Short-term water demand forecasting: a review

References

• Abadi, M. L., A. Samé, L. Oukhellou, N. Cheifetz, P. Mandel, C. Féliers, and O. Chesneau. 2017. “Predictive Classification of Water Consumption Time Series Using Non-Homogeneous Markov Mod- Els.” In 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 323–331. IEEE.
   Google Scholar
• Al-Zahrani, M. A., and A. Abo-Monasar. 2015. “Urban Residential Water Demand Prediction Based on Artificial Neural Networks and Time Series Models.” Water Resources Management 29 (10): 3651–3662. https://doi.org/10.1007/s11269-015-1021-z.
   Web of Science ®Google Scholar
• Bata, M. H., R. Carriveau, and D. S.-K. Ting. 2020. “Short-Term Wa- Ter Demand Forecasting Using Nonlinear Autoregressive Artificial Neural Networks.” Journal of Water Resources Planning and Management 146 (3): 04020008. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001165.
   Web of Science ®Google Scholar
• Bata, M., R. Carriveau, and D. S.-K. Ting. 2020. “Short-Term Water Demand Forecasting Using Hybrid Supervised and Unsupervised Machine Learning Model.” Smart Water 5 (1): 1–18. https://doi.org/10.1186/s40713-020-00020-y.
   Google Scholar
• Bennett, C., R. A. Stewart, and C. D. Beal. 2013. “Ann-Based Residential Water End-Use Demand Forecasting Model.” Expert Systems with Applications 40 (4): 1014–1023. https://doi.org/10.1016/j.eswa.2012.08.012.
   Web of Science ®Google Scholar
• Bich-Ngoc, N., Prevedello, C., Cools, M., & Teller, J. 2022. “Factors Influencing Residential Water Consumption in Wallonia, Belgium.” Utilities Policy 74: 101281. https://doi.org/10.1016/j.jup.2021.101281.
   Web of Science ®Google Scholar
• Blokker, E., J. Vreeburg, and J. Van Dijk. 2010. “Simulating Residential Water Demand with a Stochastic End-Use Model.” Journal of Water Resources Planning and Management 136 (1): 19–26. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000002.
   Web of Science ®Google Scholar
• Caiado, J. 2010. “Performance of Combined Double Seasonal Univariate Time Series Models for Forecasting Water Demand.” Journal of Hydrologic Engineering 15 (3): 215–222. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000182.
   Web of Science ®Google Scholar
• Candelieri, A. 2017. “Clustering and Support Vector Regression for Water Demand Forecasting and Anomaly Detection.” Water 9 (3): 224. https://doi.org/10.3390/w9030224.
   Web of Science ®Google Scholar
• Chen, G., T. Long, Y. Bai, and J. Zhang. 2019. “A Forecasting Framework Based on Kalman Filter Integrated Multivariate Local Polynomial Regression: Application to Urban Water Demand.” Neural Process- Ing Letters 50 (1): 497–513. https://doi.org/10.1007/s11063-019-10001-3.
   Web of Science ®Google Scholar
• Coelho, B., and A. G. Andrade-Campos. 2019. “Short-Term Forecasting of Hourly Water Demands-A Portuguese Case Study.” International Journal of Water 13 (2): 173–207. https://doi.org/10.1504/IJW.2019.099515.
   Google Scholar
• Du, B., S. Huang, J. Guo, H. Tang, L. Wang, and S. Zhou. 2022. “Interval Forecasting for Urban Water Demand Using Pso Optimized Kde Distribution and Lstm Neural Networks.” Applied Soft Computing 122:108875. https://doi.org/10.1016/j.asoc.2022.108875.
   Web of Science ®Google Scholar
• Du, B., Q. Zhou, J. Guo, S. Guo, and L. Wang. 2021. “Deep Learning with Long Short-Term Memory Neural Networks Combining Wavelet Transform and Principal Component Analysis for Daily Urban Water Demand Forecasting.” Expert Systems with Applications 171:114571. https://doi.org/10.1016/j.eswa.2021.114571.
   Web of Science ®Google Scholar
• Gagliardi, F., S. Alvisi, Z. Kapelan, and M. Franchini. 2017. “A Proba- Bilistic Short-Term Water Demand Forecasting Model Based on the Markov Chain.” Water 9 (7): 507. https://doi.org/10.3390/w9070507.
