Abstract Submission: Water demand management is paramount for water utilities to meet the potable water needs of the population. Utilities can forecast residential water demand based on the traits of the households they serve utilizing monthly water consumption data and property information. This research applies a data-driven approach to analyze monthly and yearly water consumption patterns at a spatially aggregated level and their corresponding aggregated property characteristics to predict future demand. A regression tree model is applied using the average lot area, building area, building age, and the number of bathrooms as input variables and the monthly water demand as the response. The tree model explains an average of 62% of monthly water demand across subdivisions, with the lot and building areas as the most important predictors followed by the building age. Furthermore, a stronger correlation between monthly water demand and household characteristics was found in the cooler months such as January and April, and a weaker correlation in warmer months such as July and September due to the higher consumption variations. When aggregating demand at the annual level, the mean number of bathrooms and the property area explain more than 50% and 75% of water demand, respectively. Practitioners and researchers can use the proposed model in water infrastructure long-term planning to account for the socioeconomic effects on water demand at multiple temporal resolutions.
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