Modelling spatial dynamics of landslides: Integration of GIS-based cellullar automata and multicriteria evaluation methods

Landslides occur when the slopes become unstable due to natural causes as well as by disturbances from human activities. Landslides have adverse socio-economic consequences and negatively impact natural resources, public safety, and property. Urban planners require tools to make informed decisions that will mitigate landslide hazards. The objective of this research is the development of a GIS-based methodology coupled with cellular automata (CA) theory and multicriteria evaluation (MCE) methods for modelling landslide flow. Landslides are viewed as geographic phenomena that exhibit complex system behaviour that can be effectively modelled in both space and time. The integration of GIS, logistic regression, MCE and CA was used to model the dynamics of landslide flow. The methodology was tested on historical landslide data for Metro Vancouver, Canada. The results showed a strong degree of correlation. This research makes a unique contribution to knowledge by introducing non-linear dynamics into GIS-CA landslide models, further improving our ability to understand processes and predictions for applications in urban planning and disaster management.