Deep Learning for Smart Water Grids: A Targeted Review of Leak Detection Technologies

Authors

  • Sheriff Adepoju   Prairie View A & M University

Keywords:

Deep Learning, Leak Detection, Smart Water Grids, IoT-Based Monitoring, Reinforcement Learning & Explainable AI

Abstract

Deep learning is a revolutionary technology in improving leak detection in smart water grids which make water management more accurate, automated and data-driven. Recent advancements indicate that deep learning models, convolutional, recurrent and hybrid models are superior in detecting subtle hydraulic anomalies as compared to traditional models. Combined with sensor networks and cloud analytics using IoT, real-time monitoring and scalability has also been enhanced. Nevertheless, issues like the scarcity of labeled data, generalization of models under a variety of network conditions, and interpretability are some of the major obstacles. New developments show the development of interest in reinforcement learning, explainable AI, and edge computing to develop adaptive and transparent leak detection systems. This review preheced is a systematic synthesis of the latest five years of research in the field of deep learning, breaking down the progress of the main methods of deep learning and defining further research directions that will help to guarantee the sustainability and resilience of smart water grids.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2024-01-12

Issue

Volume 10, Issue 1, January-February-2024

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Sheriff Adepoju , " Deep Learning for Smart Water Grids: A Targeted Review of Leak Detection Technologies" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 10, Issue 1, pp.344-355, January-February-2024.