An Ensemble-Based Approach for Water Demand Prediction: Comparative Analysis of Conventional, Machine Learning, and Deep Learning Techniques

Elham Pashaei; Doğukan Sürücü; Elif Sakal; Ayşe Nur Korkmaz

doi:10.47000/tjmcs.1723524

An Ensemble-Based Approach for Water Demand Prediction: Comparative Analysis of Conventional, Machine Learning, and Deep Learning Techniques

Abstract

Water demand prediction is vital for effective infrastructure planning and sustainable water resource management. Accurate forecasting enables better decision-making in resource allocation and conservation efforts, ensuring equitable access to water. While traditional and machine learning approaches have shown promise in this domain, there is a growing need for methods that combine the strengths of these techniques to achieve higher predictive accuracy and adaptability. In this study, we propose a novel Ensemble Learning method that integrates Seasonal Autoregressive Integrated Moving-Average with Exogenous Regressors (SARIMAX), Support Vector Regression (SVR), and Artificial Neural Networks (ANN) using a stacking approach. This method is specifically designed to leverage the complementary strengths of statistical, machine learning, and deep learning models for improved water demand prediction. We compare the proposed method against 14 other predictive models, including classical approaches like Autoregressive (AR), ARIMA, and SARIMAX, as well as advanced machine learning and deep learning models such as Random Forest (RF), Extreme Gradient Boosting (XGBoost), LightGBM, Categorical Boosting (CatBoost), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM). The models are evaluated on a real-world dataset of monthly water consumption from Istanbul municipality spanning 2011–2023. Performance is assessed using multiple metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). Results demonstrate that our proposed Ensemble Learning method consistently outperforms the other models, showcasing its superior predictive accuracy and robustness for water demand forecasting.

Keywords

Project Number

This research received support from the "2209-A University Students Research Projects Support Program" administered by the TÜBİTAK Scientist Support Programs Directorate (BİDEB)

References

Adamowski, J., Karapataki, C., Comparison of Multivariate Regression and Artificial Neural Networks for Peak Urban Water-Demand Forecasting: Evaluation of Different ANN Learning Algorithms, J. Hydrologic Eng., 15(2010), 729–743.
Abdulsahib Al-Qazzaz-L, Z.A., Pasa, Y., Pashaei, E., Evaluation of Ensemble Learning Methods for Flood Prediction, in: 33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025, Sile, Istanbul, 2025.
Al-Zahrani, M.A., Abo-Monasar, A., Urban residential water demand prediction based on artificial neural networks and time series models, Water Resour. Manage., 29(2015), 3651–3662.
Anele, A.O., Hamam, Y., Abu-Mahfouz, A.M., Todini, E., Overview, Comparative Assessment and Recommendations of Forecasting Models for Short-Term Water Demand Prediction, Water, 9(2017), 887.
Antunes, A., Andrade-Campos, A., Sardinha-Lourenc¸o, A., Oliveira, M.S., Short-term water demand forecasting using machine learning techniques, J. Hydroinformatics, 20(2018), 1343–1366.
Bai, Y., Wang, P., Li, C., Xie, J., Wang, Y., Dynamic Forecast of Daily Urban Water Consumption Using a Variable-Structure Support Vector Regression Model, J. Water Resour. Plann. Manage., 141(2014), 04014058.
Bakker, M., Van Duist, H., Van Schagen, K., Vreeburg, J., Rietveld, L., Improving the Performance of Water Demand Forecasting Models by Using Weather Input, Procedia Eng., 70(2014), 93–102.
Banda, P., Bhuiyan, M., Zhang, K., Song, A., Multivariate Monthly Water Demand Prediction Using Ensemble and Gradient Boosting Machine Learning Techniques, Proc. Int. Conf. Evolving Cities (ICEC), (2021).

