A Systematic Literature Review on Machine Learning Techniques for Predicting Household Water Consumption

A Systematic Literature Review on Machine Learning Techniques for Predicting Household Water Consumption Table 1: Primary studies: ID, reference, and publication type (Journal or Conference) ID Reference Type ID Reference Type P01 [Niknam et al., 2023a] Journal P41 [Uzlu, 2024] Journal P02 [Pu et al., 2022] Journal P42 [Gautam et al., 2020] Journal P03 [Michalopoulos et al., 2024] Journal P43 [Dai et al., 2011] Conference P04 [Zubaidi et al., 2020b] Journal P44 [Guo et al., 2022] Journal P05 [Jithish and Sankaran, 2017] Conference P45 [Felfelani and and, 2016] Journal P06 [Zubaidi et al., 2020a] Journal P46 [Velasco et al., 2018] Conference P07 [Sajadifar and Pakseresht, 2024] Journal P47 [Vo et al., 2022] Conference P08 [Nasser et al., 2020] Journal P48 [Niyongabo et al., 2024] Journal P09 [Zheng et al., 2022] Journal P49 [Pakpahan et al., 2023] Conference P10 [Suh and Ham, 2016] Journal P50 [Li et al., 2023] Journal P11 [Rustam et al., 2022] Journal P51 [Zhang and Wang, 2022] Conference P12 [Karamaziotis et al., 2020] Journal P52 [Cao et al., 2024] Journal P13 [Sebri, 2013] Journal P53 [Wang et al., 2023] Journal P14 [Zhou et al., 2024] Journal P54 [Kavya et al., 2023] Journal P15 [Smolak et al., 2020] Journal P55 [Zhang et al., 2022] Journal P16 [Nguyen et al., 2016] Conference P56 [Xenochristou et al., 2021] Journal P17 [Aggarwal and Sehgal, 2022] Conference P57 [Alsumaiei, 2021] Journal P18 [Görenekli and Gülbağ, 2024] Journal P58 [Namdari et al., 2023] Journal P19 [Ghannam and Hussain, 2023] Journal P59 [Thakur et al., 2021] Journal P20 [Deng et al., 2022] Conference P60 [Athapaththu et al., 2020] Conference P21 [Li and Fu, 2024] Journal P61 [Polić et al., 2023] Conference P22 [Zubaidi et al., 2023] Journal P62 [Menapace et al., 2021] Journal P23 [Niknam et al., 2023b] Journal P63 [Candelieri et al., 2019] Journal P24 [Sajjanshetty et al., 2023] Conference P64 [Said et al., 2021] Journal P25 [Pourmousavi et al., 2022] Journal P65 [Shi et al., 2013] Journal P26 [Yin and Xiong, 2024] Journal P66 [Oyebode and Ighravwe, 2019] Journal P27 [El Hanjri et al., 2023] Journal P67 [Yang et al., 2023] Journal P28 [Chatzigeorgakidis et al., 2018] Journal P68 [Kesornsit and Sirisathitkul, 2022] Journal P29 [Cao et al., 2023] Journal P69 [Drevetskyi et al., 2018] Conference P30 [Jiang et al., 2024] Journal P70 [Yan et al., 2022] Conference P31 [Mumbi et al., 2022] Journal P71 [García-Soto et al., 2024] Journal P32 [Cheng et al., 2023] Conference P72 [dos Santos and Pereira Filho, 2014] Journal P33 [Li et al., 2024] Journal P73 [Chang and Liu, 2009] Conference P34 [Iwakin and Moazeni, 2024] Journal P74 [Yurdusev et al., 2009] Journal P35 [Abu Talib et al., 2023] Journal P75 [Faiz and Daniel, 2022] Conference P36 [Bashar et al., 2023] Journal P76 [Compagnon et al., 2022] Conference P37 [Ndayisenga et al., 2022] Conference P77 [Wang et al., 2024] Conference P38 [Filho et al., 2024] Journal P78 [Sahoo et al., 2023] Journal P39 [Santos de Jesus and Silva Gomes, 2023] Journal P79 [Xu, 2024] Journal P40 [Gil-Gamboa et al., 2024] Journal P80 [Kim et al., 2022] Journal References [Abu Talib et al., 2023] Abu Talib, M., Abdallah, M., Abdeljaber, A., and Abu Waraga, O. (2023). Influence of exogenous factors on water demand forecasting models during the COVID-19 period. Engineering Applications of Artificial