Smart Water Management Systems: Engineering Innovations for Water Conservation and Distribution

Year 2025, Volume: 3 Issue: 1, 13 - 31, 30.06.2025

Dıckson Davıd Olodu , Francis Inegbedion , Osagie Imevbore Ihenyen

https://doi.org/10.63063/jsat.1613583

Abstract

Smart water management systems (SWMS) leverage engineering innovations, such as IoT sensors, machine learning algorithms, and real-time monitoring, to improve water conservation and distribution efficiency. The traditional water systems, characterized by high water wastage (30%) and substantial leakage (15%), are being increasingly replaced by smarter systems that utilize IoT sensors, automated valves, and data analytics to reduce wastage, improve reliability, and increase system efficiency. In a comparison of water usage efficiency, smart systems exhibit a 40% reduction in average daily water usage, from 500,000 liters to 300,000 liters. Water leakage is reduced from 15% to 5%, and water wastage due to improper distribution decreases from 30% to 10%. Consumer satisfaction also improves, with complaints decreasing and system response times dropping from 24 hours to 2 hours. IoT sensors, such as pressure and flow rate sensors, offer high accuracy and low power consumption, ensuring reliable data transmission and energy efficiency, with a mean transmission frequency of 10-15 minutes and power consumption as low as 8 mW. Cost analysis indicates a higher initial setup cost for smart systems (₦150 million) compared to traditional ones (₦100 million), but the reduction in annual maintenance (₦2 million vs. ₦5 million) and operational costs (40% reduction) make smart systems more cost-effective over time. Energy consumption is reduced by 16%, with solar-powered IoT sensors contributing to a decrease in carbon footprint by 60%. Regression and statistical analyses confirm that water pressure uniformity, leak detection time, and daily water demand significantly influence water loss, while machine learning optimization leads to an 18% improvement in water distribution efficiency. A correlation model was developed to assess the relationship between key parameters: the correlation coefficient between leak detection time and water wastage is found to be 0.85, indicating a strong positive correlation. Similarly, the correlation between pressure uniformity and system efficiency shows a value of 0.92, reflecting a strong positive relationship. These innovations collectively represent a transformative shift toward sustainable and efficient water management.

Keywords

Water Distribution, Leak Detection, Pressure Uniformity, Seasonal Consumption, Leak Density, Water Management.

Supporting Institution

The authors acknowledge the following institutions for their support: Benson Idahosa University, Faculty of Engineering, Department of Mechanical Engineering, Benin City, Edo State, Nigeria; and University of Benin, Faculty of Engineering, Department of Production Engineering, Benin City, Edo State, Nigeria. The authors extend their gratitude for the technical and infrastructural support provided during the research process, which significantly contributed to the successful completion of this st

Smart Water Management Systems: Engineering Innovations for Water Conservation and Distribution

Abstract

Smart water management systems (SWMS) leverage engineering innovations, such as IoT sensors, machine learning algorithms, and real-time monitoring, to improve water conservation and distribution efficiency. The traditional water systems, characterized by high water wastage (30%) and substantial leakage (15%), are being increasingly replaced by smarter systems that utilize IoT sensors, automated valves, and data analytics to reduce wastage, improve reliability, and increase system efficiency. In a comparison of water usage efficiency, smart systems exhibit a 40% reduction in average daily water usage, from 500,000 liters to 300,000 liters. Water leakage is reduced from 15% to 5%, and water wastage due to improper distribution decreases from 30% to 10%. Consumer satisfaction also improves, with complaints decreasing and system response times dropping from 24 hours to 2 hours. IoT sensors, such as pressure and flow rate sensors, offer high accuracy and low power consumption, ensuring reliable data transmission and energy efficiency, with a mean transmission frequency of 10-15 minutes and power consumption as low as 8 mW. Cost analysis indicates a higher initial setup cost for smart systems (₦150 million) compared to traditional ones (₦100 million), but the reduction in annual maintenance (₦2 million vs. ₦5 million) and operational costs (40% reduction) make smart systems more cost-effective over time. Energy consumption is reduced by 16%, with solar-powered IoT sensors contributing to a decrease in carbon footprint by 60%. Regression and statistical analyses confirm that water pressure uniformity, leak detection time, and daily water demand significantly influence water loss, while machine learning optimization leads to an 18% improvement in water distribution efficiency. A correlation model was developed to assess the relationship between key parameters: the correlation coefficient between leak detection time and water wastage is found to be -0.85, indicating a strong negative correlation. Similarly, the correlation between pressure uniformity and system efficiency shows a value of 0.92, reflecting a strong positive relationship. These innovations collectively represent a transformative shift toward sustainable and efficient water management.

