439 - Deep Learning for Assessing the Impact of Climate Change on Future Water Demand at the University of British Columbia

We have created a methodology of quantifying the impact of climate change on The University of British Columbia (UBC) by establishing a link between water demand and weather based on climate change scenarios via coupled general circulation models (GCMs) using random forest machine learning algorithms. The random forest machine learning algorithms are based on weather variables to establish the complex relationship between water demand and weather. The smart meters provided at UBC residences have been used to analyze the data as well as smart meters from UBC labs and agricultural lands. We ultimately answer the question, “Does water demand increase with an increase in temperature?” Since daily and weekly fluctuations in weather variables are highly associated with changes in water demand, climate change is also expected to influence water demand. Changes in water demand could affect the existing water systems in terms of capacity and operation. Specifically, increases in water demand can cause imbalance in water resources and problems in storage capacity, worsening the situation of water shortages in British Columbia, Canada. Therefore, knowing the extent of water demand changes due to climate change is needed for long-term climate adaption planning. The methodology used in the study will be upscaled to the city of Vancouver to compare results.