Short-term water demand forecasting: a review

Figures & data

Table 1. Search category and keywords.

Search category	Keywords
Short-term forecasting of water consumption.	(‘short-term forecast*’ OR ‘short-term predict*’)AND (‘water consumpt*’ OR ‘water demand*’)

Figure 1. Article selection process.

Figure 2. Article classification process.

Table 2. Relevant articles on neural network-based methods and publication information.

ID	Study title	Author(s)/Year	Journal/conference name	Contributions	Techniques/methods used
N1	ANN-based residential water end-use demand forecasting model	Bennett et al. (2013)	Expert Systems with Applications	Developed water end-use demand forecasting models and identified the key determinants for a residential end-use forecasting model.	Feedforward backpropagation net- works one radial basis function net- work, Sigmoid activation, linear activation, linear regression analysis.
N2	Modeling and Forecasting Short-Term Water Demand Reported by Smart Meters	Pesantez et al. (2018)	WDSA-CCWI2018 Joint Conference	Developed models to predict six- hour ahead water demand for in- dividual accounts.	Three-layer feedforward neural net- works, regression tree, time clustering, k-means algorithm.
N3	Using extreme learning machines for short-term urban water demand forecasting	Mouatadid and Adamowski (2017)	Urban Water Journal	Presented various machine learning methods to explore the most reliable and accurate method to predict daily urban water consumption 1- and 3-days ahead.	Multi-layer perceptron feedforward, Levenberg Marquardt, backpropagation algorithm, support vector regression, extreme learning machine, multiple linear regression models.
N4	Bayesian regularized NAR neural network basedshort-term prediction method of water consumption	Liu et al. (2019)	E3S Web of Conferences	Implemented a predictive model for short-term water consumption time series, specifically forecasting five hours ahead.	Bayesian regularization algorithm and nonlinear autoregressive method, standard backpropagation NN, Bayesian regularized backpropagation, min-max standardization method.
N5	Short-term forecasting ofhourlywater demands–A Portuguese case study	Coelho and Andrade- Campos (2019)	International Journal of Water	A comparison study to evaluate different developed models to forecast hourly water demand 1 and 24 hours ahead in Portugal.	Pearson correlation coefficient analysis, exponential smoothing, naïve models, feedforward neural networks.
N6	Performance comparison of techniques for water demand forecasting	Vijai and Sivaku-mar (2018)	Procedia Computer Science	Different machine learning methods were utilized to assess the accuracy of predicting water demand at different interval.	Deep neural network, extreme learning machines, a simple feedforward neural network, random forest, Gaussian process regression, multiple regression, least-squares support vector ma- chine.
N7	Short-term water demand forecast based on deep learning method	Guo et al. (2018)	Journal of Water Resources Planning and Management	The authors developed models to predict water demand at two different time intervals: 15 minutes and one day.	GRUN, feedforward propagation and backpropagation, dense layer weight matrix, seasonal autoregressive integrated moving average.
N8	Hourly and Daily Urban Water Demand Predictions Using a LongShort-Term Memory Based Model	Mu et al. (2020)	Journal of Water Resources Planning and Management	LSTM technique was employed to develop a model to forecast 1 hour and 24 hour ahead urban water demand in Hefei, China. The effectiveness of the proposed model was evaluated by comparing its performance with several other forecasting models.	LSTM, autoregressive integrated moving average model, support vector regression model, random forest model, sensitivity analysis.
N9	Daily Urban Water Demand Forecasting Based on Chaotic Theory and Continuous Deep Belief Neural Network.	Xu et al. (2019)	Water (Switzerland).	Constructed a novel continuous deep belief neural network forecasting model for daily urban water demand time series prediction in Zhuzhou, China.	Continuous deep belief neural network, RBM, backpropagation, chaotic theory, phase -space reconstruction method, C–C method, multilayer FFNN, power spectrum, largest Lyapunov exponent, support vector regression, GRNN.
N10	Hourly Urban Water Demand Forecasting Using the Continuous Deep Belief Echo State Network.	Xu Y et al. (2019)	Water	A novel deep (CDBESN) was proposed to forecast hourly urban water demand in Zhuzhou, China.	A continuous deep belief network, echo state network, support vector regression models, sigmoid activation function
N11	Short-Term Water Demand Forecasting Using Nonlinear Autoregressive Artificial Neural Networks.	M. H. Bata et al. (2020)	Journal of Water Re- sources Planning and Management.	The authors suggested the use of artificial neural network (ANN) models to forecast water demand in Ontario, Canada, for both 24-hour and one-week periods ahead.	Nonlinear autoregressive, seasonal autoregressive integrated moving average, nonlinear autoregressive with exogenous.
N12	Deep learning with long short-term memory neuralnetworks combining wavelet transform and principal component analysis for daily urban water demand forecasting.	Du et al. (2021)	Expert Systems with Applications.	A hybrid model was proposed to predict daily urban water demand in Suzhou, China.	Discrete wavelet transform, principal component analysis, LSTM.
N13	Application of LSTM Networks for Water Demand Prediction in Optimal Pump Control	Kühnert et al. (2021)	Water	The study investigated the application of LSTM models for predicting water demand one day ahead. The authors conducted a comparative analysis of various approaches to evaluate the models’ performance.	Naïve method, vector autoregressive and LSTM.

