Lassonde School of Engineering Professor 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.
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.
Khan’s proposed model uses a simple linear regression approach with lagged climatic variables (such as precipitation and temperature) as inputs to predict the flow rate in urban rivers.
The simplicity of the model is balanced with the use of a fuzzy set theory approach to quantify the uncertainty and variability of the hydrological system.
Data from the 2013 flood in Southern Alberta is used to demonstrate the applicability of the novel approach.
The results show the proposed method can better predict the magnitude and timing of the floods several days in advance, as compared to conventional methods. This means using the proposed model would provide emergency workers with enough time to issue flood warnings and implement flood defence measures.