Globally, floods are one of the most devastating natural disasters. Flooding impacts many regions in the world and the results can be devastating. Major floods have caused casualties and are responsible for billions of dollars in losses annually.
Many cities are becoming more vulnerable to flooding due to urbanization, aging infrastructure and the increasing frequency and intensity of extreme weather events.
Lassonde School of Engineering researcher Usman Khan has found a new forecasting method to predict the risk of floods in urban areas, which could potentially reduce damage and fatalities.
The new method uses high-resolution data from Environment Canada to create data-driven models. Khan’s findings were published in the study “Short-Term Peak Flow Rate Prediction and Flood Risk Assessment Using Fuzzy Linear Regression” in the Journal of Environmental Informatics.
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