The use of AI is changing our world, allowing us to understand complex systems like never before.  How can we use AI to screen for infrastructure vulnerabilities at risk of landslide-induced damage?  Landslide and debris flow risk assessments often rely on computer methods to simulate flooding, rapid landslide movement and slope failure.  These processes are governed by a complex set of parameters, including rainfall, soil conditions, terrain and landform transformation due to changes to the landscape from human activities.

CES-Lab has obtained Tasmania State Emergency Service competitive funding (total project value of $241,000) to investigate the current gaps in Tasmania’s landslide vulnerability assessments, centring on Hobart and West Tamar councils using AI-based landslide susceptibility tools.

This project will include the development of a platform allowing current vulnerability models to interface with infrastructure-based datasets, providing a range of benefits to disaster-prone communities.  The funding continues the ongoing work conducted by CES-Lab under Drs Ali Tolooiyan, Ashley Dyson, and Gholamreza Kefayati on Tasmanian landslides and debris flow hazards.  This project seeks to assess current gaps in Tasmania’s landslide susceptibility maps, which can be significantly improved by the incorporation of additional available data sets, leveraging AI methods to determine the vulnerability of public and private infrastructure.