An analysis of landslide susceptibility zonation using a subjective geomorphic mapping and existing landslides

  • Authors:

  • Mihai Pavel;John D. Nelson;R. Jonathan Fannin

  • Affiliations:

  • Department of Forest Resources Management, Faculty of Forestry, University of British Columbia, 2424 Main Mall, Vancouver, BC, Canada V6T 1Z4;Department of Forest Resources Management, Faculty of Forestry, University of British Columbia, 2424 Main Mall, Vancouver, BC, Canada V6T 1Z4;Department of Civil Engineering, University of British Columbia, 6250 Applied Science Lane, Vancouver, BC, Canada V6T 1Z4

  • Venue:
  • Computers & Geosciences
  • Year:
  • 2011

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Abstract

This study explores the possibility of creating landslide susceptibility mappings by using two types of data: (i) an existing subjective geomorphic mapping; and (ii) landslides already identified in the area analyzed. The analysis is conducted using a type of Artificial Neural Network (ANN) named Learning Vector Quantization. For the subjective geomorphic mapping various definitions of stability were considered/analyzed, some using a 2-class system and some using a 5-class system. The study concludes that mappings using an existing subjective geomorphic classification and based on two stability classes can be successfully replicated with the ANN-based approach. However, mappings based on existing landslides and on the 5-class system do not yield results sufficiently accurate for practical applications. Creation of landslide susceptibility mappings involved utilization of data of numerous types (numerical and class-type variables). This study also investigated various methods of data coding and identified the most appropriate method for this type of analysis.