Feasibility of Principal Component Analysis for Multi-Class Earthquake Prediction Machine Learning Model Utilizing Geomagnetic Field Data

Author:

Qaedi Kasyful1ORCID,Abdullah Mardina12ORCID,Yusof Khairul Adib13ORCID,Hayakawa Masashi45

Affiliation:

1. Space Science Center, Institute of Climate Change, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia

2. Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia

3. Department of Physics, Faculty of Science, Universiti Putra Malaysia (UPM), Seri Kembangan 43400, Malaysia

4. Hayakawa Institute of Seismo Electromagnetics Co., Ltd. (Hi-SEM), UEC Alliance Center, 1-1-1 Kojimacho, Chofu 182-0026, Japan

5. Advanced & Wireless Communications Research Center (AWCC), The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu 182-8585, Japan

Abstract

Geomagnetic field data have been found to contain earthquake (EQ) precursory signals; however, analyzing this high-resolution, imbalanced data presents challenges when implementing machine learning (ML). This study explored feasibility of principal component analyses (PCA) for reducing the dimensionality of global geomagnetic field data to improve the accuracy of EQ predictive models. Multi-class ML models capable of predicting EQ intensity in terms of the Mercalli Intensity Scale were developed. Ensemble and Support Vector Machine (SVM) models, known for their robustness and capabilities in handling complex relationships, were trained, while a Synthetic Minority Oversampling Technique (SMOTE) was employed to address the imbalanced EQ data. Both models were trained on PCA-extracted features from the balanced dataset, resulting in reasonable model performance. The ensemble model outperformed the SVM model in various aspects, including accuracy (77.50% vs. 75.88%), specificity (96.79% vs. 96.55%), F1-score (77.05% vs. 76.16%), and Matthew Correlation Coefficient (73.88% vs. 73.11%). These findings suggest the potential of a PCA-based ML model for more reliable EQ prediction.

Funder

Malaysia Ministry of Higher Education

Publisher

MDPI AG

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