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Water Resources Management

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07-07-2021

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

Authors: Taís Maria Nunes Carvalho, Francisco de Assis de Souza Filho

Published in: Water Resources Management | Issue 10/2021

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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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Adamowski J, Adamowski K, Prokoph A (2013) A Spectral Analysis Based Methodology to Detect Climatological Influences on Daily Urban Water Demand. Math Geosci 45:49–68. https://doi.org/10.1007/s11004-012-9427-0CrossRef

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

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

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

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.2017.03.164CrossRef

Chang H, Praskievicz S, Bureau PW, et al (2014) Sensitivity of Urban Water Consumption to Weather and Climate Variability at Multiple Temporal Scales: The Case of Portland, Oregon

Chu L, Quentin Grafton R (2019) Policy Note: Short-term Pain for Long-term Gain: Urban Water Pricing and the Risk-adjusted User Cost. Water Econ Policy 5:1871005. https://doi.org/10.1142/S2382624X18710054CrossRef

Dragomiretskiy K, Zosso D (2014) Variational mode decomposition. IEEE Trans Signal Process 62:531–544. https://doi.org/10.1109/TSP.2013.2288675CrossRef

Duerr I, Merrill HR, Wang C et al (2018) Forecasting urban household water demand with statistical and machine learning methods using large space-time data: A Comparative study. Environ Model Softw 102:29–38. https://doi.org/10.1016/j.envsoft.2018.01.002CrossRef

Feng Z, kai, Niu W jing, Tang Z yang, et al (2020) Monthly runoff time series prediction by variational mode decomposition and support vector machine based on quantum-behaved particle swarm optimization. J Hydrol 583:124627. https://doi.org/10.1016/j.jhydrol.2020.124627CrossRef

Fiorillo D, Kapelan Z, Xenochristou M et al (2021) Assessing the Impact of Climate Change on Future Water Demand using Weather Data. Water Resour Manag 35:1449–1462. https://doi.org/10.1007/s11269-021-02789-4CrossRef

Friedman JH (2001) Greedy function approximation: A gradient boosting machine. Ann Stat 29:1189–1232. https://doi.org/10.1214/aos/1013203451CrossRef

Gao Y, Ge G, Sheng Z, Sang E (2008) Analysis and Solution to the Mode Mixing Phenomenon in EMD. In: 2008 Congress on Image and Signal Processing. IEEE, pp 223–227

Haque MM, Rahman A, Hagare D, Kibria G (2014) Probabilistic Water Demand Forecasting Using Projected Climatic Data for Blue Mountains Water Supply System in Australia. Water Resour Manag 28:1959–1971. https://doi.org/10.1007/s11269-014-0587-1CrossRef

Hastenrath S, Heller L (1977) Dynamics of climatic hazards in northeast Brazil. Q J R Meteorol Soc 103:77–92. https://doi.org/10.1002/qj.49710343505CrossRef

House-Peters LA, Chang H (2011) Urban water demand modeling: Review of concepts, methods, and organizing principles. Water Resour Res 47:351–360. https://doi.org/10.1029/2010WR009624CrossRef

Huang NE, Shen Z, Long SR et al (1998) The empirical mode decomposition and the Hubert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc A Math Phys Eng Sci 454:903–995. https://doi.org/10.1098/rspa.1998.0193CrossRef

Jiménez Cisneros BE, Oki TNW, Arnell GB et al (2014) Freshwater Resources. In: Field CB, Barros VR, Dokken DJ et al (eds) Climate Change 2014 Impacts, Adaptation, and Vulnerability. Cambridge University Press, Cambridge, pp 229–270

Lee D, Derrible S (2020) Predicting Residential Water Demand with Machine-Based Statistical Learning. J Water Resour Plan Manag 146:04019067. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001119CrossRef

Maidment DR, Miaou S-P (1986) Daily Water Use in Nine Cities. Water Resour Res 22:845–851. https://doi.org/10.1029/WR022i006p00845CrossRef

Milly PCD, Betancourt J, Falkenmark M, et al (2008) Stationarity Is Dead: Whither Water Management? Science (80- ) 319:573–574. https://doi.org/10.1126/science.1151915

Olmstead SM (2014) Climate change adaptation and water resource management: A review of the literature. Energy Econ 46:500–509. https://doi.org/10.1016/j.eneco.2013.09.005CrossRef

Orlowsky B, Seneviratne SI (2012) Global changes in extreme events: Regional and seasonal dimension. Clim Change 110:669–696. https://doi.org/10.1007/s10584-011-0122-9CrossRef

Parandvash GH, Chang H (2016) Analysis of long-term climate change on per capita water demand in urban versus suburban areas in the Portland metropolitan area, USA. J Hydrol 538:574–586. https://doi.org/10.1016/j.jhydrol.2016.04.035CrossRef

Pontes Filho JD, de Souza Filho F, A, Martins ESPR, Studart TM de C, (2020) Copula-Based Multivariate Frequency Analysis of the 2012–2018 Drought in Northeast Brazil. Water 12:834. https://doi.org/10.3390/w12030834CrossRef

Rasifaghihi N, Li SS, Haghighat F (2020) Forecast of urban water consumption under the impact of climate change. Sustain Cities Soc 52:101848. https://doi.org/10.1016/j.scs.2019.101848CrossRef

Torres ME, Colominas MA, Schlotthauer G, Flandrin P (2011) A complete ensemble empirical mode decomposition with adaptive noise. In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. pp 4144–4147

Vasconcelos Junior FDC, Jones C, Gandu AW (2018) Interannual and intraseasonal variations of the onset and demise of the pre-wet season and the wet season in the northern northeast Brazil. Rev Bras Meteorol 33:472–484. https://doi.org/10.1590/0102-7786333007CrossRef

Villarin MC, Rodriguez-Galiano VF (2019) Machine Learning for Modeling Water Demand. J Water Resour Plan Manag 145:04019017. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001067CrossRef

Wu Z, Huang NE (2009) Ensemble empirical mode decomposition: A noise-assisted data analysis method. Adv Adapt Data Anal 1:1–41. https://doi.org/10.1142/S1793536909000047CrossRef

Yeh JR, Shieh JS, Huang NE (2010) Complementary ensemble empirical mode decomposition: A novel noise enhanced data analysis method. Adv Adapt Data Anal 2:135–156. https://doi.org/10.1142/S1793536910000422CrossRef

Zubaidi SL, Ortega-Martorell S, Kot P et al (2020) A Method for Predicting Long-Term Municipal Water Demands Under Climate Change. Water Resour Manag 34:1265–1279. https://doi.org/10.1007/s11269-020-02500-zCrossRef

Zuo G, Luo J, Wang N et al (2020) Decomposition ensemble model based on variational mode decomposition and long short-term memory for streamflow forecasting. J Hydrol 124776 https://doi.org/10.1016/j.jhydrol.2020.124776

Title: Variational Mode Decomposition Hybridized With Gradient Boost Regression for Seasonal Forecast of Residential Water Demand
Authors: Taís Maria Nunes Carvalho
Francisco de Assis de Souza Filho
Publication date: 07-07-2021
Publisher: Springer Netherlands
Published in: Water Resources Management / Issue 10/2021
Print ISSN: 0920-4741
Electronic ISSN: 1573-1650
DOI: https://doi.org/10.1007/s11269-021-02902-7

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