IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v120y2024i14d10.1007_s11069-024-06724-9.html
   My bibliography  Save this article

On the use of VMD-LSTM neural network for approximate earthquake prediction

Author

Listed:
  • Qiyue Wang

    (Beijing Technology and Business University (BTBU))

  • Yekun Zhang

    (Beijing Technology and Business University (BTBU))

  • Jiaqi Zhang

    (Beijing Technology and Business University (BTBU))

  • Zekang Zhao

    (Beijing Technology and Business University (BTBU))

  • Xijun He

    (Beijing Technology and Business University (BTBU))

Abstract

Earthquake prediction has been widely studied in many fields using various technologies, including machine learning, which is able to explore the underlying information of data. This study adopts machine learning for earthquake prediction, and employs the well-studied long short-term memory (LSTM) neural networks to predict the earthquake occurrence time, longitude, latitude, and magnitude. A variational mode decomposition (VMD) approach is also used to improve the precision of the precision. The proposed model is referred to as VMD-LSTM. The used datasets consist of earthquake catalogs from 1935 to 2023 in the Taiwan region, where earthquakes having magnitudes greater than 5.0 are studied. Four VMD-LSTMs are constructed to predict the earthquake occurrence time, longitude, latitude, and magnitude. The experimental results show that the proposed model has high performance on the test set. The results are also compared with those obtained by the LSTM model without VMD, verifying that the VMD-LSTM model leads to higher performance. This study introduces a novel model for the prediction of earthquake magnitude, location, and time. It also demonstrates that the VMD method can be combined with various neural networks to increase the prediction accuracy.

Suggested Citation

  • Qiyue Wang & Yekun Zhang & Jiaqi Zhang & Zekang Zhao & Xijun He, 2024. "On the use of VMD-LSTM neural network for approximate earthquake prediction," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(14), pages 13351-13367, November.
    • Handle: RePEc:spr:nathaz:v:120:y:2024:i:14:d:10.1007_s11069-024-06724-9
    DOI: 10.1007/s11069-024-06724-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-024-06724-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11069-024-06724-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Huang Xing & Song Junyi & Huidong Jin, 2020. "The casualty prediction of earthquake disaster based on Extreme Learning Machine method," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 102(3), pages 873-886, July.
    2. Khawaja M Asim & Adnan Idris & Talat Iqbal & Francisco Martínez-Álvarez, 2018. "Earthquake prediction model using support vector regressor and hybrid neural networks," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-22, July.
    3. Phoebe M. R. DeVries & Fernanda Viégas & Martin Wattenberg & Brendan J. Meade, 2018. "Deep learning of aftershock patterns following large earthquakes," Nature, Nature, vol. 560(7720), pages 632-634, August.
    4. Jiaqi Zhang & Xijun He, 2023. "Earthquake magnitude prediction using a VMD-BP neural network model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(1), pages 189-205, May.
    5. K. M. Asim & F. Martínez-Álvarez & A. Basit & T. Iqbal, 2017. "Earthquake magnitude prediction in Hindukush region using machine learning techniques," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 85(1), pages 471-486, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jiaqi Zhang & Xijun He, 2023. "Earthquake magnitude prediction using a VMD-BP neural network model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(1), pages 189-205, May.
    2. Wenjuan Sun & Paolo Bocchini & Brian D. Davison, 2020. "Applications of artificial intelligence for disaster management," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(3), pages 2631-2689, September.
    3. Társilo Girona & Kyriaki Drymoni, 2024. "Abnormal low-magnitude seismicity preceding large-magnitude earthquakes," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    4. Suzlyana Marhain & Ali Najah Ahmed & Muhammad Ary Murti & Pavitra Kumar & Ahmed El-Shafie, 2021. "Investigating the application of artificial intelligence for earthquake prediction in Terengganu," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(1), pages 977-999, August.
    5. Jaime de-Miguel-Rodríguez & Antonio Morales-Esteban & María-Victoria Requena-García-Cruz & Beatriz Zapico-Blanco & María-Luisa Segovia-Verjel & Emilio Romero-Sánchez & João Manuel Carvalho-Estêvão, 2022. "Fast Seismic Assessment of Built Urban Areas with the Accuracy of Mechanical Methods Using a Feedforward Neural Network," Sustainability, MDPI, vol. 14(9), pages 1-27, April.
    6. Javad N. Rashidi & Mehdi Ghassemieh, 2023. "Predicting the magnitude of injection-induced earthquakes using machine learning techniques," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(1), pages 545-570, August.
    7. Dongdong Chen & Zhiqiang Wang & Zaisheng Jiang & Shengrong Xie & Zijian Li & Qiucheng Ye & Jingkun Zhu, 2023. "Research on J 2 Evolution Law and Control under the Condition of Internal Pressure Relief in Surrounding Rock of Deep Roadway," Sustainability, MDPI, vol. 15(13), pages 1-22, June.
    8. Rabia Tehseen & Muhammad Shoaib Farooq & Adnan Abid, 2020. "Earthquake Prediction Using Expert Systems: A Systematic Mapping Study," Sustainability, MDPI, vol. 12(6), pages 1-32, March.
    9. Chen, Weiyi & Zhang, Limao, 2022. "An automated machine learning approach for earthquake casualty rate and economic loss prediction," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    10. Matteo Picozzi & Antonio Giovanni Iaccarino, 2021. "Forecasting the Preparatory Phase of Induced Earthquakes by Recurrent Neural Network," Forecasting, MDPI, vol. 3(1), pages 1-20, January.
    11. Zhou, Yuhao & Wang, Yanwei, 2022. "An integrated framework based on deep learning algorithm for optimizing thermochemical production in heavy oil reservoirs," Energy, Elsevier, vol. 253(C).
    12. KASHIWAGI Yuzuka & TODO Yasuyuki, 2021. "How Do Disasters Change Inter-Group Perceptions? Evidence from the 2018 Sulawesi Earthquake," Discussion papers 21082, Research Institute of Economy, Trade and Industry (RIETI).
    13. Yong Mu & Ying Li & Ran Yan & Pingping Luo & Zhe Liu & Yingying Sun & Shuangtao Wang & Wei Zhu & Xianbao Zha, 2023. "Analysis of the Ongoing Effects of Disasters in Urbanization Process and Climate Change: China’s Floods and Droughts," Sustainability, MDPI, vol. 16(1), pages 1-16, December.
    14. Amna Hafeez & Muhsan Ehsan & Ayesha Abbas & Munawar Shah & Rasim Shahzad, 2022. "Machine learning-based thermal anomalies detection from MODIS LST associated with the Mw 7.7 Awaran, Pakistan earthquake," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(2), pages 2097-2115, March.
    15. Jimmy Cheung & Smruthi Rangarajan & Amelia Maddocks & Xizhe Chen & Rohitash Chandra, 2024. "Quantile deep learning models for multi-step ahead time series prediction," Papers 2411.15674, arXiv.org.
    16. Vera Wendler-Bosco & Charles Nicholson, 2022. "Modeling the economic impact of incoming tropical cyclones using machine learning," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(1), pages 487-518, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:nathaz:v:120:y:2024:i:14:d:10.1007_s11069-024-06724-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.