Bennett, C., Stewart, R.A., Beal, C.D., ANN-based residential water end-use demand forecasting model, Expert Syst. Appl., 40(2013), 1014–1023.
Brentan, B.M., Luvizotto, E., Herrera, M., Izquierdo, J., P´erez-Garc´ıa, R., Hybrid regression model for near real-time urban water demand forecasting, J. Comput. Appl. Math., 309(2017), 532–541.
Candelieri, A., Clustering and Support Vector Regression for Water Demand Forecasting and Anomaly Detection, Water, 9(2017), 224.
Capt, T., Mirchi, A., Kumar, S.,Walker,W.S., Urban Water Demand: Statistical Optimization Approach to Modeling Daily Demand, J.Water Resour. Plann. Manage., 147(2021).
Nunes Carvalho, T.M., de Assis de Souza Filho, F., Costa Porto, V., Urban Water Demand Modeling Using Machine Learning Techniques: Case Study of Fortaleza, Brazil, J. Water Resour. Plan. Manag., 147(1)(2021).
Chen, G., Long, T., Xiong, J., Bai, Y., Multiple Random Forests Modelling for UrbanWater Consumption Forecasting,Water Resour. Manage., 31(2017), 4715–4729.
Chen, L., Yan, H., Yan, J.,Wang, J., Tao, T. et al., Short-term water demand forecast based on automatic feature extraction by one-dimensional convolution, J. Hydrol., 606(2022), 127440.
Cuceloglu, G., Abbaspour, K.C., Ozturk, I., Assessing the water-resources potential of Istanbul by using a soil and water assessment tool (SWAT) hydrological model, Water, 9(2017), 814.
Deng, L., Chang, X., Wang, P., Daily Water Demand Prediction Driven by Multi-source Data, Procedia Comput. Sci., 208(2022), 128–135.
Du, B., Huang, S., Guo, J., Tang, H.,Wang, L., Zhou, S., Interval forecasting for urban water demand using PSO optimized KDE distributionand LSTM neural networks, Appl. Soft Comput., 122(2022), 108875.
Du, B., Zhou, Q., Guo, J., Guo, S., Wang, L., Deep learning with long short-term memory neural networks combining wavelet transform and principal component analysis for daily urban water demand forecasting, Expert Syst. Appl., 171(2021), 114571.
Du, H., Zhao, Z., Xue, H., ARIMA-M: A New Model for Daily Water Consumption Prediction Based on the Autoregressive Integrated Moving Average Model and the Markov Chain Error Correction, Water, 12(2020), 760.
Fiorillo, D., Kapelan, Z., Xenochristou, M., De Paola, F., Giugni, M., Assessing the Impact of Climate Change on Future Water Demand using Weather Data, Water Resour. Manage., 35(2021), 1449–1462.
Guo, J., Sun, H., Du, B., Multivariable Time Series Forecasting for Urban Water Demand Based on Temporal Convolutional Network Combining Random Forest Feature Selection and Discrete Wavelet Transform, Water Resour. Manage., 36(2022), 3385–3400.
Guo, W., Liu, T., Dai, F., Xu, P., An improved whale optimization algorithm for forecasting water resources demand, Appl. Soft Comput. J., 86(2020), 105925.
Hu, P., Tong, J.,Wang, J., Yang, Y., De Oliveira Turci, L., A Hybrid Model Based on CNN and Bi-LSTM for Urban Water Demand Prediction, Proc. IEEE Congr. Evol. Comput. (CEC), (2019), 1088–1094.
Hu, S., Gao, J., Zhong, D., Deng, L., Ou, C. et al., An innovative hourly water demand forecasting preprocessing framework with local outlier correction and adaptive decomposition techniques, Water, 13(5)(2021), 582.
Huang, H., Lin, Z., Liu, S., Zhang, Z., A neural network approach for short-term water demand forecasting based on a sparse autoencoder, J. Hydroinform., 25(1)(2023), 70–84.
Huang, H., Wu, M., Short-term water demand prediction based on decomposition technique optimization and a multihead attention mechanism, J. Hydroinform., (2024).
