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Texas A&M Urban HydroInformatics Working Group (TAUHG)

Goals

The advent of advanced metering infrastructure (AMI) deployed by water providers and the increasing availability of related data concerning infrastructure, population, the economy, and the environment, can inform water infrastructure management for current operations, to assist leak detection, user demand characterization, and determine user sensitivity to policy, pricing, and information. Beyond these operational questions, these data can help understand longer term needs in provisioning water provider infrastructure, providing water affordably, and formulating responses to challenges presented by changes in climate, patterns of employment, and economic activity, together with increased urbanization and the development of urban agriculture.

The goals of the Texas A&M Urban Hydroinformatics Working Group (TAUHG) are to

  • Undertake data-driven research that informs current operations of water infrastructure;
  • Develop partnerships with water providers, government, and other stakeholders in order to ground our research in real-world challenges and help anticipate future needs;
  • Identify and address longer term challenges for water supply through interdisciplinary collaboration with researchers in relevant domains within science, engineering, agriculture, and the social and behavioral sciences.

Members

  • Xinyue Ye, (Coordinator) Associate Director, Texas A&M Center for Housing & Urban Development and Associate Professor, Landscape Architecture & Urban Planning
  • Mohsen Aghashahi, Research Engineer III, Texas A&M Institute of Data Science and Electrical & Computer Engineering
  • Allen Berthold, Associate Director, Texas Water Resources Institute
  • Keith Biggers, Interim Director, Texas A&M Center for Applied Technology
  • Nick Duffield, Director, Texas A&M Institute of Data Science and Professor, Electrical & Computer Engineering
  • Bardia Heidari, Research Scientist, Texas Water Resources Institute
  • Wendy Jepson, Associate Director, Texas Water Resources Institute and Professor, Geography
  • Saurav Kumar, Assistant Professor, Texas A&M AgriLife Research, El Paso (waterdmd.info)
  • Zhe Zhang, Assistant Professor, Geography
  • Lei Zou, Assistant Professor, Geography

Contact

Partners

TAUHG welcomes partners from water utilities, industry, academia, and government.

Sponsorship

The Texas A&M Urban HydroInformatics Working Group (TAUHG) is supported by the Texas A&M Institute of Data Science (TAMIDS), Texas A&M AgriLife, and the Texas Water Resources Institute (TWRI)

