IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v55y2010i2p389-404.html
   My bibliography  Save this article

Cyclic behavior of seismogenic sources in India and use of ANN for its prediction

Author

Listed:
  • M. Sharma
  • A. Tyagi

Abstract

Endeavors to realistically model physical processes responsible for earthquake occurrence and sustained large uncertainties in the results have lead to the application of techniques like artificial neural network for estimation of rate/probability of earthquake occurrence in future. The earthquake occurrence in India has been re-visited and artificial neural networks have been applied to learn the cyclic behavior of seismicity in the independent seismogenic sources to predict their future trends. As a prerequisite, the whole country has been divided into 24 seismogenic sources for which the seismicity cycles were studied. Their cyclic behavior has been captured in form of four stages of earthquake occurrence and the future trends have been predicted using ANN. To validate the trained ANN model, testing has been carried out in two ways: first, by giving the samples that are not used in training (NT) and second, by giving the total samples (T). As a method of testing, standard errors and correlation coefficients between the network output patterns and observed patterns of the testing sample given were considered. The outcome of the ANN is used to interpret the future seismicity of each of the 24 seismogenic zones in terms of various stages of the future seismicity cycles. Copyright Springer Science+Business Media B.V. 2010

Suggested Citation

  • M. Sharma & A. Tyagi, 2010. "Cyclic behavior of seismogenic sources in India and use of ANN for its 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. 55(2), pages 389-404, November.
    • Handle: RePEc:spr:nathaz:v:55:y:2010:i:2:p:389-404
    DOI: 10.1007/s11069-010-9536-3
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11069-010-9536-3
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11069-010-9536-3?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shahram Kaboodvandpour & Jamil Amanollahi & Samira Qhavami & Bakhtiyar Mohammadi, 2015. "Assessing the accuracy of multiple regressions, ANFIS, and ANN models in predicting dust storm occurrences in Sanandaj, Iran," 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. 78(2), pages 879-893, September.
    2. Yen-Ming Chiang & Wei-Guo Cheng & Fi-John Chang, 2012. "A hybrid artificial neural network-based agri-economic model for predicting typhoon-induced losses," 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. 63(2), pages 769-787, September.
    3. Jamil Amanollahi & Shahram Kaboodvandpour & Hiva Majidi, 2017. "Evaluating the accuracy of ANN and LR models to estimate the water quality in Zarivar International Wetland, Iran," 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(3), pages 1511-1527, February.
    4. Masoomeh Mirrashid, 2014. "Earthquake magnitude prediction by adaptive neuro-fuzzy inference system (ANFIS) based on fuzzy C-means algorithm," 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. 74(3), pages 1577-1593, December.

    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:55:y:2010:i:2:p:389-404. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.