IDEAS home Printed from https://ideas.repec.org/a/ids/ijgenv/v17y2018i2-3p130-143.html
   My bibliography  Save this article

Soft prediction model for spatial data analysis

Author

Listed:
  • J. Velmurugan
  • M. Venkatesan

Abstract

A natural disaster causes huge loss in terms of people life and infrastructures. Landslide is one of the prime disasters in the hill regions such as Uttarakhand, Sikkim and Ooty in India. The extent of damages of landslide could be reduced or minimised by proposing novel landslide risk analysis model. Landslide is generated by various factors such as rainfall, soil, slope, land use and land covers, geology, etc. Data science and soft computing plays major role in landslide risk analysis. In this paper, classification data science technique is integrated with rough set model and soft Bayesian prediction model (SBPM) is proposed to analyse the possibilities of various landslide risk level at Coonor Taluk of Niligiri district. The proposed model is validated with real time data and performance is compared with other classification models.

Suggested Citation

  • J. Velmurugan & M. Venkatesan, 2018. "Soft prediction model for spatial data analysis," International Journal of Global Environmental Issues, Inderscience Enterprises Ltd, vol. 17(2/3), pages 130-143.
    • Handle: RePEc:ids:ijgenv:v:17:y:2018:i:2/3:p:130-143
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=91465
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

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

    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:ids:ijgenv:v:17:y:2018:i:2/3:p:130-143. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=14 .

    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.