Table 3. Relevant articles on regression-based methods and publication information.

ID	Study title	Author(s)/Year	Journal/conference name	Contributions	Techniques/methods used
R1	Predictive models for forecasting hourly urban water demand.	Herrera et al. (2010)	Journal of Hydrology	Investigated various predictive machine learning models to predict hourly urban water demand using non-linear time series data.	ANN (one hidden layer feedforward neural network and the backpropagation algorithm). Random forest, multivariate adaptive regression splines, support vector regression model and projection pursuit regression.
R2	Clustering and Support Vector Regression for Water Demand Fore- casting and Anomaly Detection.	Candelieri (2017)	Water (Switzerland)	A model was proposed to predict water demand on an hourly basis, considering two distinct data levels: the aggregated and the individual.	Time series clustering, k-means algorithm, support vector machine regression, mapping function.
R3	Novel bottom-up urban water demand forecasting model: Revealing the determinants, drivers and predictors of residentialindoor end-use consumption.	Makki et al. (2015)	Resources, Conservation and Recycling	To predict daily water demand and explore the key determinants of indoor water consumption at the end-use level.	Regression analysis, statistical techniques such as bootstrapping cluster analysis dummy coding, Pearson’s chi- squared test.
R4	A Forecasting Frame- work Based on Kalman Filter Integrated Multivariate Local Polynomial Regression: Application to Urban Water Demand.	Chenet al. (2019)	Neural Processing Letters	he study utilized the extended Kalman filter technique to predict daily water demand.	Kalman filter technique, MLPR, wavelet transformation, RVR, phase space reconstruction, c-c method.
R5	Factors influencing residential water consumption in Wallonia, Belgium.	Bich-Ngoc et al. (2022)	Utilities Policy	Study investigated the possible variables that may impact urban water demand in Wallonia, Belgium using regression analysis.	Fixed effects regression with spatial predictors and mixed-effects regression with spatial random intercepts.
R6	The Assessment of Climate Variables and Geographical Distribution on Residential Drinking Water Demand in Ethiopia.	Timotewos et al. (2022)	Water	In Ethiopia, a research investigation was conducted to examine the impact of weather variables on urban water demand.	Principal component analysis. Multiple linear regression.
R7	Improving short- term water demand forecasting using evolutionary algorithms.	Stańczyket al. (2022)	Scientific Reports	A technique that combines a linear regression model and evolutionary algorithms was proposed to forecast water demand in Wroclaw, Poland.	Evolutionary algorithms, support vector regression, linear regression, regression trees, multilayer perceptron.

Table 4. Relevant articles on stochastic-based methods and publication information.

