Water demand forecast: a literature review
DOI:
https://doi.org/10.33448/rsd-v12i4.40819Keywords:
Water demand; Demand forecast; Time series; Neural networks; Smart Cities.Abstract
Forecasting water demand is fundamental to a region's social and economic development. In the literature there are several studies with specific applications, However, the topic still lacks a comprehensive view. Therefore, this article proposes a integrative review of the literature, to obtain an overview of the subject (methods, areas of application, objectives, and other factors). Using Methodi Ordinatio methodology, 74 articles with scientific relevance for analysis were selected, most of them published in the USA, Australia, and the United Kingdom. It was concluded that in the use of methods there is a predominance of the approach of artificial neural networks and regression analyzes. As for the application, most studies were for forecasting residential demand.
References
Adamowski, J. F.(2008). Peak daily water demand forecast modeling using artificial neural networks. Journal of Water Resources Planning and Management, 134 (2), 119-128. 10.1061/(ASCE)0733-9496(2008)134
Amelin, M. (2013). Monte Carlo Simulation in Engineering. KTH Royal Institute of Technology, Stockholm. https://www.kth.se/social/files/55e017b4f276545643070e39/Monte%20Carlo%20Simulation%20in%20Engineering.pdf. Access in: May 11, 2022.
Ajbar, A., & Ali, E.M. (2015). Prediction of municipal water production in touristic Mecca City in Saudi Arabia using neural networks. Journal of King Saud University - Engineering Sciences, 27(1), 89-91. 10.1016/j.jksues.2013.01.001
Arandia, E., Eck, B., & Mckenna, S. (2014). The effect of temporal resolution on the accuracy of forecasting models for total system demand. Procedia Engineering, 89, 916-925. 10.1016/j.proeng.2014.11.525
Ashoori, N., Dzombak, D. A., & Small, M. J. (2017). Identifying water price and population criteria for meeting future urban water demand targets. Journal of Hydrology, 555, 547-556. 10.1016/j.jhydrol.2017.10.047
Bakker, M., Vreeburg, J. H. G., Van Schagen, K. M., & Rietveld, L. C. (2013). A fully adaptive forecasting model for short-term drinking water demand. Environmental Modelling and Software, 48, 141-151. 10.1016/j.proeng.2014.02.012
Basheer, I. A., & Hajmeer, M. (2000). Artificial neural networks: Fundamentals, computing, design, and application. Journal of Microbiological Methods, 43(1), 3–31.10.1016/j.envsoft.2013.06.012
Batty, M., Axhausen, K. W., Giannotti, F., Pozdnoukhov, A., Bazzani, A., Wachowicz, M., Ouzounis, G., & Portugali, Y. Smart cities of the future (2012). The European Physical Journal Special Topics, 214(1), 481-518, 2012. 10.1140/epjst/e2012-01703-3
Bennett, C., Stewart, R.A., & Beal, C.D. (2013). ANN-based residential water end-use demand forecasting model. Expert Systems with Applications, 40(4), 1014-1023. 10.1016/j.eswa.2012.08.012
Brentan, B. M., Luvizotto Jr., E., Herrera, M., Izquierdo, J., & Pérez-García, R. (2017). Hybrid regression model for near real-time urban water demand forecasting. Journal of Computational and Applied Mathematics, 309, 532-541. 10.1016/j.cam.2016.02.009.
Breyer, B., & Chang, H. (2014). Urban water consumption and weather variation in the Portland, Oregon metropolitan area. Urban Climate, 9, 1-18. 10.1016/j.uclim.2014.05.001.
Burek, P., Satoh, Y., Fischer, G., Kahil, M.T., Scherzer, A., Tramberend, S., Nava, L.F., Wada, Y., Eisner, S., Flörke, M., Hanasaki, N., Magnuszewski, P., Cosgrove, B., & Wilberg, D. (2016). Water Futures and Solution. International Institute for Applied Systems Analysis, 1–113. http://pure.iiasa.ac.at/13008
Candelieri, A., & Archetti, F. (2014). Identifying typical urban water demand patterns for a reliable short-term forecasting - The icewater project approach. Procedia Engineering, 89, 1004-1012. 10.1016/j.proeng.2015.08.948.
Chen, X., Yang, S.-H., Yang, L., & Chen, X. (2015). A benchmarking model for household water consumption based on adaptive logic networks. Procedia Engineering, 119(1), 1391-1398. 10.1016/j.proeng.2015.08.998.
Darbandsari, P., Kerachian, R., & Malakpour-Estalaki, S. (2017). An Agent-based behavioral simulation model for residential water demand management: The case-study of Tehran, Iran. Simulation Modelling Practice and Theory, 78, 51-72. 10.1016/j.simpat.2017.08.006.
