Abstract
Climate variability highly influences water availability and demand in urban areas, but medium-term predictive models of residential water demand usually do not include climate variables. This study proposes a method to predict monthly residential water demand using temperature and precipitation, by combining a novel decomposition technique and gradient boost regression. The variational mode decomposition (VMD) was used to filter the water demand time series and remove the component associated with the socioeconomic characteristics of households. VMD was also used to extract the relevant signal from precipitation and maximum temperature series which could explain water demand. The results indicate that by filtering the water demand and climate signals we can obtain accurate predictions at least four months in advance. These results suggest that the climate information can be used to explain and predict residential water demand.
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Data Availability
Some or all data and models that support the findings of this study are available from the corresponding author upon request.
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Funding
This study was supported by the Brazilian Federal Agency for Support and Evaluation of Graduate Education (Grant No. 88887.123932/2015–00); the Brazilian Council for Scientific and Technological Development (Grant No. 441457/2017–7); and the Cearense Foundation for Scientific and Technological Support (Cientista-chefe program).
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Taís Maria Nunes Carvalho. The first draft of the manuscript was written by Taís Maria Nunes Carvalho and the review and editing was performed by Taís Maria Nunes Carvalho and Francisco de Assis de Souza Filho. All authors read and approved the final manuscript.
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Carvalho, T.M.N., de Assis de Souza Filho, F. Variational Mode Decomposition Hybridized With Gradient Boost Regression for Seasonal Forecast of Residential Water Demand. Water Resour Manage 35, 3431–3445 (2021). https://doi.org/10.1007/s11269-021-02902-7
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DOI: https://doi.org/10.1007/s11269-021-02902-7