Analisis Arsitektur Smart Water Management Systems Perkotaan melalui Integrative Literature Review
DOI:
https://doi.org/10.37859/jf.v16i2.11327
Abstract
This study aims to compile an integrative review of Smart Water Management Systems architecture in urban contexts to identify technology integration patterns, dominant methodological approaches, and recurring research gaps. The method used is an Integrative Literature Review of 16 national and international journal articles indexed in Scopus. The review was conducted through systematic selection, structured data extraction, and comparative-thematic synthesis to map architectural components, analytical models, optimization strategies, and evaluation frameworks. Results show that system architecture evolves in a layered structure integrating IoT for data acquisition, wireless communication networks for connectivity, cloud-edge computing for data processing, and AI/ML for predictive analytics and automated decision-making. Optimization strategies focus on water distribution efficiency, improved operational reliability, and resource loss reduction. The literature also reveals gaps in system interoperability, data standardization, cybersecurity, AI model transparency, and the lack of comprehensive evaluation dimensions. The study concludes that architectural integration and systemic evaluation are essential for developing adaptive, reliable, and sustainable urban Smart Water Management Systems.
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Aivazidou, E., Banias, G., Lampridi, M., Vasileiadis, G., Anagnostis, A., Papageorgiou, E. I., and Bochtis, D., 2021. Smart Technologies for Sustainable Water Management: An Urban Analysis. Sustainability, 13(24), 13940. doi:10.3390/su132413940.
Al-Qaisi, A. Z., 2025. Smart Water Systems: The Role of Technology and Engineering in Optimizing Urban Water Resources. Journal of Information Systems Engineering & Management, 10(21s), pp. 833-846. doi:10.52783/jisem.v10i21s.3445.
Ramos, H. M., Kuriqi, A., Besharat, M., Creaco, E., Tasca, E., Coronado-Hernández, O. E., Pienika, R., and Iglesias-Rey, P., 2023. Smart Water Grids and Digital Twin for the Management of System Efficiency in Water Distribution Networks. Water, 15(6), 1129. doi:10.3390/w15061129.
Fu, G., Jin, Y., Sun, S., Yuan, Z., and Butler, D., 2022. The Role of Deep Learning in Urban Water Management: A Critical Review. Water Research, 223, 118973. doi:10.1016/j.watres.2022.118973.
Dada, M. A., Majemite, M. T., Obaigbena, A., Daraojimba, O. H., Oliha, J. S., and Nwokediegwu, Z. Q. S., 2024. Review of Smart Water Management: IoT and AI in Water and Wastewater Treatment. World Journal of Advanced Research and Reviews, 21(1), pp. 1373-1382. doi:10.30574/wjarr.2024.21.1.0171.
Ali, A. S., Abdelmoez, M. N., Heshmat, M., and Ibrahim, K., 2022. A Solution for Water Management and Leakage Detection Problems Using IoT Based Approach. Internet of Things, 18, 100504. doi:10.1016/j.iot.2022.100504.
Okoli, N. J., and Kabaso, B., 2024. Building a Smart Water City: IoT Smart Water Technologies, Applications, and Future Directions. Water, 16(4), 557. doi:10.3390/w16040557.
Jayaraman, P., Nagarajan, K. K., Partheeban, P., and Krishnamurthy, V., 2024. Critical Review on Water Quality Analysis Using IoT and Machine Learning Models. International Journal of Information Management Data Insights, 4(1), 100210. doi:10.1016/j.jjimei.2023.100210.
Joseph, K., Sharma, A. K., van Staden, R., Wasantha, P. L. P., Cotton, J., and Small, S., 2023. Application of Software and Hardware-Based Technologies in Leaks and Burst Detection in Water Pipe Networks: A Literature Review. Water, 15(11), 2046. doi:10.3390/w15112046.
Lowe, M., Qin, R., and Mao, X., 2022. A Review on Machine Learning, Artificial Intelligence, and Smart Technology in Water Treatment and Monitoring. Water, 14(9), 1384. doi:10.3390/w14091384.
Taloma, R. J. L., Cuomo, F., Comminiello, D., and Pisani, P., 2025. Machine Learning for Smart Water Distribution Systems: Exploring Applications, Challenges and Future Perspectives. Artificial Intelligence Review, 58(4), 120. doi:10.1007/s10462-024-11093-7.
Syrmos, E., Sidiropoulos, V., Bechtsis, D., Stergiopoulos, F., Aivazidou, E., Vrakas, D., Vezinias, P., and Vlahavas, I., 2023. An Intelligent Modular Water Monitoring IoT System for Real-Time Quantitative and Qualitative Measurements. Sustainability, 15(3), 2127. doi:10.3390/su15032127.
Patgar, D., Satyanaik, U., and Ramakrishna, U. B., 2023. Smart Water Grids and Infrastructure: Emerging Technologies for Real-Time Water Quality Monitoring. World Journal of Advanced Research and Reviews, 17(1), pp. 1380-1386. doi:10.30574/wjarr.2023.17.1.0114.
Krishnan, S. R., Nallakaruppan, M. K., Chengoden, R., Koppu, S., Iyapparaja, M., Sadhasivam, J., and Sethuraman, S., 2022. Smart Water Resource Management Using Artificial Intelligence—A Review. Sustainability, 14(20), 13384. doi:10.3390/su142013384.
Webber, J. L., Fletcher, T., Farmani, R., Butler, D., and Melville-Shreeve, P., 2022. Moving to a Future of Smart Stormwater Management: A Review and Framework for Terminology, Research, and Future Perspectives. Water Research, 218, 118409. doi:10.1016/j.watres.2022.118409.
Kenda, K., Mellios, N., Senožetnik, M., and Pergar, P., 2022. Computer Architectures for Incremental Learning in Water Management. Sustainability, 14(5), 2886. doi:10.3390/su14052886.
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