Fuzzy–frequency ratio model for avalanche susceptibility mapping. Issue 12 (1st December 2016)

• Record Type: Journal Article
• Authors: Kumar, Satish; , Snehmani; Srivastava, Pankaj Kumar; Gore, Akshay; Singh, Mritunjay Kumar
• Is Part Of: International journal of digital earth. Volume 9:Issue 12(2016)
• Page Start: 1168
• Page End: 1184
• Publication Date: 2016-12-01
• Subjects: Snow avalanche -- avalanche susceptibility mapping -- GIS -- fuzzy–frequency ratio (fuzzy–FR) model -- Lahaul & Spiti; Geographic information systems -- Periodicals; Sustainable development -- Information technology -- Periodicals; Social planning -- Information technology -- Periodicals; 910.285
• DOI: 10.1080/17538947.2016.1197328
• Languages: English
• ISSNs: 1753-8947

Abstract

ABSTRACT: Avalanche activities in the Indian Himalaya cause the majority of fatalities and responsible for heavy damage to the property. Avalanche susceptibility maps assist decision-makers and planners to execute suitable measures to reduce the avalanche risk. In the present study, a probabilistic data-driven geospatial fuzzy–frequency ratio (fuzzy–FR) model is proposed and developed for avalanche susceptibility mapping, especially for the large undocumented region. The fuzzy–FR model for avalanche susceptibility mapping is initially developed and applied for Lahaul-Spiti region. The fuzzy–FR model utilized the six avalanche occurrence factors (i.e. slope, aspect, curvature, elevation, terrain roughness and vegetation cover) and one referent avalanche inventory map to generate the avalanche susceptibility map. Amongst 292 documented avalanche locations from the avalanche inventory map, 233 (80%) were used for training the model and remaining 59 (20%) were used for validation of the map. The avalanche susceptibility map is validated by calculating the area under the receiver operating characteristic curve (ROC-AUC) technique. For validation of the results using ROC-AUC technique, the success rate and prediction rate were calculated. The values of success rate and prediction rate were 94.07% and 91.76%, respectively. The validation of results using ROC-AUC indicated the fuzzy–FR model is appropriate for avalanche susceptibility mapping.