期刊
COMPUTERS & GEOSCIENCES
卷 37, 期 4, 页码 554-566出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cageo.2010.10.006
关键词
Landslide susceptibility mapping; Subjective geomorphic mapping; Artificial Neural Networks (ANN); Learning Vector Quantization (LVQ); Geographic Information Systems (GIS)
资金
- Science Council of British Columbia
- University of British Columbia
- Natural Sciences and Engineering Research Council of Canada
- Western Forest Products, Ltd.
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. (C) 2010 Elsevier Ltd. All rights reserved.
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