Huang, H., Zhang, Z., Song, F., An Ensemble-Learning-Based Method for Short-Term Water Demand Forecasting, Water Resour. Manage., 35(6)(2021), 1757–1773.
Istanbul Metropolitan Municipality (IMM), Precipitation and Daily Consumption Data in Istanbul Dams, 2022, https://data.ibb.gov.tr/en/dataset/istanbul-barajlarina-dusen-gunluk-toplam-yagis-miktari/resource/762b802e-c5f9-4175-a5c1-78b892d9764b.
Iwakin, O., Moazeni, F., Improving urban water demand forecast using conformal prediction-based hybrid machine learning models, J.Water Process Eng., 58(2024), 104721.
Kang, H.S., Kim, H., Lee, J., Lee, I., Kwak, B.Y. et al., Optimization of pumping schedule based on water demand forecasting using a combined model of autoregressive integrated moving average and exponential smoothing, Water Supply, 15(1)(2015), 188–195.
Kavya, M., Mathew, A., Shekar, P.R., Sarwesh, P., Short term water demand forecast modelling using artificial intelligence for smart water management, Sustain. Cities Soc., 95(2023), 104610.
Kim, D., Choi, S., Kang, S., Noh, H., A Study on Developing an AI-Based Water Demand Prediction and Classification Model for Gurye Intake Station, Water, 15(23)(2023), 4160.
Kofinas, D., Mellios, N., Papageorgiou, E., Laspidou, C., Urban Water Demand Forecasting for the Island of Skiathos, Procedia Eng., 89(2014), 1023–1030.
Kühnert, C., Gonuguntla, N.M., Krieg, H., Nowak, D., Thomas, J.A., Application of LSTM Networks for Water Demand Prediction in Optimal Pump Control, Water, 13(5)(2021), 644.
Li, W., Wang, X., Analysis and prediction of urban household water demand with uncertain time series, Soft Comput., 2023.
Li, Z., Peng, S., Zheng, G., Chu, X., Tian, Y., Prediction of Daily Water Consumption in Residential Areas Based on Meteorologic Conditions— Applying Gradient Boosting Regression Tree Algorithm, Water, 15(19)(2023), 3455.
Li, Z., Wang, G., Lin, D., Mashhadi, A., Hybrid approach for accurate water demand prediction using socio-economic and climatic factors with ELM optimization, Heliyon, 10(3)(2024), e25028.
Liu, C., Liu, Z., Yuan, J., Wang, D., Liu, X., Urban Water Demand Prediction Based on Attention Mechanism Graph Convolutional Network- Long Short-Term Memory, Water, 16(6)(2024), 831.
Liu, G., Savic, D., Fu, G., Short-term water demand forecasting using data-centric machine learning approaches, J. Hydroinform., 25(3)(2023), 895–911.
Liu, J., Zhou, X.L., Zhang, L.Q., Xu, Y.P., Forecasting Short-term Water Demands with an Ensemble Deep Learning Model for a Water Supply System, Water Resour. Manag., 37(8)(2023), 2991–3012.
Liu, X., Sang, X., Chang, J., Zheng, Y., Multi-Model Coupling Water Demand Prediction Optimization Method for Megacities Based on Time Series Decomposition, Water Resour. Manag., 35(12)(2021), 4021–4041.
Liu, X., Zhang, Y., Zhang, Q., Comparison of EEMD-ARIMA, EEMD-BP and EEMD-SVM algorithms for predicting the hourly urban water consumption, J. Hydroinform., 24(3)(2022), 535–558.
Liu, Y., Gui, D., Chen, X., Liu, Q., Zeng, F., Sap flow characteristics and water demand prediction of cash crop in hyper-arid areas, Agric. Water Manag., 295(2024), 108767.
Ma, J., Chen, Y., Hao, X., Cui, B., Yang, J., Study on Real-Time Water Demand Prediction of Winter Wheat–Summer Corn Based on Convolutional Neural Network–Informer Combined Modeling, Sustainability, 16(9)(2024).
