR-AVCH). Accordingly, three algorithms are applied. Then a facilitated voting classifier is created that combines the two best models with the highest prediction accuracy (histogram-based gradient boosting, adaptive boosting) to create a single technique that includes the strengths of the techniques that were combined to help find important patterns in the acquired data to obtain a model capable of early prediction of earthquakes. The proposed work achieved higher accuracy, F1_score, recall, and precision (0.94, 0.92, 0.94, 0.92), respectively.