Firat, M., Turan, M. E., & Yurdusev, M. A. (2009). Comparative analysis of fuzzy inference systems for water consumption time. series prediction. Journal of Hydrology, 374(3-4), 235-241. 10.1016/j.jhydrol.2009.06.013.
Firat, M., Turan, M. E., & Yurdusev, M. A. (2010). Comparative analysis of neural network techniques for predicting water consumption time series. Journal of Hydrology, 384 (1-2), 46-51. 10.1016/j.jhydrol.2010.01.005.
Fontanazza, C. M., Notaro, V., Puleo, V., & Freni, G. (2014). Multivariate statistical analysis for water demand modeling. Procedia Engineering, 89, 901-908. 10.1016/j.proeng.2014.11.523
Gato, S., Jayasuriya, N., & Roberts, P. (2007). Temperature and rainfall thresholds for base use urban water demand modelling. Journal of Hydrology, 337 (3-4), 364-376. 10.1016/j.jhydrol.2007.02.014.
Gharabaghi, S., Stahl, E., & Bonakdari, H. (2019). Integrated nonlinear daily water demand forecast model (case study: City of Guelph, Canada). Journal of Hydrology, 579, art. no. 124182. 10.1016/j.jhydrol.2019.124182
González Perea, R., Camacho Poyato, E., Montesinos, P., & Rodríguez Díaz, J.A. (2019). Optimisation of water demand forecasting by artificial intelligence with short data sets. Biosystems Engineering, 177, 59-66. 10.1016/j.biosystemseng.2018.03.011.
Guo, W., Liu, T., Dai, F., & Xu, P. (2020). An improved whale optimization algorithm for forecasting water resources demand. Applied Soft Computing Journal, 86, art. no. 105925. 10.1016/j.asoc.2019.10592.
Gurung, T. R., Stewart, R. A., Beal, C. D., & Sharma, A. K. (2016). Smart meter enabled informatics for economically efficient diversified water supply infrastructure planning. Journal of Cleaner Production, 135, 1023-1033. 10.1016/j.jclepro.2016.07.017.
Herrera, M., Izquierdo, J., Pérez-Garcí, R., & Ayala-Cabrera, D. (2014). On-line learning of predictive kernel models for urban water demand in a smart city. Procedia Engineering, 70, 791-799. 10.1016/j.proeng.2014.02.086.
Herrera, M., Torgo, L., Izquierdo, J., & Pérez-García, R. (2010). Predictive models for forecasting hourly urban water demand. Journal of Hydrology, 387 (1-2), 141-150. 10.1016/j.jhydrol.2010.04.005.
Hutton, C. J., & Kapelan, Z. A. (2015). Probabilistic methodology for quantifying, diagnosing and reducing model structural and predictive errors in short term water demand forecasting. Environmental Modelling and Software, 66, 87-97. 10.1016/j.envsoft.2014.12.021
Hyndman, R., Koehler, A. B., Ord, J. K., & Snyder, R. D. (2008). Forecasting with Exponential Smoothing: The State Space Approach. Springer, Berlin. http://online.kottakkalfarookcollege.edu.in:8001/jspui/bitstream/123456789/3945/1/_ForecastingWithExponentialSmoo.pdf. Access in: July 11, 2022.
Hyndman, R. J., & Athanasopoulos, G. (2013). Forecasting: principles and practice. Second ed. O Texts, Austrália. https://otexts.com/fpp2/
Jain, A., Varshney, A. K., & Joshi, U. C. (2001). Short-term water demand forecast modelling at IIT Kanpur using artificial neural networks. Water Resources Management, 15 (5), 299-321. 10.1023/A
Jain, A., & Ormsbee, L. E. (2002). Short-term water demand forecast modeling techniques - Conventional methods versus AI. Journal American Water Works Association, 94 (7), 64-7. 10.1002/j.1551-8833.2002.tb09507.x
Karamaziotis, P. I., Raptis, A., Nikolopoulos, K., Litsiou, K., & Assimakopoulos, V. (2020). An empirical investigation of water consumption forecasting methods. International Journal of Forecasting, 36(2), 588-606. 10.1016/j.ijforecast.2019.07.009.
Kozlowski, E., Kowalska, B., Kowalski, D., & Mazurkiewicz, D. (2018). Water demand forecasting by trend and harmonic analysis. Archives of Civil and Mechanical Engineering, 18 (1), 140-148. 10.1016/j.acme.2017.05.006.
