Using CUDA to accelerate uncertainty propagation modelling for landslide susceptibility assessment. (May 2019) - Sandric, Ionut; Ionita, Cristian; Chitu, Zenaida; Dardala, Marian; Irimia, Radu; Furtuna, Felix Titus
Abstract: The study is focused on modelling uncertainty propagation from GIS data sources and on assessing their influence on landslide susceptibility modelling. A complete set of tools was developed and written in C++ programming language, Python, and based on NVIDIA CUDA technology for terrain analysis. These tools are using Monte Carlo simulations to generate noise in elevation values and spatial delineation of landslides bodies. The uncertainty propagation is assessed using pixel based cumulative probabilities statistics at the pixel level. Thus, for each pixel, from the landslide susceptibility map, an estimation of landslide susceptibility uncertainty was obtained and spatially visualised. The results show that weight of evidence is a robust method and is not significantly influenced by small-scale variations in the primary topographic attributes. The toolbox and the source code are available under the MIT license. Highlights: Conceptual models for DEM and Landslide uncertainty estimations and propagation. Uncertainty propagation in landslide susceptibility mapping. Tools written in C++ and Python to perform and reproduce the analysis. Implementation of CUDA to speed up the terrain analysis. Integration with existent GIS software – and ArcGIS toolbox.