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Article

Machine Learning Modeling of Water Use Patterns in Small Disadvantaged Communities

1
Chemical and Biomolecular Engineering Department, University of California, Los Angeles, CA 90095, USA
2
Department of Automation, Shanghai University, Shanghai 200444, China
3
Department of Computer Science and Engineering, California State University San Bernardino, 5500 University Parkway, San Bernardino, CA 92407, USA
4
Institute of the Environment and Sustainability, University of California, Los Angeles, CA 90095, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Chicgoua Noubactep and Hossein Tabari
Water 2021, 13(16), 2312; https://doi.org/10.3390/w13162312
Received: 17 July 2021 / Revised: 8 August 2021 / Accepted: 17 August 2021 / Published: 23 August 2021
(This article belongs to the Section Water Use and Scarcity)
Water use patterns were explored for three small communities that are located in proximity to agricultural fields and rely on their local wells for potable water supply. High-resolution water use data, collected over a four-year period, revealed significant temporal variability. Monthly, daily, and hourly water use patterns were well described by autoregressive moving average (ARMA) models. Model development was supported by unsupervised clustering analysis via self-organizing maps (SOMs) that revealed similarities of water use patterns and confirmed the time-series water use model attributes. The inclusion of ambient temperature and rainfall as model attributes improved ARMA model performance for daily and hourly water use from R2 ~0.86–0.87 to 0.94–0.97 and from R2 ~0.85–0.89 to 0.92–0.98, respectively. Water use predictions for an entire year forward in time was feasible demonstrating ARMA models’ performance of (i) R2 ~0.90–0.94 and average absolute relative error (AARE) of ~2.9–4.9% for daily water use, and (ii) R2 ~0.81–0.95 and AARE ~1.9–3.8% for hourly water use. The study suggests that ARMA modeling should be useful for analysis of temporally variable water use in support of water source management, as well as assessing capacity building for small water systems including water treatment needs and wastewater handling. View Full-Text
Keywords: water use patterns; small communities; potable well water; autoregressive moving average (ARMA) model; self-organizing map (SOM) water use patterns; small communities; potable well water; autoregressive moving average (ARMA) model; self-organizing map (SOM)
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    Doi: 10.5068/D15D61
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    Description: Machine Learning Modeling of Water Use Patterns in Small Disadvantaged Communities: Supplementary Materials
MDPI and ACS Style

Zhou, Y.; Khan, B.M.; Choi, J.Y.; Cohen, Y. Machine Learning Modeling of Water Use Patterns in Small Disadvantaged Communities. Water 2021, 13, 2312. https://doi.org/10.3390/w13162312

AMA Style

Zhou Y, Khan BM, Choi JY, Cohen Y. Machine Learning Modeling of Water Use Patterns in Small Disadvantaged Communities. Water. 2021; 13(16):2312. https://doi.org/10.3390/w13162312

Chicago/Turabian Style

Zhou, Yang, Bilal M. Khan, Jin Y. Choi, and Yoram Cohen. 2021. "Machine Learning Modeling of Water Use Patterns in Small Disadvantaged Communities" Water 13, no. 16: 2312. https://doi.org/10.3390/w13162312

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