Estimation of household water consumption pattern in a metropolitan area taking the impact of the COVID-19 pandemic.

Abstract

A new approach for estimating the household water consumption pattern was developed by taking the impact of the COVID-19 pandemic using geographical data. Water consumption data for two years before and a year after the outbreak of the pandemic were analyzed to recognize the consumption pattern on annual and bi-monthly time scales as well as in different spatial classes. Following the recognition of the pattern, the spatiotemporal distribution of household water consumption was estimated based on the discovered connections between consumption and geographical variables. Once a regression relationship between consumption and population density was observed, an idea was developed to investigate the linear equations and their coefficient of parameters in water consumption groups from very low to very high classes using the training data. The coefficients were then adjusted to account for the pandemic's impact on the consumption pattern. Results showed that the highest increases in consumption were 11% for May-July due to the impact of the pandemic while the impact was from decreasing type during lockdowns. A pandemic-induced decline in the mean of consumption was linked to temporary migration by high-income families, whereas the water consumption of others faced an increase. The impact has also increased the slope of the linear relationship between the annual water consumption and population density increased by 3.5%. The proposed model estimated the annual water consumption with the accuracy of %3.77, %1.82, and %1.85 for two years before, one year before and one year after the pandemic, respectively.