Variational Mode Decomposition Hybridized With Gradient Boost Regression for Seasonal Forecast of Residential Water Demand
Author:
Publisher
Springer Science and Business Media LLC
Subject
Water Science and Technology,Civil and Structural Engineering
Link
https://link.springer.com/content/pdf/10.1007/s11269-021-02902-7.pdf
Reference33 articles.
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2. Ali M, Prasad R, Xiang Y, Yaseen ZM (2020) Complete ensemble empirical mode decomposition hybridized with random forest and Kernel ridge regression model for monthly rainfall forecasts. J Hydrol 124647 https://doi.org/10.1016/j.jhydrol.2020.124647
3. Bolorinos J, Ajami NK, Rajagopal R (2020) Consumption Change Detection for Urban Planning: Monitoring and Segmenting Water Customers During Drought. Water Resour Res 56:e2019WR025812. https://doi.org/10.1029/2019WR025812
4. Carvalho TMN, Filho F de A de S, Porto VC (2021) Urban Water Demand Modeling Using Machine Learning Techniques: Case Study of Fortaleza, Brazil. J Water Resour Plan Manag. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001310
5. Chang H, Bonnette MR, Stoker P et al (2017) Determinants of single family residential water use across scales in four western US cities. Sci Total Environ 596–597:451–464. https://doi.org/10.1016/j.scitotenv.2017.03.164
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