The development of infrastructure and dense urbanization along water courses and the increasing severity of storm events has brought to focus the need for...
There is an increase in the collection and availability of LiDAR data across Canada. While digital terrain and surface models are the most common derivative...
Machine learning (ML) algorithms have emerged as competent tools for identifying areas that are susceptible to flooding. The primary variables considered in...
Numerous computer models have been developed for seismic loss analyses at urban and regional scales. They seem, however, ill-suited to custom application to...
Conventional knowledge of the flood hazard alone (extent and frequency) is not sufficient for informed decision-making. The public safety community needs tools...
Many different models exist for natural hazard simulations, but due to their technical complexity and data requirements, their use is generally restricted to...
Conventional knowledge of the flood hazard alone (extent and frequency) is not sufficient for informed decision-making. The public safety community needs tools...
Hydrological models are important in a range of applications, including water resources planning and development and management of flood prediction and design...
The emergence of Machine learning (ML) algorithms has shown competency in a variety of fields and are growing in popularity in their application to geospatial...
Canada's RADARSAT missions improve the potential to study past flood events; however, existing tools to derive flood depths from this remote-sensing data do...
Périodique (revue)
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