Resources

  • Useful Links:
  • Relevant Papers:
    • COVID-19
      • Cahill, Joseph, Claire Hoolohan, Rob Lawson, and Alison L. Browne. “COVID‐19 and water demand: A review of literature and research evidence.” Wiley Interdisciplinary Reviews: Water 9, no. 1 (2022): e1570.
      • Ko, Sanghyeon, and Dongwoo Lee. “Interdependencies of Urban Behavioral Dynamics Whilst COVID-19 Spread.” Sustainability 13, no. 17 (2021): 9910.
      • Pesantez, Jorge, and Emily Zechman Berglund. “Evaluating Changes in Urban Water Consumption and Impacts to Water Infrastructure During the COVID-19 Pandemic.” In AGU Fall Meeting Abstracts, vol. 2020, pp. H148-04. 2020.
      • Pesantez, Jorge E., Faisal Alghamdi, Shreya Sabu, G. Mahinthakumar, and Emily Zechman Berglund. “Using a digital twin to explore water infrastructure impacts during the COVID-19 pandemic.” Sustainable Cities and Society 77 (2022): 103520.
      • Salomons, Elad, and Mashor Housh. “Smart Water Meters Can Save Lives during the COVID-19 Pandemic.” Journal of Water Resources Planning and Management 148, no. 5 (2022): 02522003.
      • Spearing, Lauryn A., Helena R. Tiedmann, Lina Sela, Zoltan Nagy, Jessica A. Kaminsky, Lynn E. Katz, Kerry A. Kinney, Mary Jo Kirisits, and Kasey M. Faust. “Human–Infrastructure Interactions during the COVID-19 Pandemic: Understanding Water and Electricity Demand Profiles at the Building Level.” ACS Es&t Water 1, no. 11 (2021): 2327-2338.
      • Zechman Berglund, Emily, Nathalie Thelemaque, Lauryn Spearing, Kasey M. Faust, Jessica Kaminsky, Lina Sela, Erfan Goharian et al. “Water and wastewater systems and utilities: Challenges and opportunities during the COVID-19 pandemic.” Journal of water resources planning and management 147, no. 5 (2021): 02521001.
    • Customer Complaint Processing
      • Tian, Xin, Ina Vertommen, Lydia Tsiami, Peter van Thienen, and Sotirios Paraskevopoulos. “Automated Customer Complaint Processing for Water Utilities
    • Customer Privacy
      • Based on Natural Language Processing—Case Study of a Dutch Water Utility.” Water 14, no. 4 (2022): 674.
      • DiCarlo, Morgan, and Emily Zechman Berglund. “Survey Exploring Water Utility Approaches to Smart Technologies and Customer Complaint Management.” In AGU Fall Meeting 2021. AGU, 2021.
      • Salomons, Elad, Lina Sela, and Mashor Housh. “Hedging for privacy in smart water meters.” Water Resources Research 56, no. 9 (2020): e2020WR027917.
    • Demand Forecast
      • Carvalho, Taís Maria Nunes, and Francisco de Assis de Souza Filho. “Variational Mode Decomposition Hybridized With Gradient Boost Regression for Seasonal Forecast of Residential Water Demand.” Water Resources Management 35, no. 10 (2021): 3431-3445.
      • Capt, Tallen, Ali Mirchi, Saurav Kumar, and W. Shane Walker. “Urban Water Demand: Statistical Optimization Approach to Modeling Daily Demand.” Journal of Water Resources Planning and Management 147, no. 2 (2021): 04020105.
      • Frankel, M., L. Xing, C. Chewning, and L. Sela. “Combined Clustering and Prediction of Daily Water and Energy Usage in Multi-family Residential and Commercial Buildings.” Interactive session 1 Sensing and modelling for urban and agricultural water management Thursday 2nd of September (14: 30-16: 00 UK time) (2021).
      • Frankel, Matthew, Lu Xing, Connor Chewning, and Lina Sela. “Water-energy benchmarking and predictive modeling in multi-family residential and non-residential buildings.” Applied Energy 281 (2021): 116074.
      • Lee, Dongwoo, and Sybil Derrible. “Predicting residential water demand with machine-based statistical learning.” Journal of Water Resources Planning and Management 146, no. 1 (2020): 04019067.
      • Nunes Carvalho, Taís Maria, Francisco de Assis de Souza Filho, and Victor Costa Porto. “Urban Water Demand Modeling Using Machine Learning Techniques: Case Study of Fortaleza, Brazil.” Journal of Water Resources Planning and Management 147, no. 1 (2021): 05020026.
      • Pesantez, Jorge E., Emily Zechman Berglund, and Nikhil Kaza. “Modeling and Forecasting Short-Term Water Demand Reported by Smart Meters.” In WDSA/CCWI Joint Conference Proceedings, vol. 1. 2018.
      • Pesantez, Jorge E., Emily Zechman Berglund, and Nikhil Kaza. “Smart meters data for modeling and forecasting water demand at the user-level.” Environmental Modelling & Software 125 (2020): 104633.
      • Shuang, Qing, and Rui Ting Zhao. “Water demand prediction using machine learning methods: a case study of the Beijing–Tianjin–Hebei region in China.” Water 13, no. 3 (2021): 310.
      • Xenochristou, M., Hutton, C., Hofman, J. and Kapelan, Z., 2021. Short-term forecasting of household water demand in the UK using an interpretable machine learning approach. Journal of Water Resources Planning and Management147(4), p.04021004.
    • Utility Management
      • Cominola, Andrea, I. Monks, and Rodney A. Stewart. “Smart water metering and ai for utility operations and customer engagement: Disruption or incremental innovation?.” HydroLink 4 (2020): 114-119.
      • Link, Michael, and Lina Sela. “Analysis of University Water and Energy Consumption to Support Management and Conservation Strategies.” In World Environmental and Water Resources Congress 2018: Watershed Management, Irrigation and Drainage, and Water Resources Planning and Management, pp. 152-161. Reston, VA: American Society of Civil Engineers, 2018.
    • Water Distribution System Operation
      • Shafiee, M. Ehsan, Amin Rasekh, Lina Sela, and Ami Preis. “Streaming smart meter data integration to enable dynamic demand assignment for real-time hydraulic simulation.” Journal of Water Resources Planning and Management 146, no. 6 (2020): 06020008.
      • Zhuang, Janice G., and Lina Sela. “Network effects of evolving water demand patterns.” In World Environmental and Water Resources Congress 2018: Watershed Management, Irrigation and Drainage, and Water Resources Planning and Management, pp. 93-104. Reston, VA: American Society of Civil Engineers, 2018.
    • Water Pricing
      • Carvalho, Taís Maria Nunes, and Francisco de Assis de Souza Filho. “A data-driven model to evaluate the medium-term effect of contingent pricing policies on residential water demand.” Environmental Challenges 3 (2021): 100033.
  • Datasets: