An Improved Bayesian Classification Data Mining Method for Early Warning Landslide Susceptibility Model Using GIS

摘要

Landslide causes huge damage to human life, infrastructure and the agricultural lands. Landslide susceptibility is required for disaster management and planning development activities in mountain regions. The extent of damages could be reduced or minimized if a long-term early warning system predicting the landslide prone areas would have been in place. We required an early warning system to predict the occurrence of Landslide in advance to prevent these damages. Landslide is triggered by many factors such as rainfall, landuse, soil type, slope and etc. The proposed idea is to build an Early Warning Landslide Susceptibility Model (EWLSM) to predict the possibilities of landslides in Niligri's district of the Tamil Nadu. The early warning of the landslide susceptibility model is built through data mining technique classification approach with the help of important factors, which triggers a landslide. In this study, we also compared and shown that the performance of Bayesian classifier is more accurate than SVM Classifier in landslide analysis.