AI-Driven Leakage Detection & Water Network Analytics
Keywords:
Water Utilities, Predictive Analytics, Smart Distribution, Leakage Detection, Leakage Prediction, Time Series, Anomaly Detection, Data Streams, Infrastructure Aging, Water Losses, Decision Support, Resource Optimization, Energy Efficiency, Data Driven, Forecasting Models, Pattern Analysis, High Frequency, System Monitoring, Repair Planning, Budget Allocation.Abstract
Water utilities are under growing pressure to improve operational performance and service levels while conserving energy and resources. Predictive analytics—including time series forecasting, anomaly detection, leakage detection, and leakage prediction—can inform data-driven decision-making for smart distribution systems. The overall research is designed to explore and implement predictive analytics for smart water distribution systems and leakage detection, with key contributions including a temporal analysis framework to extract time-dependent patterns from management-relevant data streams, and techniques for the detection of anomalies in flushing patterns, as well as the detection and prediction of leaks.
Water losses from ageing infrastructure, corporate theft, infiltration, and leakage combine to impose economic, social, and environmental costs. Although regulation is often a key driver for leakage detection and mitigation, proactive management is typically more cost-effective than responding to external pressure. Indeed, decision support tools can facilitate the identification of optimal solutions with respect to detection priority, repair timing, and budget allocation. Predictive analytics—including time series forecasting, anomaly detection, leakage detection, and leakage prediction—can support data-driven decision-making for smart distribution systems by exploiting existing data streams that are often recorded at high frequency.
References
1. Alawadhi, A., & Tartakovsky, D. M. (2020). Bayesian update and method of distributions: Application to leak detection in transmission mains. Water Resources Research, 56(2), e2019WR025879.
2. Bata, M., Carriveau, R., & Ting, D. S.-K. (2020). Short-term water demand forecasting using hybrid supervised and unsupervised machine learning model. Smart Water, 5, 2.
3. Ganti, V. K. A. T., Pandugula, C., Polineni, T. N. S., & Mallesham, G. (2023). Transforming sports medicine with deep learning and generative AI: personalized rehabilitation protocols and injury prevention strategies for professional athletes. Review of Contemporary Philosophy, 22(1), 3868-3882.
4. Bermúdez, J. R., López-Estrada, F. R., Besançon, G., Torres, L., & Santos-Ruiz, I. (2020). Leak-diagnosis approach for water distribution networks based on a k-NN classification algorithm. IFAC-PapersOnLine, 53(2), 16651–16656.
5. Cody, R. A., & Narasimhan, S. (2020). A field implementation of linear prediction for leak-monitoring in water distribution networks. Advanced Engineering Informatics, 45, 101103.
6. Madbhavi, R., Joshi, A., Munikoti, S., Das, L., & Mohapatra, P. K. (2020). Sensor placement for leak localization in water distribution networks using machine learning. Proceedings of the 3rd IEEE International Conference on Computing, 1–6.
7. Singireddy, J. (2022). Leveraging Artificial Intelligence and Machine Learning for Enhancing Automated Financial Advisory Systems: A Study on AIDriven Personalized Financial Planning and Credit Monitoring. Mathematical Statistician and Engineering Applications, 71(4), 16711-16728.
8. Romero, L. B., Blesa, J., Puig, V., Cembrano, M. G., & Trapiello, C. (2020). First results in leak localization in water distribution networks using graph-based clustering and deep learning. IFAC-PapersOnLine, 53(2), 16691–16696.
9. Shekofteh, M., Jalili Ghazizadeh, M., & Yazdi, J. (2020). A methodology for leak detection in water distribution networks using graph theory and artificial neural network. Urban Water Journal, 17(6), 525–533.
10. Kaulwar, P. K. Designing Secure Data Pipelines for Regulatory Compliance in Cross-Border Tax Consulting. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering (IJIREEICE), DOI, 10.
11. Fan, X., Zhang, X., & Yu, X. B. (2021). Machine learning model and strategy for fast and accurate detection of leaks in water supply network. Journal of Infrastructure Preservation and Resilience, 2, 10.
12. Fan, X., & Yu, X. B. (2022). An innovative machine learning based framework for water distribution network leakage detection and localization. Structural Health Monitoring, 21(4), 1938–1956.
13. Lakkarasu, P., & Kalisetty, S. (2022). Hybrid Cloud and AI Integration for Scalable Data Engineering: Innovations in Enterprise AI Infrastructure. Math. Stat. Eng. Appl., 71(4), 16774-16784.
14. Chen, J., Feng, X., & Xiao, S. (2021). An iterative method for leakage zone identification in water distribution networks based on machine learning. Structural Health Monitoring, 20(4), 1938–1956.
15. Momeni, A. M., & Piratla, K. R. (2021). A proof-of-concept study for hydraulic model-based leakage detection in water pipelines using pressure monitoring data. Frontiers in Water, 3, 648622.
16. O'Mahony, N., Murphy, T., Panduru, K., Riordan, D., & Walsh, J. (2016, December). Machine learning algorithms for process analytical technology. In 2016 World Congress on Industrial Control Systems Security (WCICSS) (pp. 1-7). IEEE.
17. Mashhadi, N., Shahrour, I., Attoue, N., El Khattabi, J., & Aljer, A. (2021). Use of machine learning for leak detection and localization in water distribution systems. Smart Cities, 4(4), 1293–1315.
18. Gonzalez Perea, R., Ballesteros, R., Ortega, J. F., & Moreno, M. Á. (2021). Water and energy demand forecasting in large-scale water distribution networks for irrigation using open data and machine learning algorithms. Computers and Electronics in Agriculture, 188, 106327.
