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VERSION:2.0
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CALSCALE:GREGORIAN
X-WR-CALNAME:Sustainable Systems Seminar Lunch Series -  Quantifying Uncert
 ainty in Urban Water Demand Projections
X-WR-TIMEZONE:Pacific Time (US & Canada)
BEGIN:VEVENT
DTSTAMP:20260722T234138Z
UID:tag:localist.com\,2008:EventInstance_52623171418123
DTSTART:20260421T190000Z
DTEND:20260421T200000Z
DESCRIPTION:This week's speaker is:\n\nAniket Verma\, Ph.D. Candidate\, Sta
 nford University\n"Quantifying Uncertainty in Urban Water Demand Projectio
 ns"\n\nAbstract:\n\nWater demand projections are essential for urban water
  supply planning. Yet\, long-term demand projections are highly overestima
 ted and uncertain\, potentially warranting unneeded costly infrastructure\
 , which may lead to irreversible environmental impacts and exacerbate wate
 r affordability outcomes. While demand forecasting methods have improved\,
  uncertainty in long-term demand projections remains poorly understood. Ou
 r study develops a novel uncertainty characterization framework that can q
 uantify and partition uncertainty in long-term urban water demand projecti
 ons across planning-relevant spatial scales. Results show that demand unce
 rtainty at the household-\, neighborhood-\, and city-scale is dominated by
  human behavior\, population growth and housing diversity\, and economic t
 rends and policy action\, respectively. Our analysis offers new insights i
 nto the magnitude and sources of urban water demand uncertainty\, informin
 g utility decisions about urban water supply planning\, infrastructure inv
 estments\, and conservation policies.\n\n\nBio:\n\nAniket is a third-year 
 PhD candidate in Civil and Environmental Engineering (CEE) at Stanford Uni
 versity and is advised by Prof. Sarah Fletcher. He completed his MS in Env
 ironmental Engineering at Stanford in 2023 and is a recipient of the 2025 
 Natural Sciences and Engineering Research Council of Canada (NSERC) Postgr
 aduate Scholarship-Doctoral (PGSD). His research integrates a systems mode
 ling framework grounded in decision making under deep uncertainty (DMDU) a
 nd reinforcement learning (RL) principles together with uncertainty quanti
 fication and hydrologic and econometric modeling. His primary research are
 a focuses on investigating the impact of demand uncertainty in urban water
  resources planning.\n\nThe topics of this seminar are broad but typically
  fall under technologies’ scaling potential and impact on (the system of
 ) people\, the environment and the economy. A particular focus is placed o
 n the interaction potential of technologies with the energy\, water\, and 
 material systems. Our goal is to create an intimate\, collaborative space 
 for students\, postdocs\, scientists\, and PIs within Stanford across micr
 o-level (material and technology) to macro-level (system) interests. These
  seminars will provide an opportunity to disseminate insights from your st
 udies\, connect with fellow researchers\, and strengthen bonds across the 
 community.
GEO:37.425273;-122.172422
LOCATION:Press\, 106
SUMMARY:Sustainable Systems Seminar Lunch Series -  Quantifying Uncertainty
  in Urban Water Demand Projections
URL;VALUE=URI:https://events.stanford.edu/event/sustainable-systems-seminar
 -lunch-series-QuantifyingUncertaintyinUrbanWaterDemandProjections
CATEGORIES:Class/Seminar
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