O‘ZBEKISTONDA SUVGA BO‘LGAN TALABNI VAQTLI QATORLAR ORQALI TAHLIL QILISH

1. 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. https://doi.org/10.1016/j.jhydrol.2010.04.005

2. Donkor, E. A., Mazzuchi, T. A., Soyer, R., & Alan Roberson, J. (2014). Urban water demand forecasting: Review

of methods and models. Journal of Water Resources Planning and Management, 140(2), 146–159. https://doi.

org/10.1061/(ASCE)WR.1943-5452.0000314

3. IPCC. (2021). Climate Change 2021: The Physical Science Basis. Cambridge University Press.

4. Alcamo, J., Flörke, M., & Märker, M. (2007). Future long-term changes in global water resources driven by socioeconomic

and climatic changes. Hydrological Sciences Journal, 52(2), 247–275.

5. Kundzewicz, Z. W., & Stakhiv, E. Z. (2010). Are climate models “ready for prime time” in water resources management

applications, or is more research needed? Hydrological Sciences Journal, 55(7), 1085–1089.

6. Pahl-Wostl, C. (2007). Transitions towards adaptive management of water facing climate and global change. Water

Resources Management, 21(1), 49–62.

7. 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.

8. 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.

9. Pagano, T. C., Garen, D. C., & Sorooshian, S. (2004). Evaluation of official western U.S. seasonal water supply

outlooks, 1922–2002. Journal of Hydrometeorology, 5(5), 896–909.

10. Clark, M. P., & Hay, L. E. (2004). Use of medium-range weather forecasts to produce predictions of streamflow. Journal

of Hydrometeorology, 5(1), 15–32.

11. House-Peters, L. A., & Chang, H. (2011). Urban water demand modeling: Review of concepts, methods, and organizing

principles. Water Resources Research, 47(5), W05401.

12. Shamir, E., & Georgakakos, K. P. (2014). Distributed snow accumulation and ablation modeling in the American River

basin. Advances in Water Resources, 68, 123–140.

13. Donkor, E. A., Mazzuchi, T. A., Soyer, R., & Roberson, J. A. (2014). Urban water demand forecasting: Review of methods

and models. Journal of Water Resources Planning and Management, 140(2), 146–159. https://doi.org/10.1061/(ASCE)

WR.1943-5452.0000314

14. 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. https://doi.org/10.1016/j.jhydrol.2010.04.005

15. 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. https://doi.org/10.1016/S0022-1694(00)00287-0

16. 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. Journal of Hydrologic Engineering,

15(10), 729–743. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000245

17. Brentan, B. M., Luvizotto, E. Jr., 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.

https://doi.org/10.1016/j.cam.2016.06.019

18. Msiza, I. S., Nelwamondo, F. V., & Marwala, T. (2008). Water demand prediction using artificial neural networks and

support vector regression. Journal of Computers, 3(11), 1–8.