Intelligence, 117:105617. [Aggarwal and Sehgal, 2022] Aggarwal, S. and Sehgal, S. (2022). Automation of Water Distribution System by Prediction of Water Consumption and Leakage Detection Using Machine Learning and IoT. In 2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence), pages 345–350. [Alsumaiei, 2021] Alsumaiei, A. A. (2021). Short-term forecasting of monthly water consumption in hyper-arid climate using recurrent neural networks. Journal of Engineering Research, 9(3B):56–69. [Athapaththu et al., 2020] Athapaththu, A. M. H. N., Illeperumarachchi, D. U. S., Herath, H. M. K. U., Jayasinghe, H. K., Rankothge, W. H., and Gamage, N. (2020). Supply and Demand Planning for Water: A Sustainable Water Management System. In 2020 2nd International Conference on Advancements in Computing (ICAC), pages 305–310. [Bashar et al., 2023] Bashar, A. M., Nozari, H., Marofi, S., Mohamadi, M., and Ahadiiman, A. (2023). Investigation of factors affecting rural drinking water consumption using intelligent hybrid models. Water Science and Engineering, 16(2):175–183. 1 [Candelieri et al., 2019] Candelieri, A., Giordani, I., Archetti, F., Barkalov, K., Meyerov, I., Polovinkin, A., Sysoyev, A., and Zolotykh, N. (2019). Tuning hyperparameters of a SVM-based water demand forecasting system through parallel global optimization. Computers & Operations Research, 106:202–209. [Cao et al., 2023] Cao, L., Yuan, X., Tian, F., Xu, H., and Su, Z. (2023). Forecasting of water consumption by integrating spatial and temporal characteristics of short-term water use in cities. Physics and Chemistry of the Earth, Parts A/B/C, 130:103390. [Cao et al., 2024] Cao, Y., Wang, Z., Li, P., Zhou, Z., Li, W., Zheng, T., Liu, J., Wu, W., Shi, Z., and Liu, J. (2024). Prediction of rural domestic water and sewage production based on automated machine learning in northern China. Journal of Cleaner Production, 434:140016. [Chang and Liu, 2009] Chang, M. and Liu, J. (2009). Water Demand Prediction Model Based on Radial Basis Function Neural Network. In 2009 First International Conference on Information Science and Engineering, pages 5295–5298. [Chatzigeorgakidis et al., 2018] Chatzigeorgakidis, G., Karagiorgou, S., Athanasiou, S., and Skiadopoulos, S. (2018). FMLkNN: scalable machine learning on Big Data using k-nearest neighbor joins. Journal of Big Data, 5(1):4. [Cheng et al., 2023] Cheng, Y., Qian, Z., and Bao, K. (2023). Hot Water Load Prediction Based on a Combined Model of CNN-GRU and TCN. In 2023 4th International Conference on Advanced Electrical and Energy Systems (AEES), pages 300–304. [Compagnon et al., 2022] Compagnon, P., Lomet, A., Reyboz, M., and Mermillod, M. (2022). Domestic hot water forecasting for individual housing with deep learning. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 223–235. [Dai et al., 2011] Dai, J., Li, M., Sahu, S., Naphade, M., and Chen, F. (2011). Multi-granular demand forecasting in SmarterWater. In Proceedings of the 13th International Conference on Ubiquitous Computing, UbiComp ’11, page 595–596, New York, NY, USA. Association for Computing Machinery. [Deng et al., 2022] Deng, L., Chang, X., and Wang, P. (2022). Daily Water Demand Prediction Driven by Multi-source Data. Procedia Computer Science, 208:128–135. 