Mallick, J., Alqadhi, S., Hang, H.T., Alsubih, M., Interpreting optimised data-driven solution with explainable artificial intelligence (XAI) for water quality assessment for better decision-making in pollution management, Environ. Sci. Pollut. Res., 31(30)(2024), 42948–42969.
Mouatadid, S., Adamowski, J., Using extreme learning machines for short-term urban water demand forecasting, UrbanWater J., 14(6)(2017), 630–638.
Namdari, H., Ashrafi, S.M., Haghighi, A., Deep learning-based short-term water demand forecasting in urban areas: A hybrid multichannel model, Aqua Water Infrastruct. Ecosyst. Soc., 73(3)(2024), 380–395.
Namdari, H., Haghighi, A., Ashrafi, S.M., Short-term urban water demand forecasting; application of 1D convolutional neural network (1D CNN) in comparison with different deep learning schemes, Stoch. Environ. Res. Risk Assess., 2023, 1–16.
Nasser, A.A., Rashad, M.Z., Hussein, S.E., A Two-Layer Water Demand Prediction System in Urban Areas Based on Micro-Services and LSTM Neural Networks, IEEE Access, 8(2020), 147647–147661.
Nazari, B., Keshavarz, M., Water population density: Global and regional analysis, Theor. Appl. Climatol., 153(1-2)(2023), 431–445.
Niknam, A., Khademi Zare, H., Hosseininasab, H., Mostafaeipour, A., Developing an LSTM model to forecast the monthly water consumption according to the effects of the climatic factors in Yazd, Iran, J. Eng. Res., 11(1)(2023), 100028.
Niknam, A., Khademi Zare, H., Hosseininasab, H., Mostafaeipour, A., Herrera, M., A Critical Review of Short-Term Water Demand Forecasting Tools—What Method Should I Use?, Sustainability, 14(9)(2022), 5412.
Oliveira, P.J., Steffen, J.L., Cheung, P., Parameter Estimation of Seasonal Arima Models for Water Demand Forecasting Using the Harmony Search Algorithm, Procedia Eng., 186(2017), 177–185.
Oliveira, P.J.A., Boccelli, D.L., k-Nearest Neighbor for Short-Term Water Demand Forecasting, Proc. World Environ. Water Resour. Congr. 2017, 501–510, ASCE, 2017.
Ou, Z., He, F., Zhu, Y., Lu, P., Wang, L., Analysis of driving factors of water demand based on explainable artificial intelligence, J. Hydrol. Reg. Stud., 47(2023).
Ozturk, O.F., Pashaei, E., Konuşmalardaki duygunun evrişimsel LSTM modeli ile tespiti, Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 12(4)(2021), 581–589.
Pandey, P., Bokde, N.D., Dongre, S., Gupta, R., Hybrid Models for Water Demand Forecasting, J. Water Resour. Plan. Manag., 147(2)(2020), 04020106.
Pashaei, E., Ozen, M., Aydin, N., Random Forest in Splice Site Prediction of Human Genome, in: XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016, Springer, Cham, 2016, pp. 518–523.
Pashaei, E., Ozen, M., Aydin, N., Splice sites prediction of human genome using AdaBoost, in: 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016, IEEE, Las Vegas, 2016, pp. 300–303.
Pashaei, E., Yilmaz, A., Aydin, N., A combined SVM and Markov model approach for splice site identification, in: 6th International Conference on Computer and Knowledge Engineering, ICCKE 2016, IEEE, 2016, pp. 200–204.
Pashaei, E., Yilmaz, A., Ozen, M., Aydin, N., Prediction of splice site using AdaBoost with a new sequence encoding approach, in: IEEE International Conference on Systems, Man, and Cybernetics, SMC 2016, IEEE, 2017, pp. 3853–3858.
Gonzalez Perea, R., Ballesteros, R., Ortega, J.F., Moreno, M.A., Water and energy demand forecasting in large-scale water distribution networks for irrigation using open data and machine learning algorithms, Comput. Electron. Agric., 188(2021), 106327.
