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\begin{document}

\title{Leveraging statistical learning theory to characterize the U.S. water
consumption}



\author[1]{Ellen Wongso}%
\author[1]{Roshanak Nateghi}%
\author[2]{Benjamin Zaitchik}%
\author[3]{Steven Quiring}%
\author[4]{Rohini Kumar}%
\affil[1]{School of Industrial Engineering, Purdue University, West Lafayette, IN, USA}%
\affil[2]{Department of Earth and Planatery Sciences, Johns Hopkins University, Baltimore, MD, USA}%
\affil[3]{Department of Geography, Ohio State University Columbus OH, USA}%
\affil[4]{UFZ-Helmholtz Centre for Environmental Research, Leipzig, Germany}%


\vspace{-1em}



  
  \date{May 20, 2024}


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\begin{abstract}
Access to accurate estimates of water withdrawal is requisite for urban
planners as well as operators of critical infrastructure systems to make
optimal operational decisions and investment plans to ensure reliable
and affordable provisioning of water. Furthermore, identifying the key
predictors of water withdrawal is important to regulators for promoting
sustainable development policies to reduce water use. In this paper, we
developed a rigorously evaluated predictive model, using statistical
learning theory, to estimate state-level, per-capita water withdrawal as
a function of various geographic, climatic and socio-economic variables.
We then harnessed the data-driven predictive model to identify the key
factors associated with high water-usage intensity among different
sectors in the U.S. We analyzed the predictive accuracy of a range of
parametric models (e.g., generalized linear models) and non-parametric,
flexible learning algorithms (e.g., generalized additive models,
multivariate adaptive regression splines and random forest). Our results
identified irrigated farming, thermo-electric energy generation and
urbanization as the most water-intensive anthropogenic activities, on a
per-capita basis. Among the climate factors, precipitation was also
found to be a key predictor of per-capita water withdrawal, with drier
conditions associated with higher water withdrawals. Results of the
first-order sensitivity analysis indicated changes between +/-10\% in
the future water withdrawal across the U.S., in response to
precipitation changes, by the end of the 21st Century under the
business-as-usual scenario. Overall, our study highlights the utility of
leveraging statistical learning theory in developing data-driven models
that can yield valuable insights related to the water withdrawal
patterns across expansive geographical areas.%
\end{abstract}%



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