7th International Conference on Intelligent, Interactive Systems and Applications. [dos Santos and Pereira Filho, 2014] dos Santos, C. C. and Pereira Filho, A. J. (2014). Water Demand Forecasting Model for the Metropolitan Area of São Paulo, Brazil. Water Resources Management, 28(13):4401–4414. [Drevetskyi et al., 2018] Drevetskyi, V., Klepach, M., and Kutia, V. (2018). Water Consumption Prediction for City Pumping Station Using Neural Networks. In Burduk, A. and Mazurkiewicz, D., editors, Intelligent Systems in Production Engineering and Maintenance – ISPEM 2017, pages 459–467, Cham. Springer International Publishing. [El Hanjri et al., 2023] El Hanjri, M., Kabbaj, H., Kobbane, A., and Abouaomar, A. (2023). Federated learning for water consumption forecasting in smart cities. In ICC 2023-IEEE International Conference On Communications, pages 1798– 1803. IEEE, Institute of Electrical and Electronics Engineers Inc. [Faiz and Daniel, 2022] Faiz, M. and Daniel, A. K. (2022). Wireless Sensor Network Based Distribution and Prediction of Water Consumption in Residential Houses Using ANN. In Misra, R., Kesswani, N., Rajarajan, M., Veeravalli, B., and Patel, A., editors, Internet of Things and Connected Technologies, pages 107–116, Cham. Springer International Publishing. [Felfelani and and, 2016] Felfelani, F. and and, R. K. (2016). Municipal water demand forecasting under peculiar fluctuations in population: a case study of Mashhad, a tourist city. Hydrological Sciences Journal, 61(8):1524–1534. [Filho et al., 2024] Filho, J. V., Scortegagna, A., Vieira, A. P. d. S. D., and Jaskowiak, P. A. (2024). Machine learning for water demand forecasting: Case study in a Brazilian coastal city. Water Practice and Technology, 19(5):1586–1602. [García-Soto et al., 2024] García-Soto, C. G., Torres, J. F., Zamora-Izquierdo, M. A., Palma, J., and Troncoso, A. (2024). Water consumption time series forecasting in urban centers using deep neural networks. Applied Water Science, 14(2):21. [Gautam et al., 2020] Gautam, J., Chakrabarti, A., Agarwal, S., Singh, A., Gupta, S., and Singh, J. (2020). Monitoring and forecasting water consumption and detecting leakage using an IoT system. Water Supply, 20(3):1103–1113. [Ghannam and Hussain, 2023] Ghannam, S. and Hussain, F. (2023). Comparison of deep learning approaches for forecasting urban short-term water demand a Greater Sydney Region case study. Knowledge-Based Systems, 275:110660. [Gil-Gamboa et al., 2024] Gil-Gamboa, A., Paneque, P., Trull, O., and Troncoso, A. (2024). Medium-term water consumption forecasting based on deep neural networks. Expert Systems with Applications, 247:123234. 2 [Guo et al., 2022] Guo, J., Sun, H., and Du, B. (2022). Multivariable Time Series Forecasting for Urban Water Demand Based on Temporal Convolutional Network Combining Random Forest Feature Selection and Discrete Wavelet Transform. Water Resources Management, 36(9):3385–3400. [Görenekli and Gülbağ, 2024] Görenekli, K. and Gülbağ, A. (2024). Comparative Analysis of Machine Learning Techniques for Water Consumption Prediction: A Case Study from Kocaeli Province. Sensors, 24(17). [Iwakin and Moazeni, 2024] Iwakin, O. and Moazeni, F. (2024). Improving urban water demand forecast using conformal prediction-based hybrid machine learning models. Journal of Water Process Engineering, 58:104721. [Jiang et al., 2024] Jiang, Q., Guo, W., Wang, Z., Wu, Y., Zhao, Y., Tao, M., and Sun, Y. (2024). Forecasting regional water demand using multi-fidelity data and harris hawks optimization of generalized regression neural network models – A case study of Heilongjiang Province, China. Journal of