Gonz´alez Perea, R., Fern´andez Garc´ıa, I., Camacho Poyato, E., Rodr´ıguez D´ıaz, J.A., New memory-based hybrid model for middle-term water demand forecasting in irrigated areas, Agric. Water Manage., 284(2023), 108367.
Pu, Z., Yan, J., Chen, L., Li, Z., Tian, W. et al., A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting, Front. Environ. Sci. Eng., 17(2)(2023), 1–14.
Pulido-Calvo, I., Montesinos, P., Rold´an, J., Ruiz-Navarro, F., Linear regressions and neural approaches to water demand forecasting in irrigation districts with telemetry systems, Biosyst. Eng., 97(2)(2007), 283–293.
Rezaali, M., Quilty, J., Karimi, A., Probabilistic urban water demand forecasting using wavelet-based machine learning models, J. Hydrol., 600(2021).
Ristow, D.C.M., Henning, E., Kalbusch, A., Petersen, C.E., Models for forecasting water demand using time series analysis: A case study in southern Brazil, J. Water Sanit. Hyg. Dev., 11(2)(2021), 231–240.
Rustam, F., Ishaq, A., Kokab, S.T., de la Torre Diez, I., Vidal Maz´on, J.L. et al., An Artificial Neural Network Model for Water Quality and Water Consumption Prediction, Water, 14(21)(2022), 3359.
Sahoo, B.B., Panigrahi, B., Nanda, T., Tiwari, M.K., Sankalp, S., Multi-step Ahead Urban Water Demand Forecasting Using Deep Learning Models, SN Comput. Sci., 4(6)(2023), 1–16.
Salloom, T., Kaynak, O., He, W., A novel deep neural network architecture for real-time water demand forecasting, J. Hydrol., 599(2021), 126353.
Salloom, T., Kaynak, O., Yu, X., He, W., Proportional integral derivative booster for neural networks-based time-series prediction: Case of water demand prediction, Eng. Appl. Artif. Intell., 108(2022), 104570.
Shabani, S., Yousefi, P., Naser, G., Support Vector Machines in Urban Water Demand Forecasting Using Phase Space Reconstruction, Procedia Eng., 186(2017), 537–543.
Shan, S., Ni, H., Chen, G., Lin, X., Li, J., A Machine Learning Framework for Enhancing Short-Term Water Demand Forecasting Using Attention-BiLSTM Networks Integrated with XGBoost Residual Correction, Water, 15(20)(2023), 3605.
Sharma, S.K., A novel approach on water resource management with Multi-Criteria Optimization and Intelligent Water Demand Forecasting in Saudi Arabia, Environ. Res., 208(2022), 112578.
Shirkoohi, M.G., Doghri, M., Duchesne, S., Short-term water demand predictions coupling an artificial neural network model and a genetic algorithm, Water Supply, 21(5)(2021), 2374–2386.
Shuang, Q., Zhao, R.T., Water demand prediction using machine learning methods: A case study of the Beijing–Tianjin–Hebei region in China, Water, 13(3)(2021), 1–16.
Sta´nczyk, J., Kajewska-Szkudlarek, J., Lipi´nski, P., Rychlikowski, P., Improving short-term water demand forecasting using evolutionary algorithms, Sci. Rep., 12(1)(2022), 1–25.
Stelzl, A., Pointl, M., Fuchs-Hanusch, D., Estimating Future Peak Water Demand with a Regression Model Considering Climate Indices, Water, 13(14)(2021), 1912.
Tiwari, M.K., Adamowski, J.F., An ensemble wavelet bootstrap machine learning approach to water demand forecasting: a case study in the city of Calgary, Canada, Urban Water J., 14(2)(2017), 185–201.
Tunay, M., Pashaei, E., Pashaei, E., Hybrid Hypercube Optimization Search Algorithm and Multilayer Perceptron Neural Network for Medical Data Classification, Comput. Intell. Neurosci., 2022(1)(2022), 1612468.
Uzlu, E., Modeling and Forecasting ofWater Demand in the City of Istanbul Using Artificial Neural Networks Optimized with Rao Algorithms, Arab. J. Sci. Eng., (2024).
