Predicting the pulse of urban water demand: a machine learning approach to deciphering meteorological influences
(2024)
Journal Article
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
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),... Read More about Predicting the pulse of urban water demand: a machine learning approach to deciphering meteorological influences.