Spatio-Temporal Feature Extraction for Pipeline Leak Detection in Smart Cities Using Acoustic Emission Signals: A One-Dimensional Hybrid Convolutional Neural Network–Long Short-Term Memory Approach

Spatio-Temporal Feature Extraction for Pipeline Leak Detection in Smart Cities Using Acoustic Emission Signals: A One-Dimensional Hybrid Convolutional Neural Network–Long Short-Term Memory Approach

Pipeline leakage represents a critical challenge in smart cities and various industries, leading to severe economic, environmental, and safety consequences. Early detection of leaks is essential for overcoming these risks and ensuring the safe operation of pipeline systems. In this study, a hybrid c...

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Bibliographic Details
Main Authors: Saif Ullah, Niamat Ullah, Muhammad Farooq Siddique, Zahoor Ahmad, Jong-Myon Kim
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Applied Sciences
Subjects:
acoustic emission
leak detection
Savitzky–Golay
convolutional neural network
long short-term memory
hybrid model
Online Access:https://www.mdpi.com/2076-3417/14/22/10339
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