Replication of a terrain stability mapping using an Artificial Neural Network

Subjective geomorphic mapping is a method commonly used for landslide hazard zonation. This method relies heavily on the skills and experience of the mapper, and therefore, its major drawbacks are the high costs and lack of consistency between products generated by different terrain mappers. In this study a method for cost-effective and consistent replication of subjective geomorphic mappings is demonstrated, by using a type of Artificial Neural Network named Learning Vector Quantization. This paper presents a study conducted in the Canadian province of British Columbia employing a high-quality data set. By utilizing Learning Vector Quantization, stable and unstable terrains were delineated with a similarity of approximately 91%, compared to the mapping produced by terrain specialists. Also, in this process, slope, elevation, aspect, and existing geomorphic processes were identified as the terrain attributes that contributed most to the quality of the mapping.