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Predicting the pulse of urban water demand: a machine learning approach to deciphering meteorological influences

Zarrin, Ziba; Hamidi, Omid; Amini, Payam; Maryanaji, Zohreh

Authors

Ziba Zarrin

Omid Hamidi

Zohreh Maryanaji



Abstract

Objective
This study delves into the impact of urban meteorological elements—specifically, air temperature, relative humidity, and atmospheric pressure—on water consumption in Kamyaran city. Data on urban water consumption, temperature (in Celsius), air pressure (in hectopascals), and relative humidity (in percent) were used for the statistical period 2017–2023. Various models, including the correlation coefficient, generalized additive models (GAM), generalized linear models (GLM), and support vector machines (SVM), were employed to scrutinize the data.

Results
Water consumption increases due to the influence of relative humidity and air pressure when the temperature variable is controlled. Under specific air temperature conditions, elevated air pressure coupled with high relative humidity intensifies the response of water consumption to variations in these elements. Water consumption exhibits heightened sensitivity to high relative humidity and air pressure compared to low levels of these factors. During winter, when a western low-pressure air mass arrives and disrupts normal conditions, causing a decrease in pressure and temperature, urban water consumption also diminishes. The output from the models employed in this study holds significance for enhancing the prediction and management of water resource consumption.

Citation

Zarrin, Z., Hamidi, O., Amini, P., & Maryanaji, Z. (in press). Predicting the pulse of urban water demand: a machine learning approach to deciphering meteorological influences. BMC Research Notes, 17(1), Article 221. https://doi.org/10.1186/s13104-024-06878-6

Journal Article Type Article
Acceptance Date Jul 30, 2024
Online Publication Date Aug 9, 2024
Deposit Date Aug 19, 2024
Publicly Available Date Aug 19, 2024
Journal BMC Research Notes
Electronic ISSN 1756-0500
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 17
Issue 1
Article Number 221
DOI https://doi.org/10.1186/s13104-024-06878-6
Keywords Water consumption of households, Simplex optimization algorithm, Spline smoother, Nonlinear response
Public URL https://keele-repository.worktribe.com/output/885279
Publisher URL https://bmcresnotes.biomedcentral.com/articles/10.1186/s13104-024-06878-6

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Predicting the pulse of urban water demand: a machine learning approach to deciphering meteorological influences (1.4 Mb)
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Licence
https://creativecommons.org/licenses/by-nc-nd/4.0/

Publisher Licence URL
https://creativecommons.org/licenses/by-nc-nd/4.0/

Copyright Statement
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.






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