U2.04 Quesnel 2019 ReNUWIt Annual Meeting Poster

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Abstract/Contents

Abstract: Planning for future water demands and identifying opportunities for increased conservation and efficiency requires understanding how and where water is used in cities. Homogeneous neighborhoods within a heterogeneous service area can provide appropriate planning and demand management scales. Classifying these neighborhoods, however, remains a challenge. Additionally, while the drivers of urban water use have been well-studied, nonlinear water use behavior related to “hinges” in covariates has been less explored. For example, is there a threshold at which temperatures affects certain neighborhoods but not others? In this study, we integrated a suite of novel datasets to develop a Census block-group level database of monthly water use from 2008—2017, including 14 variables related to social and built environment dimensions of communities. Through a sequence of unsupervised learning methods, we identified five neighborhoods for water demand modeling. Our MARS model performs well in estimating water use for block-groups with different neighborhood features while identifying nonlinear and piecewise water use behavior.

Type of resource	other
Date created	May 2019

Author	Quesnel, Kim
Author	Ajami, Newsha

Subject	Re-inventing the Nation’s Urban Water Infrastructure
Subject	ReNUWIt
Subject	U2.04
Subject	Urban Systems Integration and Institutions
Subject	Visioning
Subject	assessment
Subject	and implementation tools for regional and municipal water planning
Subject	California

Location	https://purl.stanford.edu/wk522cd5252

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License: This work is licensed under an Open Data Commons Attribution License v1.0.

Preferred Citation: Quesnel, K. J., & Ajami, N. K. (2019). U2.04 Quesnel 2019 ReNUWIt Annual Meeting Poster. Stanford Digital Repository. Available at: https://purl.stanford.edu/wk522cd5252

Re-inventing the Nation's Urban Water Infrastructure (ReNUWIt)

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