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    Comparative Analysis of Machine Learning and Deep Learning Based Water Pipeline Leak Detection Using EDFL Sensor 

    Source: Journal of Pipeline Systems Engineering and Practice:;2023:;Volume ( 014 ):;issue: 004:;page 04023026-1
    Author(s): Uma Rajasekaran; Mohanaprasad Kothandaraman
    Publisher: ASCE
    Abstract: A pipeline is the most efficient way to transport water from one place to another. Due to aging, corrosion, and external factors, the pipeline is prone to damage, which causes leaks. Many machine learning (ML) and deep ...
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    Water Pipeline Leakage Recognition and Localization Using Machine Learning and Deep Learning Techniques 

    Source: Journal of Pipeline Systems Engineering and Practice:;2025:;Volume ( 016 ):;issue: 003:;page 04025035-1
    Author(s): Ali Asgar Chandanwala; Srutakirti Bhowmik; Parna Chaudhury; Uma Rajasekaran; J. Jean Jenifer Nesam; Mohanaprasad Kothandaraman
    Publisher: American Society of Civil Engineers
    Abstract: Water distribution systems often face problems with leaks, causing water loss and environmental worries. In literature, applications of machine learning (ML) and deep learning (DL) algorithms in detecting a pipeline leak ...
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    Adaptive Independent Component Analysis–Based Cross-Correlation Techniques along with Empirical Mode Decomposition for Water Pipeline Leakage Localization Utilizing Acousto-Optic Sensors 

    Source: Journal of Pipeline Systems Engineering and Practice:;2020:;Volume ( 011 ):;issue: 003
    Author(s): Mohanaprasad Kothandaraman; Zijian Law; Morris A. G. Ezra; Chang Hong Pua
    Publisher: ASCE
    Abstract: The leak localization in a water pipeline system plays a vital role in pipeline system modeling. The leak location is identified by estimating the time difference between two sensor signals placed on either side of the ...
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    DSpace software copyright © 2002-2015  DuraSpace
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