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Application of artificial neural networks for terrain stability mapping

University of British Columbia

This thesis investigates terrain stability mapping using Artificial Neural Networks (ANN). Preliminary analyses were conducted to evaluate the numerous types of ANN and select the one considered most appropriate for this problem. Kohonen Self-Organizing Maps were selected to be used in this study. Self-Organizing Maps include in principle two architectures (paradigms): Learning Vector Quantization (LVQ) for supervised learning, and the Self-Organizing Map itself (SOM) for unsupervised learning. Both architectures were used in this thesis. Analyses were performed on two study areas in southwestern British Columbia. Data were stored in a Geographic Information System (GIS), and terrain analyzed was represented in the raster format. Analyses were conducted based on topographic and geomorphic terrain attributes. Both supervised and unsupervised analyses produced good results. The attributes most relevant to terrain stability mapping were identified as slope, elevation, aspect, and existing geomorphic processes. In supervised mode, unstable terrain was delineated with accuracies of 94% and 95% for the two study sites, and unstable and potentially unstable terrain were delineated with accuracies of 91% and 82%, respectively. A comparison with a physically-based model showed that LVQ-based analyses yielded superior results. Unsupervised analyses also produced accurate terrain mappings, and SOM proved to have good explanatory power with respect to the influence of the attributes used.

Thesis/Dissertation

application/pdf

2009-12-01

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http://hdl.handle.net/2429/16014

Forestry

University of British Columbia

UBCV

DSpace