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Variational Mode Decomposition Hybridized With Gradient Boost Regression for Seasonal Forecast of Residential Water Demand

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  • Taís Maria Nunes Carvalho

    (Dept. of Hydraulic and Environmental Engineering, Universidade Federal Do Ceará)

  • Francisco Souza Filho

    (Universidade Federal Do Ceará)

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.

Suggested Citation

  • Taís Maria Nunes Carvalho & Francisco Souza Filho, 2021. "Variational Mode Decomposition Hybridized With Gradient Boost Regression for Seasonal Forecast of Residential Water Demand," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(10), pages 3431-3445, August.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:10:d:10.1007_s11269-021-02902-7
    DOI: 10.1007/s11269-021-02902-7
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    References listed on IDEAS

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    1. Md Haque & Ataur Rahman & Dharma Hagare & Golam Kibria, 2014. "Probabilistic Water Demand Forecasting Using Projected Climatic Data for Blue Mountains Water Supply System in Australia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(7), pages 1959-1971, May.
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    Cited by:

    1. Huseyin Cagan Kilinc & Iman Ahmadianfar & Vahdettin Demir & Salim Heddam & Ahmed M. Al-Areeq & Sani I. Abba & Mou Leong Tan & Bijay Halder & Haydar Abdulameer Marhoon & Zaher Mundher Yaseen, 2023. "Daily Scale River Flow Forecasting Using Hybrid Gradient Boosting Model with Genetic Algorithm Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3699-3714, July.
    2. Kang, Yan & Chen, Peiru & Cheng, Xiao & Zhang, Shuo & Song, Songbai, 2022. "Novel hybrid machine learning framework with decomposition–transformation and identification of key modes for estimating reference evapotranspiration," Agricultural Water Management, Elsevier, vol. 273(C).

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