Liu, J., Savenije, H. H. G., & Xu, J. (2003). Forecast of water demand in Weinan City in China using WDF-ANN model. Physics and Chemistry of the Earth, 28 (4-5), 219-224. 10.1016/S0022-1694(00)00287-0
Lorente-Leyva, L. L. et al. (2019). Artificial Neural Networks for Urban Water Demand Forecasting: A Case Study. Journal of Physics: Conference Series, 1284(1), 0–8. 10.1088/1742-6596/1284/1/012004.
Makki, A. A., Stewart, R. A., Panuwatwanich, K., & Beal, C. (2013). Revealing the determinants of shower water end use consumption: Enabling better targeted urban water conservation strategies. Journal of Cleaner Production, 60, 129-146. 10.1016/j.jclepro.2011.08.007
Makki, A. A., Stewart, R. A., Beal, C. D., & Panuwatwanich, K. (2015). Novel bottom-up urban water demand forecasting model: Revealing the determinants, drivers and predictors of residential indoor end-use consumption. Resources, Conservation and Recycling, 95, 15-37. Resources, Conservation and Recycling, 10.1016/j.resconrec.2014.11.009.
Makridakis, S., Wheelwright, S., & Hyndman, R., (1998). Forecasting: methods and applications. Third ed., John Wiley & Sons, New York.
Matos, L. M. C., & Martinelli, F. J. (1998). Application of machine learning in water distribution networks. Intelligent Data Analysis, 2(4), 311-332. 10.3233/IDA-1998-2405
Mohamed, M. M., & Al-Mualla, A. A. (2010). Water demand forecasting in Umm Al-Quwain using the constant rate model. Desalination, 259 (1-3), 161-168. 10.1016/j.desal.2010.04.014.
Montgomery, D. C., Peck, E. A., & Vining, G. (2012). Introduction to linear regression analysis. Fifth ed. John Wiley & Sons, New Jersey.
Mori, K., & Christodoulou, A. (2012). Review of sustainability indices and indicators: Towards a new City Sustainability Index (CSI). Environmental impact assessment review, 32 (1), 94-106. 10.1016/j.eiar.2011.06.001
Nasseri, M., Moeini, A., & Tabesh, M. (2011). Forecasting monthly urban water demand using Extended Kalman Filter and genetic programming. Expert Systems with Applications, 38(6), 7387-7395. 10.1016/j.eswa.2010.12.087.
Nguyen, K. A., Stewart, R. A., Zhang, H., Sahin, O., & Siriwardene, N. (2018). Re-engineering traditional urban water management practices with smart metering and informatics. Environmental Modelling and Software, 101, 256-267. 10.1016/j.envsoft.2017.12.015
Oyebode, O., Babatunde, D. E., Monyei, C. G., & Babatunde, O. M. (2019). Water demand modelling using evolutionary computation techniques: integrating water equity and justice for realization of the sustainable development goals. Heliyon, 5(11), e02796. 10.1016/j.heliyon.2019.e02796
Pagani, R. N., Kovaleski, J. L., & Resende, L. M. M. (2015). Methodi Ordinatio: a proposed methodology to select and rank relevant scientific papers encompassing the impact factor, number of citation, and year of publication. Scientometrics, 105(3), 2109-2135, 10.1007/s11192-015-1744-x.
Pagani, R. N., Kovaleski, J. L., & Resende, L. M. M. (2017). Avanços na composição da Methodi Ordinatio para revisão sistemática de literatura. Ciência da Informação, 46(2). 10.18225/ci.inf..v47i1.1886.
Pagani, R. N., Pedroso, B., dos Santos, C. B., Picinin, C. T., & Kovaleski, J. L. (2022). Methodi Ordinatio 2.0: revisited under statistical estimation, and presenting FInder and RankIn. Quality & Quantity, 1-40.
Palit, A. K., & Popovic, D. (2005) Computational Intelligence in Time Series Forecasting: Theory and Engineering Applications. Springer, London.
Pesantez, J. E., Berglund, E. Z., & Kaza, N. (2020). Smart meters data for modeling and forecasting water demand at the user-level. (2020). Environmental Modelling and Software, 125, art. no. 104633. 10.1016/j.envsoft.2020.104633.
Pulido-Calvo, I., Montesinos, P., Roldán, J., & Ruiz-Navarro, F. (2007). Linear regressions and neural approaches to water demand forecasting in irrigation districts with telemetry systems. Biosystems Engineering, 97(2), 283-293. 10.1016/j.biosystemseng.2007.03.003.
Qi, C., & Chang, N. (2011). System dynamics modeling for municipal water demand estimation in an urban region under uncertain economic impacts. Journal Of Environmental Management, 92(6), 1628-1641. 10.1016/j.jenvman.2011.01.020.
