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Feasibility of principal component analysis for multi-class earthquake prediction machine learning model utilizing geomagnetic field data

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 dimension...

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Main Authors: Qaedi, Kasyful, Abdullah, Mardina, Yusof, Khairul Adib, Hayakawa, Masashi
Format: Article
Language:English
Published: Multidisciplinary Digital Publishing Institute 2024
Online Access:http://psasir.upm.edu.my/id/eprint/113525/1/113525.pdf
http://psasir.upm.edu.my/id/eprint/113525/
https://www.mdpi.com/2076-3263/14/5/121
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spelling my.upm.eprints.1135252024-11-26T03:20:46Z http://psasir.upm.edu.my/id/eprint/113525/ Feasibility of principal component analysis for multi-class earthquake prediction machine learning model utilizing geomagnetic field data Qaedi, Kasyful Abdullah, Mardina Yusof, Khairul Adib Hayakawa, Masashi 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. Multidisciplinary Digital Publishing Institute 2024 Article PeerReviewed text en cc_by_4 http://psasir.upm.edu.my/id/eprint/113525/1/113525.pdf Qaedi, Kasyful and Abdullah, Mardina and Yusof, Khairul Adib and Hayakawa, Masashi (2024) Feasibility of principal component analysis for multi-class earthquake prediction machine learning model utilizing geomagnetic field data. Geosciences, 14 (5). art. no. 121. pp. 1-11. ISSN 2076-3263; eISSN: 2076-3263 https://www.mdpi.com/2076-3263/14/5/121 10.3390/geosciences14050121
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description 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.
format Article
author Qaedi, Kasyful
Abdullah, Mardina
Yusof, Khairul Adib
Hayakawa, Masashi
spellingShingle Qaedi, Kasyful
Abdullah, Mardina
Yusof, Khairul Adib
Hayakawa, Masashi
Feasibility of principal component analysis for multi-class earthquake prediction machine learning model utilizing geomagnetic field data
author_facet Qaedi, Kasyful
Abdullah, Mardina
Yusof, Khairul Adib
Hayakawa, Masashi
author_sort Qaedi, Kasyful
title Feasibility of principal component analysis for multi-class earthquake prediction machine learning model utilizing geomagnetic field data
title_short Feasibility of principal component analysis for multi-class earthquake prediction machine learning model utilizing geomagnetic field data
title_full Feasibility of principal component analysis for multi-class earthquake prediction machine learning model utilizing geomagnetic field data
title_fullStr Feasibility of principal component analysis for multi-class earthquake prediction machine learning model utilizing geomagnetic field data
title_full_unstemmed Feasibility of principal component analysis for multi-class earthquake prediction machine learning model utilizing geomagnetic field data
title_sort feasibility of principal component analysis for multi-class earthquake prediction machine learning model utilizing geomagnetic field data
publisher Multidisciplinary Digital Publishing Institute
publishDate 2024
url http://psasir.upm.edu.my/id/eprint/113525/1/113525.pdf
http://psasir.upm.edu.my/id/eprint/113525/
https://www.mdpi.com/2076-3263/14/5/121
_version_ 1817844672810188800
score 13.223943

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