IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v117y2023i1d10.1007_s11069-023-05856-8.html
   My bibliography  Save this article

Earthquake magnitude prediction using a VMD-BP neural network model

Author

Listed:
  • Jiaqi Zhang

    (Beijing Technology and Business University (BTBU))

  • Xijun He

    (Beijing Technology and Business University (BTBU))

Abstract

Earthquakes instantaneously occur and can cause huge disasters to cities, villages, and human beings. Therefore, it is of great significance to develop relevant theories and methods of earthquake prediction. This study builds a new model for seismic magnitude prediction, which uses a classic back propagation (BP) neural network combined with the variational mode decomposition (VMD) technique as a preprocessing for seismic dataset. The proposed model is referred to as VMD-BP. For each entry in the chronological earthquake catalog, three features are taken into consideration: magnitude, latitude, and longitude. The features of the past three adjacent seismic events are used as the input of the VMD-BP model, and the magnitude of the next seismic event is considered as the output. The VMD-BP model is then applied for seismic magnitude prediction in the Tibet and Yunnan regions. The results show that the VMD-BP model has high prediction accuracy, it performs better than the single BP neural network, and it can effectively predict the earthquake magnitude.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:nathaz:v:117:y:2023:i:1:d:10.1007_s11069-023-05856-8
    DOI: 10.1007/s11069-023-05856-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-023-05856-8
    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/s11069-023-05856-8?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. Arnaud Mignan & Marco Broccardo, 2019. "One neuron versus deep learning in aftershock prediction," Nature, Nature, vol. 574(7776), pages 1-3, October.
    2. Zhuang J. & Ogata Y. & Vere-Jones D., 2002. "Stochastic Declustering of Space-Time Earthquake Occurrences," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 369-380, June.
    3. Qing Ling & Qin Zhang & Jing Zhang & Lingjie Kong & Weiqi Zhang & Li Zhu, 2021. "Prediction of landslide displacement using multi-kernel extreme learning machine and maximum information coefficient based on variational mode decomposition: a case study in Shaanxi, China," 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 925-946, August.
    4. Robert Shcherbakov & Jiancang Zhuang & Gert Zöller & Yosihiko Ogata, 2019. "Forecasting the magnitude of the largest expected earthquake," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
    5. 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.
    6. 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. van den Hengel, G. & Franses, Ph.H.B.F., 2018. "Forecasting social conflicts in Africa using an Epidemic Type Aftershock Sequence model," Econometric Institute Research Papers EI2018-31, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. Chandan Singh & Armin Askari & Rich Caruana & Jianfeng Gao, 2023. "Augmenting interpretable models with large language models during training," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    3. Chenlong Li & Zhanjie Song & Wenjun Wang, 2020. "Space–time inhomogeneous background intensity estimators for semi-parametric space–time self-exciting point process models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(4), pages 945-967, August.
    4. Ying Song & Harvey Miller, 2012. "Exploring traffic flow databases using space-time plots and data cubes," Transportation, Springer, vol. 39(2), pages 215-234, March.
    5. Chhotu Kumar Keshri & William Kumar Mohanty & Pratul Ranjan, 2020. "Probabilistic seismic hazard assessment for some parts of the Indo-Gangetic plains, India," 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(1), pages 815-843, August.
    6. Giada Adelfio & Marcello Chiodi, 2021. "Including covariates in a space-time point process with application to seismicity," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(3), pages 947-971, September.
    7. Lippiello, E. & Baccari, S. & Bountzis, P., 2023. "Determining the number of clusters, before finding clusters, from the susceptibility of the similarity matrix," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 616(C).
    8. Gresnigt, Francine & Kole, Erik & Franses, Philip Hans, 2015. "Interpreting financial market crashes as earthquakes: A new Early Warning System for medium term crashes," Journal of Banking & Finance, Elsevier, vol. 56(C), pages 123-139.
    9. 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.
    10. 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.
    11. 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).
    12. Rachele Foschi & Francesca Lilla & Cecilia Mancini, 2020. "Warnings about future jumps: properties of the exponential Hawkes model," Working Papers 13/2020, University of Verona, Department of Economics.
    13. D'Angelo, Nicoletta & Adelfio, Giada & Mateu, Jorge, 2023. "Locally weighted minimum contrast estimation for spatio-temporal log-Gaussian Cox processes," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
    14. Chenhui Wang & Wei Guo, 2023. "Prediction of Landslide Displacement Based on the Variational Mode Decomposition and GWO-SVR Model," Sustainability, MDPI, vol. 15(6), pages 1-18, March.
    15. Gilian van den Hengel & Philip Hans Franses, 2020. "Forecasting Social Conflicts in Africa Using an Epidemic Type Aftershock Sequence Model," Forecasting, MDPI, vol. 2(3), pages 1-25, August.
    16. 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.
    17. Nader Davoudi & Hamid Reza Tavakoli & Mehdi Zare & Abdollah Jalilian, 2020. "Aftershock probabilistic seismic hazard analysis for Bushehr province in Iran using ETAS 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. 100(3), pages 1159-1170, February.
    18. V. Filimonov & D. Sornette, 2015. "Apparent criticality and calibration issues in the Hawkes self-excited point process model: application to high-frequency financial data," Quantitative Finance, Taylor & Francis Journals, vol. 15(8), pages 1293-1314, August.
    19. 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.
    20. Tomlinson, Matthew F. & Greenwood, David & Mucha-Kruczyński, Marcin, 2024. "2T-POT Hawkes model for left- and right-tail conditional quantile forecasts of financial log returns: Out-of-sample comparison of conditional EVT models," International Journal of Forecasting, Elsevier, vol. 40(1), pages 324-347.

    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:117:y:2023:i:1:d:10.1007_s11069-023-05856-8. 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.