   Web of Science ®Google Scholar
• Guo, G., S. Liu, Y. Wu, J. Li, R. Zhou, and X. Zhu. 2018. “Short-Term Water Demand Forecast Based on Deep Learning Method.” Journal of Water Resources Planning and Management 144 (12): 04018076. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000992.
   Web of Science ®Google Scholar
• Herrera, M., L. Torgo, J. Izquierdo, and R. Pérez-Garcıa. 2010. “Predictive Models for Forecasting Hourly Urban Water Demand.” Journal of Hydrology 387 (1–2): 141–150. https://doi.org/10.1016/j.jhydrol.2010.04.005.
   Web of Science ®Google Scholar
• Kühnert, C., N. M. Gonuguntla, H. Krieg, D. Nowak, and J. A. Thomas. 2021. “Application of Lstm Networks for Water Demand Predic- Tion in Optimal Pump Control.” Water 13 (5): 644. https://doi.org/10.3390/w13050644.
   Web of Science ®Google Scholar
• Liu, J., L. Zhao, and Y. Mao. 2019. “Bayesian Regularized NAR Neural Network Based Short-Term Prediction Method of Water Consumption.” In E3S Web of Conferences, 03024. Vol. 118. EDP Sciences.
   Google Scholar
• Makki, A. A., R. A. Stewart, C. D. Beal, and K. Panuwatwanich. 2015. “Novel Bottom-Up Urban Water Demand Forecasting Model: Re- Vealing the Determinants, Drivers and Predictors of Residential Indoor End-Use Consumption.” Resources, Conservation and Re- Cycling 95:15–37. https://doi.org/10.1016/j.resconrec.2014.11.009.
   Web of Science ®Google Scholar
• Mouatadid, S., and J. Adamowski. 2017. “Using Extreme Learning Machines for Short-Term Urban Water Demand Forecasting.” Urban Water Journal 14 (6): 630–638. https://doi.org/10.1080/1573062X.2016.1236133.
   Web of Science ®Google Scholar
• Mu, L., F. Zheng, R. Tao, Q. Zhang, and Z. Kapelan. 2020. “Hourly and Daily Urban Water Demand Predictions Using a Long Short-Term Memory Based Model.” Journal of Water Resources Planning and Management 146 (9): 05020017. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001276.
   Web of Science ®Google Scholar
• Nunes Carvalho, T. M., F. d. A. de Souza Filho, and V. C. Porto. 2021. “Ur- Ban Water Demand Modeling Using Machine Learning Techniques: Case Study of Fortaleza, Brazil.” Journal of Water Resources Planning and Management 147 (1): 05020026. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001310.
   Web of Science ®Google Scholar
• Odan, F. K., and L. F. R. Reis. 2012. “Hybrid Water Demand Forecasting Model Associating Artificial Neural Network with Fourier Series.” Journal of Water Resources Planning and Management 138 (3): 245–256. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000177.
   Web of Science ®Google Scholar
• Pesantez, J. E., E. Z. Berglund, and N. Kaza. 2018. “Modeling and Forecast- Ing Short-Term Water Demand Reported by Smart meters: (187).” In WDSA/CCWI Joint Conference Proceedings. Vol. 1.
   Google Scholar
• Rahim, M. S., K. A. Nguyen, R. A. Stewart, D. Giurco, and M. Blumenstein. 2020. “Machine Learning and Data Analytic Techniques in Digital Water Metering: A Review.” Water 12 (1): 294. https://doi.org/10.3390/w12010294.
   Web of Science ®Google Scholar
• Rathnayaka, K., H. Malano, M. Arora, B. George, S. Maheepala, and B. Nawarathna. 2017. “Prediction of Urban Residential End- Use Water Demands by Integrating Known and Unknown Water Demand Drivers at Multiple Scales Ii: Model Application and Validation.” Resources, Conservation and Recycling 118:1–12. https://doi.org/10.1016/j.resconrec.2016.11.015.
   Web of Science ®Google Scholar
• Romano, M., and Z. Kapelan. 2014. “Adaptive Water Demand Forecasting for Near Real-Time Management of Smart Water Distribution Systems.” Environmental Modelling & Software 60:265–276. https://doi.org/10.1016/j.envsoft.2014.06.016.
   Web of Science ®Google Scholar
• Sardinha-Lourenço, A., A. Andrade-Campos, A. Antunes, and M. Oliveira. 2018. “Increased Performance in the Short-Term Water Demand Forecasting Through the Use of a Parallel Adaptive Weighting Strategy.” Journal of Hydrology 558:392–404. https://doi.org/10.1016/j.jhydrol.2018.01.047.