Wang, K., Ye, Z., Wang, Z., Liu, B., Feng, T., MACLA-LSTM: A Novel Approach for Forecasting Water Demand, Sustainability, 15(4)(2023), 3628.
Westall, F., Brack, A., The Importance of Water for Life, Space Sci. Rev., 214(2)(2018), 1–23.
Xenochristou, M., Hutton, C., Hofman, J., Kapelan, Z., Water Demand Forecasting Accuracy and Influencing Factors at Different Spatial Scales Using a Gradient Boosting Machine, Water Resour. Res., 56(8)(2020).
Xenochristou, M., Kapelan, Z., An ensemble stacked model with bias correction for improved water demand forecasting, Urban Water J., 17(3)(2020), 212–223.
Xia, X., Liu, B., Tian, R., He, Z., Han, S. et al., An interval water demand prediction method to reduce uncertainty: A case study of Sichuan Province, China, Environ. Res., 238(2023).
Xu, Z., Lv, Z., Li, J., Shi, A., A Novel Approach for Predicting Water Demand with Complex Patterns Based on Ensemble Learning, Water Resour. Manag., 36(11)(2022), 4293–4312.
Yan, D., Tao, Y., Zhang, J., Yang, H., Research on urban water demand prediction based on machine learning and feature engineering,Water Supply, 0(2024), 1.
Zanfei, A., Brentan, B.M., Menapace, A., Righetti, M., A short-term water demand forecasting model using multivariate long short-term memory with meteorological data, J. Hydroinform., 24(5)(2022), 1053–1065.
Zanfei, A., Brentan, B.M., Menapace, A., Righetti, M., Herrera, M., Graph Convolutional Recurrent Neural Networks for Water Demand Forecasting, Water Resour. Res., 58(7)(2022).
Zhang, Y., Zhao, Z., Zheng, J., CatBoost: A new approach for estimating daily reference crop evapotranspiration in arid and semi-arid regions of Northern China, J. Hydrol., 588(2020), 125087.
Zubaidi, S.L., Al-Bdairi, N.S.S., Ortega-Martorell, S., Ridha, H.M., Al-Ansari, N. et al., Assessing the Benefits of Nature-Inspired Algorithms for the Parameterization of ANN in the Prediction of Water Demand, J. Water Resour. Plan. Manag., 149(1)(2023), 04022075.
Zubaidi, S.L., Al-Bugharbee, H., Ortega-Martorell, S., Gharghan, S.K., Olier, I. et al., A Novel Methodology for Prediction Urban Water Demand by Wavelet Denoising and Adaptive Neuro-Fuzzy Inference System Approach, Water, 12(6)(2020), 1628.
Zubaidi, S.L., Kot, P., Alkhaddar, R.M., Abdellatif, M., Al-Bugharbee, H., Short-term water demand prediction in residential complexes: Case study in Columbia city, USA, in: Proceedings of the International Conference on Developments in eSystems Engineering (DeSE), IEEE, 2018, 31–35.
Zubaidi, S.L., Hashim, K., Ethaib, S., Al-Bdairi, N.S.S., Al-Bugharbee, H. et al., A novel methodology to predict monthly municipal water demand based on weather variables scenario, J. King Saud Univ. Eng. Sci., 34(3)(2022), 163–169.
Zubaidi, S.L., Gharghan, S.K., Dooley, J., Alkhaddar, R.M., Abdellatif, M., Short-Term Urban Water Demand Prediction Considering Weather Factors, Water Resour. Manag., 32(14)(2018), 4527–4542.
Zubaidi, S.L., Kumar, P., Al-Bugharbee, H., Ahmed, A.N., Ridha, H.M. et al., Developing a hybrid model for accurate short-term water demand prediction under extreme weather conditions: a case study in Melbourne, Australia, Appl. Water Sci., 13(9)(2023).
Zubaidi, S.L., Ortega-Martorell, S., Al-Bugharbee, H., Olier, I., Hashim, K.S. et al., Urban Water Demand Prediction for a City That Suffers from Climate Change and Population Growth: Gauteng Province Case Study, Water, 12(7)(2020), 1885.

