Machine-Learning–Based Risk Assessment Method for Leak Detection and Geolocation in a Water Distribution System-Reference-Cited by-同舟云学术

Machine-Learning–Based Risk Assessment Method for Leak Detection and Geolocation in a Water Distribution System

Published:2020-03 Issue:1 Volume:26 Page:04019039
ISSN:1076-0342
Container-title:Journal of Infrastructure Systems
language:en
Short-container-title:J. Infrastruct. Syst.

Author:

Cantos Wilmer P.¹,Juran Ilan²,Tinelli Silvia³

Affiliation:

1. Artificial Intelligence Research Fellow, Tandon School of Engineering, New York Univ., Urban Infrastructure Systems, 15 MetroTech Center, Brooklyn, NY 11201 (corresponding author).

2. Professor, Tandon School of Engineering, New York Univ., Urban Infrastructure Systems, 15 MetroTech Center, Brooklyn, NY 11201.

3. Research Associate, W-SMART Association C/O LPG PARIS, 9 Villa Wagram Saint Honoré, Paris 75008, France.

Publisher

American Society of Civil Engineers (ASCE)

Subject

Civil and Structural Engineering

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29IS.1943-555X.0000517

Reference24 articles.

1. ASCE. 2017. “Report card for America’s infrastructure.” https://www.infrastructurereportcard.org/.

2. Cantos W. P. 2018. Artificial intelligence application for leak detection and geolocation in water distribution systems.” Doctoral dissertation New York Univ. Tandon School of Engineering.

3. Infrastructure aging risk assessment for water distribution systems

4. Cantos W. P. I. Juran and S. Tinelli. 2017. “Risk assessment for early water leak detection.” In Proc. Int. Conf. on Sustainable Infrastructure: Technology ICSI. Reston VA: ASCE.

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