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Machine Learning Modeling of Water Use Patterns in Small Disadvantaged Communities

Authors :
Yang Zhou
Bilal Khan
Yoram Cohen
Jin Yong Choi
Source :
Water, Volume 13, Issue 16, Water, Vol 13, Iss 2312, p 2312 (2021)
Publication Year :
2021
Publisher :
Multidisciplinary Digital Publishing Institute, 2021.

Abstract

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.

Details

Language :
English
ISSN :
20734441
Database :
OpenAIRE
Journal :
Water
Accession number :
edsair.doi.dedup.....9521edaf0f15a853b1c149989b1e0fcb
Full Text :
https://doi.org/10.3390/w13162312