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 r

SUBMITTER: Zarrin Z 

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

REPOSITORIES: biostudies-literature