Soft prediction model for spatial data analysis

J. Velmurugan and M. Venkatesan

International Journal of Global Environmental Issues, 2018, vol. 17, issue 2/3, 130-143

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.

Keywords: geographical information system; GIS; rough set; Bayesian; landslide; disaster. (search for similar items in EconPapers)
Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=91465 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijgenv:v:17:y:2018:i:2/3:p:130-143

Access Statistics for this article

More articles in International Journal of Global Environmental Issues from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().