ID	Study title	Author(s)/Year	Journal/conference name	Contributions	Techniques/methods used
S1	Predictive Classification of WaterConsumption TimeSeriesusing Non-homogeneous Markov Models.	Abadiet al. (2017)	IEEE	A probabilistic approach was proposed using non-homogeneous Markov models to cluster the patterns of daily water consumption behavior into distinct groups. The goal was to forecast daily consumption behaviors for each group.	k-means algorithm, non-homogeneous Markov models, state independent model homogeneous Markov log normalize.
S2	Simulating Residential Water Demand with a Stochastic End-Use Model.	Blokker et al. (2010)	Journal of Water Resources Planning andManagement.	A stochastic end-use model was developed to forecast water demand within a short time horizon and at a small residential scale.	Statistical information about users and end uses, Poisson rectangular pulse.
S3	Prediction of urban residential end-use water demands by integrating known and unknown water demand drivers at multiple scales II: Model application and validation.	Rathnayaka et al. (2017)	Resources, Conservation and Recycling	A stochastic model was proposed to predict water demand for residential areas across various spatial and temporal scales.	A stochastic approach, negative binomial and Poisson probability distributions.
S4	A Probabilistic Short- Term Water Demand Forecasting Model Based on the Markov Chain Francesca.	Gagliardi et al. (2017)	Water	A method was devised to estimate the probabilities of future water demand over a predefined interval up to six hours in advance.	Homogeneous and non-homogeneous Markov chain, multi-layer perceptron ANN, naïve method.

Table 5. Relevant articles on hybrid-based methods and publication information.

ID	Study title	Author(s)/Year	Journal/conference name	Contributions	Techniques/methods used
H1	Increased performance in the short-term water demand forecasting through the use of a parallel adaptive weighting strategy.	Sardinha- Lourenço et al. (2018)	Journal of Hydrology	Proposed a methodology to fore- cast potable water demand for the next 48 hours with a 1h time step.	Parallel adaptive weighting strategy using univariate time series. Short- term forecast heuristic model, and autoregressive integrated moving average model.
H2	Urban Residential WaterDemand PredictionBased on Artificial Neural Networks and Time Series Models.	Al- Zahrani and Abo- Monasar (2015)	Water ResourcesManagement.	Developed a model to forecast daily water demand in Al- Khobar, Saudi Arabia.	GRNN and moving average and autoregressive techniques.
H3	Short-term water demand forecasting using hybrid supervised and unsupervised machine learning model.	M. Bata et al. (2020)	Smart Water	explored the performance of short-term water demand forecasting models and proposed a hybrid model to forecast water demand 1 hour, 8 hours, 24 hours, and 1 week ahead.	Self-organizing map models, regression tree, seasonal autoregressive integrated moving average, Pearson correlation coefficient.
H4	Short-Term Urban WaterDemand Prediction Considering Weather Factors.	Zubaidi et al. (2018)	Water Resources Management	The purpose of this study was to investigate the impact of weather factors on water demand. The authors proposed two hybrid models to predict daily urban water demand.	Data pre-processing, correlation matrix, autocorrelation and variance inflation factor gravitational search algorithm, backtracking search algorithm, artificial backpropagation neural network, Levenberg-Marquardt.
H5	Hybrid Model for Short-Term Water Demand Forecasting Based on Error CorrectionUsing Chaotic Time Series.	Wu et al. (2020)	Water	A hybrid model was suggested to forecast water demand one day in advance with 15-minute time intervals. The model relied on water consumption data and calendar information.	Least-squares support vector machine, chaotic time series, Fourier series, moving average and autoregressive.
H6	Hybrid Water Demand Forecasting Model Associating Artificial Neural Network with Fourier Series.	Odan and Reis (2012)	Journal of Water Re- sources Planning and Management	The authors used ANN methods to forecast hourly water demand for Araraquara, Brazil.	Correlation analysis, ANN, MLP-BP, DAN2, Fourier series.
H7	Urban water demand forecasting and uncertainty assessment using ensemble wavelet-bootstrap- neural network models.	Tiwari andAdamowski (2013)	Water Resources Research	A hybrid wavelet, bootstrap-based neural network model was developed to forecast short-term urban water demand for daily, weekly, and monthly lead times.	Autocorrelation, cross- correlation, Levenberg-Marquardt, wavelet analysis, bootstrap technique, multilayer feedforward NN.
H8	An ensemble wavelet bootstrap machine learning approach to water demand forecasting: a case study in the city of Calgary, Canada.	Tiwari and Adamowski (2017)	Urban Water Journal	A hybrid wavelet, bootstrap-based neural network was developed to forecast urban water demand for 1 day, 3 day and 5 day lead times.	Autocorrelation, cross- correlation, Levenberg-Marquardt, wavelet analysis, bootstrap technique, multilayer feedforward NN.
H9	Performance of combined double seasonal univariate time series models for forecasting water demand.	Caiado (2010)	Journal of Hydrologic Engineering	To investigate the prediction ac- curacy of various individual and combined univariate time series models to predict daily water consumption in Spain with multi-step (i.e. 1 to 7 days) ahead fore- casting.	HoltWinters, moving average, autoregressivegeneralized autoregressive conditional heteroskedasticity, recursive technique.
H10	Urban Water Demand Modeling Using Machine Learning Techniques: Case Study of Fortaleza, Brazil.	Nunes Carvalho et al. (2021)	Journal of Water Re- sources Planning and Management	To analyze water demand patterns and to predict water demand in Fortaleza, Brazil.	Random forest, self-organizing map and ANN.
H11	An ensemble stacked model with bias correction for improved water demand forecasting.	Xenochristo and Kapelan (2020)	Urban Water Journal	Proposed a stack model with a bias correction technique to improve daily water demand forecasting in UK.	Random Forests, ANNs, deep neural networks, extreme gradient boosting, generalized linear models and gradient boosting machines.
H12	Interval forecasting for urban water demand using PSO optimized KDE distribution and LSTM neural networks.	Du et al. (2022)	Applied Soft Computing	The work investigated the efficiency of two techniques - particle swarm optimization algorithm optimized kernel density estimation distribution, and a LSTM for interval forecasting of urban water demand.	Particle swarm optimization, optimized kernel density estimation, LSTM
H13	IA Hybrid Framework for Multivariate Time Series Forecasting of DailyUrbanWater DemandUsing Attention-Based Convolutional Neural NetworkandLong Short-TermMemory Network.	Zhouet al. (2022)	Sustainability	The research paper presented a hybrid framework for predicting daily urban water demand in Suzhou, China.	CNN, LSTM, encoder decoder, attention mechanism, Pearson correlation co- efficient.

