Merging Fluid Transient Waves and Artificial Neural Networks for Burst Detection and Identification in Pipelines-Reference-Cited by-同舟云学术

Merging Fluid Transient Waves and Artificial Neural Networks for Burst Detection and Identification in Pipelines

Published:2021-01 Issue:1 Volume:147 Page:04020097
ISSN:0733-9496
Container-title:Journal of Water Resources Planning and Management
language:en
Short-container-title:J. Water Resour. Plann. Manage.

Author:

Bohorquez Jessica¹^ORCID,Simpson Angus R.²,Lambert Martin F.³^ORCID,Alexander Bradley⁴^ORCID

Affiliation:

1. Ph.D. Candidate, School of Civil, Environmental and Mining Engineering, Univ. of Adelaide, Adelaide 5005, Australia. ORCID: .

2. Professor, School of Civil, Environmental and Mining Engineering, Univ. of Adelaide, Adelaide 5005, Australia (corresponding author).

3. Professor, School of Civil, Environmental and Mining Engineering, Univ. of Adelaide, Adelaide 5005, Australia. ORCID: .

4. Senior Lecturer, School of Computer Science, Univ. of Adelaide, Adelaide 5005, Australia. ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

Management, Monitoring, Policy and Law,Water Science and Technology,Geography, Planning and Development,Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29WR.1943-5452.0001296

Reference45 articles.

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3. Implicit state-estimation technique for water network monitoring

4. Leak Detection and Topology Identification in Pipelines Using Fluid Transients and Artificial Neural Networks

5. Bohorquez J. A. R. Simpson and M. F. Lambert. 2018. “Characterization of transient pressure traces due to the effects of different anomalies and features in water pipelines.” In Proc. 13th Pressure Surges Conf. 151–169. Canfield UK: BHR Group.

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