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Grindulu fault cloud radon data for earthquake magnitude prediction using machine learning

IAES International Journal of Artificial Intelligence · Vol. 13 No. 4 (2024) · hlm. 4572–4582

DOI
10.11591/ijai.v13.i4.pp4572-4582
ISSN
2252-8938, 2089-4872
Terbit
2024-12-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

The study investigates the potential of integrating radon gas concentration telemonitoring systems with machine learning techniques to enhance earthquake magnitude prediction. Conducted in Pacitan, East Java, Indonesia, where the stations are near the active Grindulu fault, the research employs random forest (RF), extreme gradient boosting (XGB), neural network (NN), AdaBoost (AB), and support vector machine (SVM) methods. The study aims to refine earthquake magnitude prediction, utilizing real-time radon gas concentration measurements, crucial for disaster preparedness. The evaluation involves multiple metrics like mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), mean squared error (MSE), symmetric mean absolute percentage error (SMAPE), and conformal normalized mean absolute percentage error (cnSMAPE). XGB and SVM emerge as top performers, showcasing superior predictive accuracy with minimal errors across various metrics. XGB achieved MAE (0.33), MAPE (6.03%), RMSE (0.51), MSE (0.26), SMAPE (0.06), and cnMAPE (0.97), while SVM recorded MAE (0.34), MAPE (6.20%), RMSE (0.51), MSE (0.26), SMAPE (0.06), and cnSMAPE (0.97). The analysis reveals XGB as the most effective method, boasting the lowest error values. The study underscores the importance of expanding data availability to enhance predictive models, ultimately contributing to more precise earthquake magnitude predictions and effective mitigation strategies.

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  • Universitas Gadjah Mada
  • Universitas Syiah Kuala
  • Politeknik Pertanian Negeri Kupang
  • National Research and Innovation Agency
  • Tokyo Institute of Technology
  • Muhammadiyah University of Yogyakarta
  • Tanjungpura University
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Rekan penulisOrganisasiArtikel bersama
Tjahyo Nugroho AdjiUniversitas Gadjah Mada5
Ahmad CahyadiUniversitas Gadjah Mada4
Muhammad NaufalUniversitas Gadjah Mada3
Bambang Hendro SunarmintoUniversitas Gadjah Mada2
Indra Agus RiyantoUniversitas Gadjah Mada2
M WidyastutiUniversitas Gadjah Mada2
Husna DiahUniversitas Syiah Kuala2
Risanuri HidayatUniversitas Gadjah Mada1
Budi SetiyantoUniversitas Gadjah Mada1
I Wayan MustikaUniversitas Gadjah Mada1
Alfred Umbu Kuala NgajiPoliteknik Pertanian Negeri Kupang1
Muhammad BaiquniUniversitas Gadjah Mada1
Hatma SuryatmojoUniversitas Gadjah Mada1
Setiadi SetiadiUniversitas Gadjah Mada1
Eka SulistiyowatiUniversitas Gadjah Mada1
Arif AnshoriNational Research and Innovation Agency1
Deepak GautamTokyo Institute of Technology1
Dwi Joko SurosoUniversitas Gadjah Mada1
Dibyo PramonoUniversitas Gadjah Mada1
Rinaldi Budi UtomoUniversitas Gadjah Mada1
Laelia Dwi AnggrainiMuhammadiyah University of Yogyakarta1
Margaretha WidyastutiUniversitas Gadjah Mada1
Dzakwan Taufiq Nur MuhammadUniversitas Gadjah Mada1
Naufal Fattah TastianUniversitas Gadjah Mada1
J. Susetyo Edy YAfiliasi tidak diketahui1
Imam FauziAfiliasi tidak diketahui1
Susetyo DidikAfiliasi tidak diketahui1
Wahyu Broto RAfiliasi tidak diketahui1
Rossie Wiedya NusantaraTanjungpura University1
Sudarmadji SudarmadjiUniversitas Gadjah Mada1
Tjut S. DjohanUniversitas Gadjah Mada1
Eko HanudinUniversitas Gadjah Mada1
Miranti ArianiUniversitas Gadjah Mada1
Indranova SuhendroUniversitas Gadjah Mada1
Rini RachmawatiUniversitas Gadjah Mada1
Estuning Tyas Wulan MeiUniversitas Gadjah Mada1

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Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Grindulu fault cloud radon data for earthquake magnitude prediction using machine learning</dc:title>
	<dc:creator>Pratama, Thomas Oka</dc:creator>
	<dc:creator>Sunarno, Sunarno</dc:creator>
	<dc:creator>Wijatna, Agus Budhie</dc:creator>
	<dc:creator>Haryono, Eko</dc:creator>
	<dc:subject xml:lang="en-US">Telemonitoring, Disaster Mitigation, Early Warning System</dc:subject>
	<dc:subject xml:lang="en-US">Earthquake; Machine learning; Magnitude; Prediction; Radon</dc:subject>
	<dc:description xml:lang="en-US">The study investigates the potential of integrating radon gas concentration telemonitoring systems with machine learning techniques to enhance earthquake magnitude prediction. Conducted in Pacitan, East Java, Indonesia, where the stations are near the active Grindulu fault, the research employs random forest (RF), extreme gradient boosting (XGB), neural network (NN), AdaBoost (AB), and support vector machine (SVM) methods. The study aims to refine earthquake magnitude prediction, utilizing real-time radon gas concentration measurements, crucial for disaster preparedness. The evaluation involves multiple metrics like mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), mean squared error (MSE), symmetric mean absolute percentage error (SMAPE), and conformal normalized mean absolute percentage error (cnSMAPE). XGB and SVM emerge as top performers, showcasing superior predictive accuracy with minimal errors across various metrics. XGB achieved MAE (0.33), MAPE (6.03%), RMSE (0.51), MSE (0.26), SMAPE (0.06), and cnMAPE (0.97), while SVM recorded MAE (0.34), MAPE (6.20%), RMSE (0.51), MSE (0.26), SMAPE (0.06), and cnSMAPE (0.97). The analysis reveals XGB as the most effective method, boasting the lowest error values. The study underscores the importance of expanding data availability to enhance predictive models, ultimately contributing to more precise earthquake magnitude predictions and effective mitigation strategies.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US">Sensor System and Tele-Control Laboratory, Department of Nuclear Engineering and Engineering Physics, Universitas Gadjah Mada, Indonesia, Postdam Geofon and the Badan Meteorologi, Klimatologi dan Geofisika Indonesia, and the RTA Program at Universitas Gad</dc:contributor>
	<dc:date>2024-12-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/25221</dc:identifier>
	<dc:identifier>10.11591/ijai.v13.i4.pp4572-4582</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 13, No 4: December 2024; 4572-4582</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v13.i4</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/25221/14299</dc:relation>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/downloadSuppFile/25221/3552</dc:relation>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/downloadSuppFile/25221/3553</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>