   Web of Science ®Google Scholar
• Stańczyk, J., J. Kajewska-Szkudlarek, P. Lipiński, and P. Rychlikowski. 2022. “Improving Short-Term Water Demand Forecasting Using Evolutionary Algorithms.” Scientific Reports 12 (1): 1–25. https://doi.org/10.1038/s41598-022-17177-0.
   PubMedGoogle Scholar
• Timotewos, M. T., M. Barjenbruch, and B. M. Behailu. 2022. “The As- Sessment of Climate Variables and Geographical Distribution on Residential Drinking Water Demand in Ethiopia.” Water 14 (11): 1722. https://doi.org/10.3390/w14111722.
   Web of Science ®Google Scholar
• Tiwari, M. K., and J. Adamowski. 2013. “Urban Water Demand Forecasting and Uncertainty Assessment Using Ensemble Wavelet-Bootstrap- Neural Network Models.” Water Resources Research 49 (10): 6486–6507. https://doi.org/10.1002/wrcr.20517.
   Web of Science ®Google Scholar
• Tiwari, M. K., and J. F. Adamowski. 2017. “An Ensemble Wavelet Bootstrap Machine Learning Approach to Water Demand Forecasting: A Case Study in the City of Calgary, Canada.” Urban Water Journal 14 (2): 185–201. https://doi.org/10.1080/1573062X.2015.1084011.
   Web of Science ®Google Scholar
• Vijai, P., and P. B. Sivakumar. 2018. “Performance Comparison of Techniques for Water Demand Forecasting.” Procedia Computer Science 143:258–266. https://doi.org/10.1016/j.procs.2018.10.394.
   Google Scholar
• Wu, S., H. Han, B. Hou, and K. Diao. 2020. “Hybrid Model for Short- Term Water Demand Forecasting Based on Error Correction Using Chaotic Time Series.” Water 12 (6): 1683. https://doi.org/10.3390/w12061683.
   Web of Science ®Google Scholar
• Xenochristou, M., and Z. Kapelan. 2020. “An Ensemble Stacked Model with Bias Correction for Improved Water Demand Forecasting.” Urban Water Journal 17 (3): 212–223. https://doi.org/10.1080/1573062X.2020.1758164.
   Web of Science ®Google Scholar
• Xu, Y., J. Zhang, Z. Long, and M. Lv. 2019. “Daily Urban Water Demand Forecasting Based on Chaotic Theory and Continuous Deep Belief Neural Network.” Neural Processing Letters 50 (2): 1173–1189. https://doi.org/10.1007/s11063-018-9914-5.
   Web of Science ®Google Scholar
• Xu, Y., J. Zhang, Z. Long, H. Tang, and X. Zhang. 2019. “Hourly Urban Water Demand Forecasting Using the Continuous Deep Belief Echo State Network.” Water 11 (2): 351. https://doi.org/10.3390/w11020351.
   Web of Science ®Google Scholar
• Zhou, S., S. Guo, B. Du, S. Huang, and J. Guo. 2022. “A Hybrid Framework for Multivariate Time Series Forecasting of Daily Urban Water Demand Using Attention-Based Convolutional Neural Network and Long Short-Term Memory Network.” Sustainability 14 (17): 11086. https://doi.org/10.3390/su141711086.
   Web of Science ®Google Scholar
• Zhou, S. L., T. A. McMahon, A. Walton, and J. Lewis. 2002. “Forecasting Operational Demand for an Urban Water Supply Zone.” Journal of Hydrology 259 (1–4): 189–202. https://doi.org/10.1016/S0022-1694(01)00582-0.
   Web of Science ®Google Scholar
• Zounemat-Kermani, M., E. Matta, A. Cominola, X. Xia, Q. Zhang, Q. Liang, and R. Hinkelmann. 2020. “Neurocomputing in Sur- Face Water Hydrology and Hydraulics: A Review of Two Decades Retrospective, Current Status and Future Prospects.” Journal of Hydrology 588:125085. https://doi.org/10.1016/j.jhydrol.2020.125085.
   Web of Science ®Google Scholar
• Zubaidi, S. L., S. K. Gharghan, J. Dooley, R. M. Alkhaddar, and M. Abdellatif. 2018. “Short-Term Urban Water Demand Prediction Consid- Ering Weather Factors.” Water Resources Management 32 (14): 4527–4542. https://doi.org/10.1007/s11269-018-2061-y.
   Web of Science ®Google Scholar