Keywords

Water Distribution, Leak Detection, Pressure Uniformity, Seasonal Consumption, Leak Density, Water Management.

References

• R. Jeya, G. R. Venkatakrishnan, R. Rengaraj, Rajalakshmi, Neythra Mohandoss, and Jayaprakash, “An integrated smart water management system for efficient water conservation,” International Journal of Power Electronics and Drive Systems, vol. 15, no. 1, pp. 635–644, 2024. https://doi.org/10.11591/ijece.v15i1.pp635-644
• Lloyd David and Owen, “Smart water management,” River, 2023. https://doi.org/10.1002/rvr2.29
• H. M. Ramos, A. Kuriqi, M. Besharat, E. Creaco, E. Sebastião, O. E. Tasca Amaral, R. Coronado-Hernández, R. Pienika, and P. L. Iglesias-Rey, “Smart Water Grids and Digital Twin for the Management of System Efficiency in Water Distribution Networks,” Water, vol. 15, p. 1129, pp. 1–22, 2023. https://doi.org/10.3390/w15061129
• P. Aiello, M. Giugni, and G. Perillo, “Internet of Things for Smart Management of Water Networks,” Environmental Sciences Proceedings, vol. 21, no. 57, pp. 1–8, 2022. https://doi.org/10.3390/environsciproc2022021057
• S. R. Krishnan, M. K. Nallakaruppan, C. Rajeswari, S. Koppu, M. Iyapparaja, J. Kandhasamy, S. Sadhasivam, and S. Sethuraman, “Smart Water Resource Management Using Artificial Intelligence—A Review,” Sustainability, vol. 14, p. 13384, pp. 1–28, 2022. https://doi.org/10.3390/su142013384
• S. A. Palermo, M. Maiolo, A. C. Brusco, M. Turco, B. Pirouz, E. Greco, G. Spezzano, and P. Piro, “Smart Technologies for Water Resource Management: An Overview,” Sensors, vol. 22, p. 6225, pp. 1–23, 2022. https://doi.org/10.3390/s22166225
• H. M. Ramos, M. C. Morani, A. Carravetta, O. Fecarrotta, K. Adeyeye, P. A. López-Jiménez, and M. Pérez-Sánchez, “New Challenges towards Smart Systems’ Efficiency by Digital Twin in Water Distribution Networks,” Water, vol. 14, p. 1304, pp. 1–17, 2022. https://doi.org/10.3390/w14081304
• N. Keriwala and A. R. Patel, “Innovative Roadmap for Smart Water Cities: A Global Perspective,” Materials Proceedings, vol. 10, no. 1, pp. 1–9, 2022. https://doi.org/10.3390/materproc2022010001
• H. Mezni, M. Driss, W. Boulila, S. B. Atitallah, M. Sellami, and N. Alharbi, “SmartWater: A Service-Oriented and Sensor Cloud-Based Framework for Smart Monitoring of Water Environments,” Remote Sensing, vol. 14, no. 922, pp. 1–26, 2022. https://doi.org/10.3390/rs14040922
• M. Driss, W. Boulila, H. Mezni, M. Sellami, S. B. Atitallah, and N. Alharbi, “An Evidence Theory Based Embedding Model for the Management of Smart Water Environments,” Sensors, vol. 23, p. 4672, pp. 1–21, 2023. https://doi.org/10.3390/s23104672
• A. Gupta, P. Pandey, A. Feijóo, Z. M. Yaseen, and N. D. Bokde, “Smart water technology for efficient water resource management: a review,” Energies, vol. 13, p. 6268, 2020. [Online]. Available: https://doi.org/10.3390/en13236268
• K. D. Shim, E. Berrettini, and Y. G. Park, “Smart Water Solutions for the Operation and Management of a Water Supply System in Aracatuba, Brazil,” Water, vol. 14, p. 3965, pp. 1–14, 2022. https://doi.org/10.3390/w14233965
• M. Kalimuthu, A. Sudharson, C. Ponraj, and J. Jackson, “Water Management and Metering System for Smart Cities,” International Journal of Scientific & Technology Research, vol. 9, no. 4, pp. 1367–1372, 2020.
