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- Title
Prediction of Daily Water Consumption in Residential Areas Based on Meteorologic Conditions—Applying Gradient Boosting Regression Tree Algorithm.
- Authors
Li, Zhengxuan; Peng, Sen; Zheng, Guolei; Chu, Xianxian; Tian, Yimei
- Abstract
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.
- Subjects
RESIDENTIAL water consumption; MUNICIPAL water supply; WATER consumption; IRRIGATION scheduling; RESIDENTIAL areas; REGRESSION trees; URBAN planning; WATER utilities
- Publication
Water (20734441), 2023, Vol 15, Issue 19, p3455
- ISSN
2073-4441
- Publication type
Article
- DOI
10.3390/w15193455