Rathnayaka, K., Malano, H., Arora, M., George, B., Maheepala, S., & Nawarathna, B. (2017). Prediction of urban residential end-use water demands by integrating known and unknown water demand drivers at multiple scale: Model development. Resources, Conservation and Recycling, 117, 85-92. 10.1016/j.resconrec.2016.11.014.
Russell, R. S, & Taylor, B. W. (2011). Operations management: creating value along the supply chain. Seventh ed. Wiley, New York.
Sanchez, G. M., Terando, A., Smith, J. W., García, A. M., Wagner, C. R., & Meentemeyer, R. K. (2020). Forecasting water demand across a rapidly urbanizing region. The Science of the total environment, 730, 139050. 10.1016/j.scitotenv.2020.13905.
Sebri, M. (2016). Forecasting urban water demand: A meta-regression analysis. Journal of Environmental Management, 183, 777-785. 10.1016/j.jenvman.2016.09.032
Shabani, S., Yousefi, P., & Naser, G. (2017). Support Vector Machines in Urban Water Demand Forecasting Using Phase Space Reconstruction. Procedia Engineering, 186, 537-543. 10.1016/j.proeng.2017.03.267.
Thopil, G. A., & Pouris, A. (2016). A 20 year forecast of water usage in electricity generation for South Africa amidst water scarce conditions. Renewable and Sustainable Energy Reviews, 62, 1106-1121. https://doi.org/10.1016/j.rser.2016.05.003.
Torraco, R. J. (2005). Writing integrative literature reviews: Guidelines and examples. Human resource development review, 4(3), 356-367. 10.1177/1534484305278283
UN - United Nations (2019). Department of Economic and Social Affairs, Population Division. World Urbanization Prospects: The 2018 Revision (ST/ESA/SER.A/420). United Nations. https://population.un.org/wup/Publications/Files/WUP2018-Report.pdf
UNESCO (2019). World Water Assessment Programme (WWAP). The United Nations World Water Development Report 2019: Leaving no one behind. UNESCO, Paris. https://reliefweb.int/sites/reliefweb.int/files/resources/367306eng.pdf
Vijai, P., & Bagavathi S. P. (2016). Design of IoT Systems and Analytics in the Context of Smart City Initiatives in India. Procedia Computer Science, 92, 583–588. 10.1016/j.procs.2016.07.386.
Vijai, P., & Bagavathi, S. P. (2018). Performance comparison of techniques for water demand forecasting. Procedia Computer Science, 143, 258-266. 10.1016/j.procs.2018.10.394.
Walker, D., Creaco, E., Vamvakeridou-Lyroudia, L., Farmani, R., Kapelan, Z., & Savić, D. (2015). Forecasting domestic water consumption from smart meter readings using statistical methods and artificial neural networks. Procedia Engineering, 119(1), 1419-1428. 10.1016/j.proeng.2015.08.1002.
Wang, K., & Davies, E. G. R. (2018). Municipal water planning and management with an end-use based simulation model. Environmental Modelling and Software, 101, 204-217. 10.1016/j.envsoft.2017.12.024.
Yin, Q.-Q., & Fang, G.-H. (2014). Harmoniousness analysis of total amount control of water use. Water Science and Engineering, 7(1), 49-59. 10.3882/j.issn.1674-2370.2014.01.006.
Yousefi, P., Shabani, S., Mohammadi, H., & Naser, G. (2017). Gene Expression Programing in Long Term Water Demand Forecasts Using Wavelet Decomposition. Procedia Engineering, 186, 544-550. 10.1016/j.proeng.2017.03.268.
Yurdusev, M. A., & Firat, M. (2009). Adaptive neuro fuzzy inference system approach for municipal water consumption modeling: An application to Izmir, Turkey. Journal of Hydrology, 365 (3-4), 225-234. 10.1016/j.jhydrol.2008.11.036.
Zeng, Y., Cai, Y., Jia, P., & Jee, H. (2012). Development of a web-based decision support system for supporting integrated water resources management in Daegu city, South Korea. Expert Systems with Applications, 39 (11), 10091-10102. 10.1016/j.eswa.2012.02.065.
Zhou, S. L., Mcmahon, T. A., Walton, A., & Lewis, J. (2000). Forecasting daily urban water demand: A case study of Melbourne. Journal of Hydrology, 236 (3-4), 153-164. 10.1016/S0022-1694(00)00287-0.
Zhou, S. L., Mcmahon, T. A., Walton, A., & Lewis, J. (2002). Forecasting operational demand for an urban water supply zone Journal of Hydrology, 259 (1-4), 189-202. 10.1016/S0022-1694(01)00582-0.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Angélica Duarte Lima; Dayane Regina Trage; Fabiane Florencio de Souza; Regina Negri Pagani
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
1) Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2) Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
3) Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.