Details

Primary Language

English

Subjects

Machine Learning (Other)

Journal Section

Research Article

Authors

Elham Pashaei ^*
0000-0001-7401-4964
Türkiye

Doğukan Sürücü This is me
0009-0001-9818-0112
Türkiye

Elif Sakal
0009-0001-1382-5023
Türkiye

Ayşe Nur Korkmaz This is me
0009-0003-7557-1115
Türkiye

Publication Date

December 30, 2025

Submission Date

June 20, 2025

Acceptance Date

August 26, 2025

Published in Issue

Year 2025 Volume: 17 Number: 2

DOI

https://doi.org/10.47000/tjmcs.1723524

IZ

https://izlik.org/JA92BD89AH

Cite

RIS / Bibtex

APA

Pashaei, E., Sürücü, D., Sakal, E., & Korkmaz, A. N. (2025). An Ensemble-Based Approach for Water Demand Prediction: Comparative Analysis of Conventional, Machine Learning, and Deep Learning Techniques. Turkish Journal of Mathematics and Computer Science, 17(2), 338-365. https://doi.org/10.47000/tjmcs.1723524

AMA

1.Pashaei E, Sürücü D, Sakal E, Korkmaz AN. An Ensemble-Based Approach for Water Demand Prediction: Comparative Analysis of Conventional, Machine Learning, and Deep Learning Techniques. TJMCS. 2025;17(2):338-365. doi:10.47000/tjmcs.1723524

Chicago

Pashaei, Elham, Doğukan Sürücü, Elif Sakal, and Ayşe Nur Korkmaz. 2025. “An Ensemble-Based Approach for Water Demand Prediction: Comparative Analysis of Conventional, Machine Learning, and Deep Learning Techniques”. Turkish Journal of Mathematics and Computer Science 17 (2): 338-65. https://doi.org/10.47000/tjmcs.1723524.

EndNote

Pashaei E, Sürücü D, Sakal E, Korkmaz AN (December 1, 2025) An Ensemble-Based Approach for Water Demand Prediction: Comparative Analysis of Conventional, Machine Learning, and Deep Learning Techniques. Turkish Journal of Mathematics and Computer Science 17 2 338–365.

IEEE

[1]E. Pashaei, D. Sürücü, E. Sakal, and A. N. Korkmaz, “An Ensemble-Based Approach for Water Demand Prediction: Comparative Analysis of Conventional, Machine Learning, and Deep Learning Techniques”, TJMCS, vol. 17, no. 2, pp. 338–365, Dec. 2025, doi: 10.47000/tjmcs.1723524.

ISNAD

Pashaei, Elham - Sürücü, Doğukan - Sakal, Elif - Korkmaz, Ayşe Nur. “An Ensemble-Based Approach for Water Demand Prediction: Comparative Analysis of Conventional, Machine Learning, and Deep Learning Techniques”. Turkish Journal of Mathematics and Computer Science 17/2 (December 1, 2025): 338-365. https://doi.org/10.47000/tjmcs.1723524.

JAMA

1.Pashaei E, Sürücü D, Sakal E, Korkmaz AN. An Ensemble-Based Approach for Water Demand Prediction: Comparative Analysis of Conventional, Machine Learning, and Deep Learning Techniques. TJMCS. 2025;17:338–365.

MLA

Pashaei, Elham, et al. “An Ensemble-Based Approach for Water Demand Prediction: Comparative Analysis of Conventional, Machine Learning, and Deep Learning Techniques”. Turkish Journal of Mathematics and Computer Science, vol. 17, no. 2, Dec. 2025, pp. 338-65, doi:10.47000/tjmcs.1723524.

Vancouver

1.Elham Pashaei, Doğukan Sürücü, Elif Sakal, Ayşe Nur Korkmaz. An Ensemble-Based Approach for Water Demand Prediction: Comparative Analysis of Conventional, Machine Learning, and Deep Learning Techniques. TJMCS. 2025 Dec. 1;17(2):338-65. doi:10.47000/tjmcs.1723524