Dataset Information

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

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.

SUBMITTER: Zarrin Z

PROVIDER: S-EPMC11316424 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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Publications

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

Zarrin Ziba Z Hamidi Omid O Amini Payam P Maryanaji Zohreh Z

BMC research notes 20240809 1

<h4>Objective</h4>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 ...[more]

PMID: 39123190

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Dataset Information

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

Objective

Results

Publications

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

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