Risk-Based Justification of the Structure and Parameters of Advanced Water Supply and Distribution Systems

Introduction. Water supply systems are strategically important, energy-intensive, capital-intensive, and highly inertial systems. These characteristics result in a virtually complete lack of adaptive mechanisms for optimizing their operating conditions. Water supply disruptions are caused by the unpredictability of system development patterns and by the lack of algorithms for determining the probability of population migration tracks, specific water consumption and system failure rates. Demonstrative examples of such errors include group water supply systems built more than 60 years ago. Due to population outflow and industrial decline, many water supply systems remain underground but are no longer operated because of insufficient consumer demand. At the same time, systems in large cities and megalopolises are overloaded because of active infill development, which causes imbalances in utility networks. Paradoxically, statistics for the past 20 years show that specific water consumption has decreased by almost half.

Materials and methods. When designing advanced water supply schemes, uncertainties related to future water demand and electricity unit costs should be considered, and system development trajectories that minimize economic and technological risks should be selected.

Results. A decision-making criteria matrix can be applied to select the parameters of a future water supply system under conditions of uncertainty related to population size, unit water demand, electricity costs, and other factors.

Conclusions. A multivariant design approach should be adopted, treating water consumption, population size, and electricity cost as fuzzy and uncertain variables and selecting options that minimize life-cycle cost risks.

1. Chupin R.V., Primin O.G. Substantiation of the wastewater disposal system parameters in the context of projected water consumption and wastewater discharge uncertainty. Water Supply and Sanitary Technique. 2017; 11:36-43. EDN ZRKPZR. (rus.).

2. Feng F., Zhang Yu., Chen Zh., Ni J., Feng Yu., Xie Yu. et al. Machine learning to assess and support safe drinking water supply : a systematic review. Journal of Environmental Chemical Engineering. 2025; 13(1):114481. DOI: 10.1016/j.jece.2024.114481. EDN NJYAPH.

3. El Bilali A., Taleb A. State-of-the-art on irrigation water quality management using data-driven methods: Practical application, limitations, and prospective directions. Physics and Chemistry of the Earth, Parts A/B/C. 2024; 136:103794. DOI: 10.1016/j.pce.2024.103794. EDN HGISYS.

4. Chupin R., Moroz M., Chupin V. Designing water supply and wastewater disposal systems in conditions of fuzzy values of prospective water consumption and population base. Water Supply and Sanitary Technique. 2022; 11:16-25. DOI: 10.35776/VST.2022.11.02. EDN XXZIEA. (rus.).

5. Chupin R.V. Indicative and adaptive management of the water supply and wastewater disposal system development. Water Supply and Sanitary Technique. 2018; 8:24-29. EDN XWQJJR. (rus.).

6. Ibuot U.P. Communication Strategies for Sustainable Water Supply Systems Development in Select Lagos and Ogun Communities. GVU Journal of Research and Development. 2025; 2(1):176-187.

7. Chupin R.V., Fam N.M. Optimization of the structure and parameters of the developing group water supply systems. Water Supply and Sanitary Technique. 2019; 1:30-37. EDN VRGOFJ. (rus.).

8. Bhattarai B.N., Pasitsuparoad P., Gyawali S., Joshi H.P., Ojha R. Can small towns sustain their own water supply systems? A systematic review of what works and what doesn't. International Journal of Advanced Research. 2025.

9. Libey A.K. The Worth of Water: Evaluating Interventions to Achieve Financially and Operationally Sustainable Urban and Rural Water Systems in Low-Income Settings. University of Colorado at Boulder, 2022.

10. Benavides-Muñoz H.M. Index of Sustainability of Water Supply Systems (ISA): An Autonomous Framework for Urban Water Sustainability Assessment in Data-Scarce Settings. Sustainability. 2025; 17(24):11293. DOI: 10.3390/su172411293. EDN XMJBGV.

11. Chupin R., Moroz M. The use of road transport in the systems of group water supply and wastewater disposal. Water Supply and Sanitary Technique. 2021; 5:57-64. DOI: 10.35776/VST.2021.05.07. EDN RDVUAL. (rus.).

12. Chupin R., Moroz M., Bober V. Substantiation of the diameters of pipelines of water supply and wastewater disposal systems based on minimizing the costs of their life cycle. Water Supply and Sanitary Technique. 2022; 4:52-58. DOI: 10.35776/VST.2022.04.07. EDN CIOYJH. (rus.).

13. Kapanski A.A., Klyuev R.V., Boltrushevich A.E., Sorokova S.N., Efremenkov E.A., Demin A.Y. et al. Geospatial clustering in smart city resource management: An initial step in the optimisation of complex technical supply systems. Smart Cities. 2025; 8(1). DOI: 10.3390/smartcities8010014. EDN UGBWSI.

14. Altaf M., Jaffari R., Alalaoul W.S., Musarat M.A., Ammad S. Developing automated strategy of life cycle cost analysis (LCCA) with building information modeling (BIM) integration for building projects. Results in Engineering. 2025; 25:104179. DOI: 10.1016/j.rineng.2025.104179. EDN FCQHQY.

15. Sharif S.A., Hammad A. Simulation-based multi-objective optimization of institutional building renovation considering energy consumption, life-cycle cost and life-cycle assessment. Journal of Building Engineering. 2019; 21:429-445. DOI: 10.1016/j.jobe.2018.11.006

16. Erto P., Lanzotti A. Statistical model "Life Cycle Cost-Reliability" for a new mass transit vehicle. WIT Transactions on The Built Environment. 2026; 6.

17. Shi H., Wang X., Guo H., Hao H. Risk assessment models to investigate the impact of emergency on a water supply system. Water Supply. 2020; 20(8):3542-3556. DOI: 10.2166/ws.2020.224. EDN FCLXNS.

18. Odjegba E., Oluwasanya G., Sadiq A., Brion G. Sustainability and risk assessment matrix (SRAM): pathway to water security. Water Supply. 2020; 20(7):2928-2940. DOI: 10.2166/ws.2020.196. EDN EFIJKD.

19. Nesticò A., De Mare G., Maselli G. An economic model of risk assessment for water projects. Water Supply. 2020; 20(6):2054-2068. DOI: 10.2166/ws.2020.093. EDN BAYGQZ.

20. Marques J., Cunha M. Upgrading water distribution networks to work under uncertain conditions. Water Supply. 2020; 20(3):878-888. DOI: 10.2166/ws.2020.011. EDN HYQRJO.

21. Wang Zh., Wu X., Wang H., Wu T. Prediction and analysis of domestic water consumption based on optimized grey and Markov model. Water Supply. 2021; 21(7):3887-3899. DOI: 10.2166/ws.2021.146. EDN HXXBWZ.

22. Pandey P., Dongre S., Gupta R. Probabilistic and fuzzy approaches for uncertainty consideration in water distribution networks : a review. Water Supply. 2020; 20(1):13-27. DOI: 10.2166/ws.2019.141

23. Kalbusch A., Ghisi E. Energy consumption in the life cycle of plumbing fixtures. Water Supply. 2019; 19(1):70-78. DOI: 10.2166/ws.2018.053

24. Orlov A.I. Theory of Decision-Making : textbook. Moscow, Examen, 2005; 656. (rus.).

25. Certificate of state registration of computer programs No. 2024687562 dated November 20, 2024. Minimizing risks in the design of urban pipeline systems / Melekhov E.S., Yarygin R.N., Bober V.A.