Table 6. Analysis criteria to define gaps.

ID	Model validation	Multiple input	Real DB	Multi horizon	Multi periodicity	Bench- mark	Behavioural changes	Evaluation indexes ≥ 3	Forecast urban water demand
N1	1	1	1	0	0	1	0	1	0
N2	1	1	1	0	0	1	1	0	0
N3	1	1	1	1	0	1	0	0	1
N4	1	0	1	0	0	1	0	1	1
N5	1	1	1	1	0	1	0	1	1
N6	0	1	0	1	0	1	0	1	1
N7	1	0	1	1	0	1	0	1	1
N8	1	1	1	1	1	1	0	1	1
N9	1	0	1	0	0	1	0	1	1
N10	1	0	1	0	0	1	0	1	1
N11	1	1	1	1	0	1	0	1	1
N12	0	1	1	0	0	1	1	1	1
N13	1	1	1	0	0	1	1	0	1
R1	1	1	1	0	0	1	0	1	1
R2	1	0	1	0	0	0	1	0	1
R3	1	1	1	0	0	0	1	0	0
R4	1	0	1	0	0	1	0	1	1
R5	1	1	0	0	0	0	1	0	0
R6	0	1	1	0	0	0	1	0	0
R7	1	1	1	0	0	1	1	0	1
S1	0	1	1	0	0	1	1	1	0
S2	1	1	0	1	0	0	0	1	1
S3	1	1	0	1	0	0	1	0	1
S4	1	0	1	0	0	1	1	0	1
H1	1	0	1	0	0	1	1	0	1
H2	1	1	1	0	0	1	0	0	1
H3	1	0	1	1	0	1	0	0	1
H4	1	1	1	0	1	0	0	1	1
H5	1	0	1	0	0	0	0	1	1
H6	1	1	1	0	0	1	0	0	1
H7	1	1	1	1	1	1	0	1	1
H8	1	1	1	1	0	1	0	1	1
H9	1	0	0	1	0	1	1	0	1
H10	1	1	1	0	0	0	1	1	1
H11	1	1	1	0	0	1	1	1	1
H12	1	1	1	1	0	1	0	1	1
H13	1	1	1	1	0	1	0	1	1

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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., 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

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

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

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

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

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

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

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

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

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