TY - GEN
T1 - Smart WDS management
T2 - 2017 IEEE SmartWorld Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017
AU - Borges, Anastassia Paula Andrade
AU - Jung, Donghwi
AU - Kim, Joong Hoon
PY - 2018/6/26
Y1 - 2018/6/26
N2 - Recently, advanced and smart techniques are being implemented for improving water distribution system (WDS) management and control. Those methods are mostly based on field data measured in real-Time throughout the system of bigdata characteristics especially with respect to its volume and velocity. An interesting research issue is to investigate how to extract useful information from big data for efficient WDS management and control (e.g., pipe burst and leakage detection). This study applies the Western Electric Company (WEC) method, a statistical process control method, for pipe burst detection which plots field data measured in real-Time around control limits obtained from historical normal field measurements. We investigate the impact of meter location and the number of meters on pipe burst detectability (i.e., detection probability and false alarm rate). Control and out-of-control pipe flow data are synthetically generated by using a hydraulic model of the Austin network and simulating pipe bursts under stochastic demand conditions.
AB - Recently, advanced and smart techniques are being implemented for improving water distribution system (WDS) management and control. Those methods are mostly based on field data measured in real-Time throughout the system of bigdata characteristics especially with respect to its volume and velocity. An interesting research issue is to investigate how to extract useful information from big data for efficient WDS management and control (e.g., pipe burst and leakage detection). This study applies the Western Electric Company (WEC) method, a statistical process control method, for pipe burst detection which plots field data measured in real-Time around control limits obtained from historical normal field measurements. We investigate the impact of meter location and the number of meters on pipe burst detectability (i.e., detection probability and false alarm rate). Control and out-of-control pipe flow data are synthetically generated by using a hydraulic model of the Austin network and simulating pipe bursts under stochastic demand conditions.
KW - burst detection
KW - water distribution systems
KW - WEC rules
UR - http://www.scopus.com/inward/record.url?scp=85050203454&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050203454&partnerID=8YFLogxK
U2 - 10.1109/UIC-ATC.2017.8397573
DO - 10.1109/UIC-ATC.2017.8397573
M3 - Conference contribution
AN - SCOPUS:85050203454
T3 - 2017 IEEE SmartWorld Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017 - Conference Proceedings
SP - 1
EP - 4
BT - 2017 IEEE SmartWorld Ubiquitous Intelligence and Computing, Advanced and Trusted Computed, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovation, SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI 2017 - Conference Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 4 April 2017 through 8 April 2017
ER -