@article{MAKHILLIJSC201813221443,
    title = {Soft Bayesian Model for Landslide Risk Analysis},
    journal = {International Journal of Soft Computing},
    volume = {13},
    number = {2},
    pages = {31-37},
    year = {2018},
    issn = {1816-9503},
    doi = {ijscomp.2018.31.37},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2018.31.37},
    author = {J. and},
    keywords = {GIS,rough set,Bayesian,landslide,disaster,real},
    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 minimized 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 study, classification data science
technique is integrated with rough set model and Soft Bayesian Prediction Model (SBPM) is proposed to
analyze 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.}
    }