Journal - Use of different bivariate statistical landslide susceptibility methods: A case study of Kulekhani watershed, Nepal

Use of different bivariate statistical landslide susceptibility methods: A case study of Kulekhani watershed, Nepal

Ananta Man Singh Pradhan

Department of Electricity Development, Ministry of Energy, Government of Nepal

Anurag Dawadi

Odum School of Ecology, University of Georgia, Athens (UGA)

Ananta Man Singh Pradhan and Yun-Tae Kim

Geosystems Engineering Lab, Department of Ocean Engineering, Pukyong National University

Jour. Nepal Geol. Soc., 2012, Vol. 44, 1-12


Abstract

The study is intended to delineate landslide susceptibility prone areas according to the degree of potential failures with application of geographic information system (GIS) based on statistical evaluation of various causative factors related to slope instability and their relationship with existing landslide distribution in the watershed area. The methodology includes calculation of the bivariate statistical model for landslide susceptibility analysis by using GIS. Landslides maps are prepared around the world, but little effort is made to assess their reliability, outline their main characteristics, and pinpoint their limitations. In order to redress this imbalance, the results of a long-term research in Kulekhani watershed in central Nepal are used to compare reconnaissance and detailed landslide inventory maps, statistical and geomorphological based density maps, and landslide susceptibility maps obtained by bivariate statistical modeling. This paper discusses the differences in landslide susceptibility zonation shown in five maps generated with the same parameters but created with different methodologies. Susceptibility maps were obtained from frequency ratio method, statistical index method, landslide susceptibility analysis, weight of evidence modeling and certainty factor method. The statistical index method is considered as the best method for landslide susceptibility mapping in the study area, and the LS map resulting from the statistical index method is chosen as the final map of this study.

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