Burst Detection in District Metering Areas Using Deep Learning Method-Reference-Cited by-同舟云学术

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Burst Detection in District Metering Areas Using Deep Learning Method

Published:2020-06 Issue:6 Volume:146 Page:04020031
ISSN:0733-9496
Container-title:Journal of Water Resources Planning and Management
language:en
Short-container-title:J. Water Resour. Plann. Manage.

Author:

Wang Xiaoting¹,Guo Guancheng¹,Liu Shuming²,Wu Yipeng¹,Xu Xiyan³,Smith Kate³

Affiliation:

1. Ph.D. Student, Smart Water Research Center, School of Environment, Tsinghua Univ., Beijing 100084, China.

2. Professor, Smart Water Research Center, School of Environment, Tsinghua Univ., Beijing, 100084, China (corresponding author).

3. Postdoctoral Fellow, Smart Water Research Center, School of Environment, Tsinghua Univ., Beijing 100084, China.

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.1943-5452.0001223

Reference40 articles.

1. Understanding diagnostic tests – Part 3: Receiver operating characteristic curves

2. Analysis of historic bursts and burst detection in water supply areas of different size

3. Heuristic burst detection method using flow and pressure measurements

4. Forecasting Hourly Water Demands With Seasonal Autoregressive Models for Real‐Time Application

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