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A noise scaled semi parametric gaussian process model for real time water network leak detection in the presence of heteroscedasticity

A noise scaled semi parametric gaussian process model for real time water network leak detection in the presence of heteroscedasticity
A noise scaled semi parametric gaussian process model for real time water network leak detection in the presence of heteroscedasticity
The timely detection of leaks in water distribution systems
is critical to the sustainable provision of clean water to consumers.
Increasingly, water companies are deploying remote
sensors to measure water flow in real-time in order to detect
such leaks. However, in practice, for typical District Metering
Zones (DMZ), financial constraints limit the number of
deployable real time flow sensors/meters to one or two, thus
constraining leak detection to be based on the aggregated
flow being monitored at these point. Such aggregated flow
data typically exhibits input signal dependence whereby both
noise and leaks are dependent on the flow being measured.
This limited monitoring and input signal dependance make
conventional approaches based on simple thresholds unreliable
for real time leak detection. To address this, we propose a
Gaussian process (GP) model with an additive diagonal noise
covariance that is able to handle the input dependant noise
observed in this setting. A parameterised mean step change
function is used to detect leaks and to estimate their size. Using
prior water distribution systems (WDS) knowledge we
dynamically bound and discretize the detection parameters of
the step change mean function, reducing and pruning the parameter
search space considerably. We evaluate the proposed
noise scaled GP (NSGP) against both the latest research work
on GP based fault detection methods and the current state of
the art and applied leak detection approaches in water distribution
systems. We show that our proposed method out performs
other approaches, on real water network data with synthetically
generated time varying leaks, with a detection accuracy
of 99%, almost zero false positive detections and the
lowest root mean
Obaid, Malik
aab431ed-5258-4238-8b74-5b4ae29f2cc8
Alex, Rogers
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Siddhartha, Ghosh
860b4bf9-1631-4d13-8968-29b1cd4347a3
Obaid, Malik
aab431ed-5258-4238-8b74-5b4ae29f2cc8
Alex, Rogers
f9130bc6-da32-474e-9fab-6c6cb8077fdc
Siddhartha, Ghosh
860b4bf9-1631-4d13-8968-29b1cd4347a3

Obaid, Malik, Alex, Rogers and Siddhartha, Ghosh (2015) A noise scaled semi parametric gaussian process model for real time water network leak detection in the presence of heteroscedasticity. Computational Sustainability Workshop at the Twenty-Ninth AAAI Conference on Artificial Intelligence, United States. 25 - 26 Jan 2015.

Record type: Conference or Workshop Item (Other)

Abstract

The timely detection of leaks in water distribution systems
is critical to the sustainable provision of clean water to consumers.
Increasingly, water companies are deploying remote
sensors to measure water flow in real-time in order to detect
such leaks. However, in practice, for typical District Metering
Zones (DMZ), financial constraints limit the number of
deployable real time flow sensors/meters to one or two, thus
constraining leak detection to be based on the aggregated
flow being monitored at these point. Such aggregated flow
data typically exhibits input signal dependence whereby both
noise and leaks are dependent on the flow being measured.
This limited monitoring and input signal dependance make
conventional approaches based on simple thresholds unreliable
for real time leak detection. To address this, we propose a
Gaussian process (GP) model with an additive diagonal noise
covariance that is able to handle the input dependant noise
observed in this setting. A parameterised mean step change
function is used to detect leaks and to estimate their size. Using
prior water distribution systems (WDS) knowledge we
dynamically bound and discretize the detection parameters of
the step change mean function, reducing and pruning the parameter
search space considerably. We evaluate the proposed
noise scaled GP (NSGP) against both the latest research work
on GP based fault detection methods and the current state of
the art and applied leak detection approaches in water distribution
systems. We show that our proposed method out performs
other approaches, on real water network data with synthetically
generated time varying leaks, with a detection accuracy
of 99%, almost zero false positive detections and the
lowest root mean

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e-pub ahead of print date: February 2015
Venue - Dates: Computational Sustainability Workshop at the Twenty-Ninth AAAI Conference on Artificial Intelligence, United States, 2015-01-25 - 2015-01-26
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 377951
URI: https://eprints.soton.ac.uk/id/eprint/377951
PURE UUID: 70e6bcde-3e88-48d8-b183-0e44c150c337

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Date deposited: 23 Jun 2015 10:53
Last modified: 19 Jul 2019 20:42

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