Overland flooding has a profound socio-economic, food security, public safety, and environmental impact on agricultural land, rural properties, and public infrastructures.

The current flood risk analysis work is predominantly focused on small-scale scenarios within densely populated urban zones, resulting in a notable information gap on flood susceptibility across a vast agricultural land in the Canadian prairies.

To bridge this critical information gap, this study is designed to use small-scale flood simulation analysis results and various publicly available data, such as hydrometric, digital elevation models (DEM), physical terrain and vegetation groundcover characteristics and meteorological data, to train artificial intelligence (AI) systems to determine if AI systems can accurately predict overland flood risk in South Saskatchewan in a near real-time.

This research will entail the utilization of geospatial data processing techniques, facilitated by Geographic Information Systems (GIS), coupled with various flood risk models and applications.  

The study will employ the latest artificial intelligence methodologies for data processing and predictive risk modeling approach.  To future proof this initiative, various climate change scenarios will be incorporated into the development of the AI models. The following two abstracts are subsections under the current research project. 

Generously sponsored by

Good Lands Environmental Inc. and North Shore Environmental Consultants.