• V. J. Wankhede and K. P. Dandge, “Smart Water Supply, Monitoring and Quality Control by Using Latest Techniques,” Indian Scientific Journal of Research in Engineering and Management, vol. 6, no. 1, pp. 1–6, 2022. https://doi.org/10.55041/ijsrem11472
• Y. Tace, S. Elfilali, M. Tabaa, and C. Leghris, “Implementation of Smart Irrigation Using IoT and Artificial Intelligence,” Mathematical Modeling and Computing, vol. 10, no. 2, pp. 575–582, 2023. https://doi.org/10.23939/mmc2023.02.575
• A. Predescu, C.-O. Truica, E.-S. Apostol, M. Mocanu, and C. Lupu, “An Advanced Learning-Based Multiple Model Control Supervisor for Pumping Stations in a Smart Water Distribution System,” Mathematics, vol. 8, p. 887, pp. 1–28, 2020. https://doi.org/10.3390/math8060887
• A. Di Nardo, M. Di Natale, A. Di Mauro, E. M. Díaz, J. A. Blázquez Garcia, G. F. Santonastaso, and F. P. Tuccinardi, “An Advanced Software to Manage a Smart Water Network with Innovative Metrics and Tools Based on Social Network Theory,” EPiC Series in Engineering, vol. 3, pp. 582–592, 2018. https://doi.org/10.29007/GVNZ
• Y. M. Djaksana, “Smart Water Management Framework Berbasis IoT Untuk Mendukung Pertanian Urban,” Jurnal Pengkajian dan Penerapan Teknik Informatika, vol. 14, no. 1, pp. 1–7, 2020. https://doi.org/10.33322/petir.v14i1.1112
• P. U. Chavan, M. R. Deore, P. R. Sonawane, P. D. Shinde, and P. P. Yadav, “Hardware & Software Architecture for IoT-Based Water Distribution and Monitoring System,” Journal of Emerging Technologies and Innovative Research, vol. 7, no. 6, pp. 1082–1085, 2020.
• H.-C. Ho, K. S. Puika, and T. K. Pirdo, “Development of IoT-Based Water Reduction System for Improving Clean Water Conservation,” Scientific Review Engineering and Environmental Sciences, vol. 29, no. 1, pp. 54–61, 2020. https://doi.org/10.22630/PNIKS.2020.29.1.5
• R. Gómez-Beas, E. Contreras-Arribas, S. Romero, Ó. Lorente, A. Linares-Sáez, and L. Panizo, “Integrated Water Resources Management in a Complex Reservoir System Through a Multipurpose DSS Tool,” EPiC Series in Engineering, vol. 3, pp. 866–873, 2018. https://doi.org/10.29007/HHW9
• S. J. and M. Kowsigan, “IoT Enabled Water Distribution Systems for Energy Efficiency in WSN,” in Proc. Int. Conf. Signals and Electronic Systems (ICSES), 2022, pp. 1–8. https://doi.org/10.1109/ICSES55317.2022.9914274
• B. S. Kumar, S. Ramalingam, S. Balamurugan, S. Soumiya, and S. Yogeswari, “Water Management and Control Systems for Smart City using IoT and Artificial Intelligence,” in 2022 Int. Conf. Edge Comput. and Appl. (ICECAA), pp. 653–657. https://doi.org/10.1109/ICECAA55415.2022.9936166
• T. Alexopoulos, J. Marsh, G. Llewellyn, and M. Packianather, “An adaptive water consumption monitoring and conservation,” Smart Innovation, Systems and Technologies, pp. 191–200, 2023. https://doi.org/10.1007/978-981-19-9205-6_18
• A. Rjoub and M. Alkhateeb, “ICT Smart Water Management System for Real-Time Applications,” in Proc. Int. Conf. Modern Circuits and Systems Technologies (MOCAST), pp. 1–4, 2022. https://doi.org/10.1109/MOCAST54814.2022.9837570
• O. J. Aigbokhan, O. H. Adedeji, A. O. Oladoye, and J. A. Oyedepo, “Dynamics of urban landscape and its thermal interactions with selected land cover types: A case of Benin City, Nigeria,” Journal of Applied Life Sciences and Environment, vol. 5, no. 6, pp. 209–229, 2023. https://doi.org/10.46909/alse-562099