2019 CSCE Annual Conference - Laval (Greater Montreal)

2019 CSCE Annual Conference - Laval (Greater Montreal) Conference

Stochastic Method for Predicting Long-term Urban Water Consumption

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Mrs. Niousha RasiFaghihi, Department of Building, Civil and Environmental Engineering, Concordia University (Presenter)
Dr. S. Samuel Li, Concordia University
Dr. Fariborz Haghighat, Concordia University

Canada is known to have abundant fresh-water resources in different forms of water bodies. However, most of them drain to the country’s northern rural areas that are much less populated than its southern urban areas. Thus, like many urban centers around the world, large Canadian cities require a sustainable and effective approach to urban water consumption (UWC). The purpose of this paper is to improve our understanding of how UWC depends on a set of influence factors. This will help effectively manage current consumption, including the control of both peak and daily-averaged water demands, and help plan future consumption for growing cities under a changing climate. The challenge is that the influence factors themselves are a random variable, in the context of predicting future consumption. The traditional methods for predicting UWC are typically based on historical data and assume that the data are linear and stationary over time. A significant gap exists in that the future changes and associated uncertainties of the influence factors have not been dealt with adequately. Consequently, it would be difficult to propose reliable planning and management strategies for water consumption sustainability. This crucial issue needs to be addressed. This paper extends the previous research of UWC by treating future consumption as a stochastic process. We propose a stochastic method for predicting future consumption that allows for water sources availability, population growth, socio-economic factors, and climate change. The predictions systematically explore the uncertainties associated with 1) individual influence factors (a lack or incompleteness of data; data outliers; mathematical models; parameters subject to future changes); 2) a combination of some of these factors or of all of them. To demonstrate its relevance, the proposed method is applied to analyze the UWC of the City of Brossard (Great Montreal) in the Canadian Province of Quebec. Long-term daily records of water consumption are divided into 1) base use, which reflects winter consumption, and 2) seasonal use, which depends on seasonal and climatic factors. Various climate and socio-economic factors are investigated as the major influence factors of UWC. The records cover a multitude of years and are of high quality. The analysis of these records uses probabilistic data mining techniques and produces quantitative results of the correlations among the factors as well as their influences on UWC. Using the proposed method, this paper discusses decision-making scenarios of sustainable UWC and provides policymakers with good knowledge in water demand management.