References

Adamowski, J., Fung Chan, H., Prasher, S.O., Ozga‐Zielinski, B., Sliusarieva, A. (2012). Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada. Water Resour. Res., 48(1). https://doi.org/10.1029/2010wr009945

Adamowski, J., Karapataki, C. (2010). Comparison of multivariate regression and artificial neural networks for peak urban water-demand forecasting: Evaluation of different ANN learning algorithms. J. Hydrol. Eng., 15(10): 729–743. https://doi.org/10.1061/(asce)he.1943-5584.0000245

Ahmed, V., Saad, A., Saleh, H., Saboor, S., Kasianov, N., Alnaqbi, T. (2020). Implementation of water demand forecasting model to aid sustainable water supply chain management in UAE. https://doi.org/10.20944/preprints202011.0205.v1

Ashbolt, S., Maheepala, S., Perera, B.J. (2014). A framework for short-term operational planning for water grids. Water Resour. Manage., 28(8): 2367–2380. https://doi.org/10.1007/s11269-014-0620-4

Bakker, M., Vreeburg, J., Van Schagen, K., Rietveld, L. (2013). A fully adaptive forecasting model for short-term drinking water demand. Environ. Model. Software, 48: 141–151. https://doi.org/10.1016/j.envsoft.2013.06.012

Caiado, J. (2010). Performance of combined double seasonal univariate time series models for forecasting water demand. J. Hydrol. Eng., 15(3): 215–222. https://doi.org/10.1061/(asce)he.1943-5584.0000182

Chen, L., Yan, H., Yan, J., Wang, J., Tao, T., Xin, K., Li, S., Pu, Z., Qiu, J. (2022). Short-term water demand forecast based on automatic feature extraction by one-dimensional convolution. J. Hydrol., 606: 127440. https://doi.org/10.1016/j.jhydrol.2022.127440

De Souza Groppo, G., Costa, M.A., Libânio, M. (2019). Predicting water demand: A review of the methods employed and future possibilities. Water Supply, 19(8): 2179–2198. https://doi.org/10.2166/ws.2019.122

Kavya, M., Mathew, A., Shekar, P.R., Sarwesh, P. (2023). Short term water demand forecast modelling using artificial intelligence for smart water management. Sustainable Cities and Society, 95: 104610. https://doi.org/10.1016/j.scs.2023.104610

Koo, K.M., Han, K.H., Jun, K.S., Lee, G., Kim, J.S., Yum, K.T. (2021). Performance assessment for short-term water demand forecasting models at an end-use level in Korea. https://doi.org/10.20944/preprints202104.0332.v1

Pandey, P., Bokde, N.D., Dongre, S., Gupta, R. (2021). Hybrid models for water demand forecasting. J. Water Resour. Plan. Manage., 147(2). https://doi.org/10.1061/(asce)wr.1943-5452.0001331

Razali, S.N., Rusiman, M.S., Zawawi, N.I., Arbin, N. (2018). Forecasting of water consumptions expenditure using Holt-winter’s and ARIMA. J. Physics: Conference Ser., 995: 012041. https://doi.org/10.1088/1742-6596/995/1/012041

Salloom, T., Kaynak, O., He, W. (2021). A novel deep neural network architecture for real-time water demand forecasting. J. Hydrology, 599: 126353. https://doi.org/10.1016/j.jhydrol.2021.126353

Vijai, P., Bagavathi Sivakumar, P. (2018). Performance comparison of techniques for water demand forecasting. Procedia Comp. Sci., 143: 258–266. https://doi.org/10.1016/j.procs.2018.10.394