19. Koppolu, H. K. R. (2021). Data-Driven Strategies for Optimizing Customer Journeys Across Telecom and Healthcare Industries. International Journal Of Engineering And Computer Science, 10(12).
20. Momeni, A., Piratla, K. R., & Madathil, K. C. (2022). Application of neural network–based modeling for leak localization in water mains. Journal of Pipeline Systems Engineering and Practice, 13(4), 04022037.
21. Adusupalli, B., Pandiri, L., & Singireddy, S. (2019). DevOps Enablement in Legacy Insurance Infrastructure for Agile Policy and Claims Deployment. risk, 7(12).
22. Kalfarisi, R., Chew, A., Cai, J., Xue, M., Pok, J., & Wu, Z. Y. (2022). Predictive modeling framework accelerated by GPU computing for smart water grid data-driven analysis in near real-time. Advances in Engineering Software, 173, 103287.
23. Hu, Z., Chen, W., Wang, H., Tian, P., & Shen, D. (2022). Integrated data-driven framework for anomaly detection and early warning in water distribution system. Journal of Cleaner Production, 373, 133977.
24. POLINENI, T., ABHIREDDY, N., & YASMEEN, Z. (2023). AI-POWERED PREDICTIVE SYSTEMS FOR MANAGING EPIDEMIC SPREAD IN HIGH-DENSITY POPULATIONS. JOURNAL FOR REATTACH THERAPY AND DEVELOPMENTAL DIVERSITIES.
25. Cai, J., Gao, J., & Xu, Y. (2022). Anomaly detection and classification in water distribution networks integrated with hourly nodal water demand forecasting models and feature extraction technique. Journal of Water Resources Planning and Management, 148(11), 04022063.
26. Annapareddy, V. N. (2021). Machine Learning Algorithms for Real-Time Fault Detection and Performance Enhancement in Solar Energy Systems. Machine Learning, 4(S4).
27. Li, Z., Wang, J., Yan, H., Li, S., Tao, T., & Xin, K. (2022). Fast detection and localization of multiple leaks in water distribution network jointly driven by simulation and machine learning. Journal of Water Resources Planning and Management, 148(9), 04022048.
28. Meda, R. (2022). Enabling sustainable manufacturing through AI-optimized supply chains. Int. J. Innov. Res. Electr. Electron. Instrum. Control Eng., 10(12).
29. Garðarsson, G. Ö., Boem, F., & Toni, L. (2022). Graph-based learning for leak detection and localisation in water distribution networks. IFAC-PapersOnLine, 55(6), 661–666.
30. Garlisi, D., Restuccia, G., Tinnirello, I., Cuomo, F., & Chatzigiannakis, I. (2022). Leakage detection via edge processing in LoRaWAN-based smart water distribution networks. In Proceedings of the 18th International Conference on Mobility, Sensing and Networking, 223–230.
31. Adusupalli, B. Strengthening Cybersecurity Compliance: Evaluating Risk Assessment Frameworks and AI-Based Anomaly Detection in Audit Practices.
32. Vanijjirattikhan, R., Khomsay, S., Kitbutrawat, N., Khomsay, K., Supakchukul, U., Udomsuk, S., Suwatthikul, J., Oumtrakul, N., & Anusart, K. (2022). AI-based acoustic leak detection in water distribution systems. Results in Engineering, 15, 100557.
33. Zhang, C., Alexander, B. J., Stephens, M. L., Lambert, M. F., & Gong, J. (2023). A convolutional neural network for pipe crack and leak detection in smart water network. Structural Health Monitoring, 22(1), 232–244.
34. Punukollu, H., Vasan, A., & Srinivasa Raju, K. (2023). Leak detection in water distribution networks using deep learning. ISH Journal of Hydraulic Engineering, 29(5), 674–682.
35. Pandugula, C., & Nampalli, R. C. R. Optimizing Retail Performance: Cloud-Enabled Big Data Strategies for Enhanced Consumer Insights.
36. Choudhary, P., Botre, B. A., & Akbar, S. A. (2023). 1-D convolution neural network based leak detection, location and size estimation in smart water grid. Urban Water Journal, 20(3), 341–351.
37. Rajabi, M. M., Komeilian, P., Wan, X., & Farmani, R. (2023). Leak detection and localization in water distribution networks using conditional deep convolutional generative adversarial networks. Water Research, 238, 120012.
38. Tornyeviadzi, H. M., & Seidu, R. (2023). Leakage detection in water distribution networks via 1D CNN deep autoencoder for multivariate SCADA data. Engineering Applications of Artificial Intelligence, 122, 106062.
39. Tornyeviadzi, H. M., Mohammed, H., & Seidu, R. (2023). Robust night flow analysis in water distribution networks: A BiLSTM deep autoencoder approach. Advanced Engineering Informatics, 58, 102135.
40. Sheelam, G. K. (2020). Data-Driven Design and Validation Techniques in Advanced Chip Engineering. Global Research Development.
41. Wu, X., Peng, S., Zheng, G., Fang, X., & Tian, Y. (2023). Leakage detection in water distribution networks based on multi-feature extraction from high-frequency pressure data. Water, 15(6), 1187.
42. Tyagi, V., Pandey, P., Jain, S., & Ramachandran, P. (2023). A two-stage model for data-driven leakage detection and localization in water distribution networks. Water, 15(15), 2710.
43. Mücke, N. T., Pandey, P., Jain, S., Bohté, S. M., & Oosterlee, C. W. (2023). A probabilistic digital twin for leak localization in water distribution networks using generative deep learning. Sensors, 23(13), 6179.
