Machine Learning Based Prediction of Water Demand in Megacities: A Case Study of Beijing
DOI:
https://doi.org/10.5281/zenodo.10791242Keywords:
sustainability, nature-based solution, sustainable development goalsAbstract
With the acceleration of urbanization, smart cities are increasingly entering the public consciousness. Beijing, a city facing severe water scarcity, has seen some relief in its water supply pressure through the South-to-North Water Diversion Project. However, as economic development progresses and the population continues to expand, the demand for water in Beijing is still on the rise. Conducting a scientific and rational prediction of water demand is a prerequisite and foundation for planning and constructing future water supply projects. This paper embarks on a study of water demand prediction in Beijing, China, initially identifying 13 explanatory variables related to economics, society, water usage, and resources. Utilizing data from Beijing from 2004 to 2020, a predictive model encompassing both statistical and machine learning models for water demand was established. The findings indicate that among all the models considered, the Random Forest model performed the best, with R2 scores of 97.9% and 97.8%, respectively. A comparative analysis of the model's predictive performance further demonstrates the superiority of machine learning models over statistical models. The results of this study offer valuable insights for the planning and construction of future water supply projects in Beijing. They can serve as a reference for the formulation of water supply management policies in other cities.
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