Prediction of Daily Water Consumption in Residential Areas Based on Meteorologic Conditions—Applying Gradient Boosting Regression Tree Algorithm

A more accurate way of water consumption forecasting can be used to help people develop a scheduling plan of water workers more targeting; therefore, this paper aims to establish a forecast model of daily water consumption based on meteorological conditions. At present, most studies of daily water c...

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Bibliographic Details
Published in:Water (Basel) Vol. 15; no. 19; p. 3455
Main Authors: Li, Zhengxuan, Peng, Sen, Zheng, Guolei, Chu, Xianxian, Tian, Yimei
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.10.2023
Subjects:
Accuracy
Algorithms
Analysis
China
Correlation analysis
Earth temperature
Forecasting
Genetic algorithms
Internet of Things
Machine learning
meteorology
Monte Carlo simulation
Neighborhoods
Population growth
prediction
public water supply
regression analysis
residential areas
solar radiation
temperature
Time series
urban development
Water
Water supply
Water use
water utilities
wind
China
ISSN:2073-4441, 2073-4441
Online Access:Get full text
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Summary:A more accurate way of water consumption forecasting can be used to help people develop a scheduling plan of water workers more targeting; therefore, this paper aims to establish a forecast model of daily water consumption based on meteorological conditions. At present, most studies of daily water consumption forecasts focus on historical data or single water use influencing factors; moreover, daily water consumption could be influenced by meteorologic conditions. The influence of complex meteorology factors on water consumption is analyzed based on a gradient-boosted regression tree (GBRT) model. The correlation of 10 meteorologic factors has been discussed and divided into 5 categories, including temperature factor, pressure factor, precipitation factor, sunshine factor, and wind factor. Through the GBRT algorithm, the daily water consumption of residential area could be predicted with a maximum error of ±8%. The results show that the average ground temperature (the feature importance accounts for 81% of the total) has the greatest impact on the daily water consumption of the residential community, followed by the somatosensory temperature (the feature importance accounts for 7% of the total). The method can provide the daily water consumption of water consumption nodes with higher precision for municipal water supply network model accuracy. It also provides a reference for water utility operation schemes and urban development planning.
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ISSN:2073-4441
2073-4441
DOI:10.3390/w15193455