Hydrology, 634:131084. [Jithish and Sankaran, 2017] Jithish, J. and Sankaran, S. (2017). A Neuro-Fuzzy Approach for Domestic Water Usage Prediction. In 2017 IEEE Region 10 Symposium (TENSYMP), pages 1–5. [Karamaziotis et al., 2020] Karamaziotis, P. I., Raptis, A., Nikolopoulos, K., Litsiou, K., and Assimakopoulos, V. (2020). An empirical investigation of water consumption forecasting methods. International Journal of Forecasting, 36:588–606. [Kavya et al., 2023] Kavya, M., Mathew, A., Shekar, P. R., and P, S. (2023). Short term water demand forecast modelling using artificial intelligence for smart water management. Sustainable Cities and Society, 95:104610. [Kesornsit and Sirisathitkul, 2022] Kesornsit, W. and Sirisathitkul, Y. (2022). Water consumption prediction based on machine learning methods and public data. Advances in Computational Design, 7(2):113–128. [Kim et al., 2022] Kim, J., Lee, H., Lee, M., Han, H., Kim, D., and Kim, H. S. (2022). Development of a deep learning-based prediction model for water consumption at the household level. Water, 14(9):1512. [Li and Fu, 2024] Li, D. and Fu, Q. (2024). Deep Learning Model-Based Demand Forecasting for Secondary Water Supply in Residential Communities: A Case Study of Shanghai City, China. IEEE Access, 12:38745–38757. [Li et al., 2023] Li, Z., Peng, S., Zheng, G., Chu, X., and Tian, Y. (2023). Prediction of Daily Water Consumption in Residential Areas Based on Meteorologic Conditions—Applying Gradient Boosting Regression Tree Algorithm. Water, 15(19). [Li et al., 2024] Li, Z., Wang, G., Lin, D., and Mashhadi, A. (2024). Hybrid approach for accurate water demand prediction using socio-economic and climatic factors with ELM optimization. Heliyon, 10(3):e25028. [Menapace et al., 2021] Menapace, A., Zanfei, A., and Righetti, M. (2021). Tuning ANN Hyperparameters for Forecasting Drinking Water Demand. Applied Sciences, 11(9). [Michalopoulos et al., 2024] Michalopoulos, C., Dimas, P., Kossieris, P., Pelekanos, N., and Makropoulos, C. (2024). A largescale evaluation of machine learning algorithms in mid-term water demand forecasting. Water Practice and Technology, 19(7):2693–2711. [Mumbi et al., 2022] Mumbi, A. W., Li, F., Bavumiragira, J. P., and Fangninou, F. F. (2022). Forecasting water consumption on transboundary water resources for water resource management using the feed-forward neural network: a case study of the Nile River in Egypt and Kenya. Marine and Freshwater Research, 73(3):292–306. [Namdari et al., 2023] Namdari, H., Haghighi, A., and Ashrafi, S. M. (2023). Short-term urban water demand forecasting; application of 1D convolutional neural network (1D CNN) in comparison with different deep learning schemes. Stochastic Environmental Research and Risk Assessment. [Nasser et al., 2020] Nasser, A. A., Rashad, M. Z., and Hussein, S. E. (2020). A Two-Layer Water Demand Prediction System in Urban Areas Based on Micro-Services and LSTM Neural Networks. IEEE Access, 8:147647–147661. [Ndayisenga et al., 2022] Ndayisenga, G., Gatera, O., Kabiri, C., Niyitegeka, J., Bampire, D., and Harerimana, S. (2022). IoT-Based Household Water Consumption Management System. In 2022 IEEE PES/IAS PowerAfrica, pages 1–4. [Nguyen et al., 2016] Nguyen, K., Sahin, O., Stewart, R., and Zhang, H. (2016). AUTOFLOW©- A novel application for water resource management and climate change response using smart technology. 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