Bayesian networks and intelligence technology applied to climate change: An application of fuzzy logic based simulation in avalanche simulation risk assessment using GIS in a Western Himalayan region

Abstract

Avalanches are snowfalls that cascade down a mountainside, although rockfalls and debris flows can also occur. The study identifies the snow meteorological data for the avalanche risk forecast through various associated factors and models a tool for forecasting avalanches according to the reference database. The terrain factors such as aspect, slope, and altitude have been studied using the Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) at 90 m resolution. Snow meteorological observed data 2540 records were obtained for Snow and Avalanche Study Establishment (SASE) Nov 2004 to April 2009 for five years data. In the present study, nine parameters were used for Avalanche forecast in the Western Himalaya. The Bayesian classification algorithm may improve the feedback system of prediction than the existing one. Fuzzy logic decides to improve the system to predict the level of danger by studying the influence of parameters. Hazard mitigation strategies applying the Bayesian classification algorithm and Fuzzy logic were combined with Integrated GIS-based methods to determine the risk zones better to identify snow avalanches in three-dimensional terrain.