Smart Cities, Volume 7, Issue 4
2024 August - 30 articles
Cover Story: This paper presents a novel pipeline leak detection system designed for smart cities. Pipeline leakage in urban areas leads to serious issues, including water wastage, environmental damage, and public safety risks. The proposed system used acoustic emission (AE) sensing combined with advanced time-series deep learning algorithms like LSTM, Bi-LSTM, and GRU to detect and classify leak sizes in real-time. This framework identifies various leak sizes in pipelines that carry liquids or gases, offering significant improvements over traditional methods. By enhancing monitoring and maintenance, this approach helps to overcome hazards, reduces economic losses, and ensures reliable urban infrastructure. The models have demonstrated high accuracy, making them ideal for real-world smart city applications. View this paper - Issues are regarded as officially published after their release is announced to the table of contents alert mailing list .
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