Matches in SemOpenAlex for { <https://semopenalex.org/work/W2290702960> ?p ?o ?g. }
Showing items 1 to 80 of
80
with 100 items per page.
- W2290702960 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 squared error in leak magnitude estimation (0.065 l/s)." @default.
- W2290702960 created "2016-06-24" @default.
- W2290702960 creator A5047020165 @default.
- W2290702960 creator A5052833933 @default.
- W2290702960 creator A5060644755 @default.
- W2290702960 date "2015-02-01" @default.
- W2290702960 modified "2023-09-23" @default.
- W2290702960 title "A Noise Scaled Semi Parametric Gaussian Process Model for Real Time Water Network Leak Detection in the Presence of Heteroscedasticity" @default.
- W2290702960 cites W1496871954 @default.
- W2290702960 cites W1503398984 @default.
- W2290702960 cites W1566916977 @default.
- W2290702960 cites W2007144263 @default.
- W2290702960 cites W2030465856 @default.
- W2290702960 cites W2041923730 @default.
- W2290702960 cites W2048523204 @default.
- W2290702960 cites W2102832680 @default.
- W2290702960 cites W2119437062 @default.
- W2290702960 cites W2122646361 @default.
- W2290702960 cites W2136816045 @default.
- W2290702960 cites W2148608678 @default.
- W2290702960 cites W2149764047 @default.
- W2290702960 cites W2162114812 @default.
- W2290702960 cites W2170078560 @default.
- W2290702960 cites W2171037198 @default.
- W2290702960 cites W2187471809 @default.
- W2290702960 hasPublicationYear "2015" @default.
- W2290702960 type Work @default.
- W2290702960 sameAs 2290702960 @default.
- W2290702960 citedByCount "0" @default.
- W2290702960 crossrefType "proceedings-article" @default.
- W2290702960 hasAuthorship W2290702960A5047020165 @default.
- W2290702960 hasAuthorship W2290702960A5052833933 @default.
- W2290702960 hasAuthorship W2290702960A5060644755 @default.
- W2290702960 hasConcept C115961682 @default.
- W2290702960 hasConcept C152745839 @default.
- W2290702960 hasConcept C154945302 @default.
- W2290702960 hasConcept C159390177 @default.
- W2290702960 hasConcept C172707124 @default.
- W2290702960 hasConcept C2988574769 @default.
- W2290702960 hasConcept C39432304 @default.
- W2290702960 hasConcept C41008148 @default.
- W2290702960 hasConcept C79403827 @default.
- W2290702960 hasConcept C99498987 @default.
- W2290702960 hasConceptScore W2290702960C115961682 @default.
- W2290702960 hasConceptScore W2290702960C152745839 @default.
- W2290702960 hasConceptScore W2290702960C154945302 @default.
- W2290702960 hasConceptScore W2290702960C159390177 @default.
- W2290702960 hasConceptScore W2290702960C172707124 @default.
- W2290702960 hasConceptScore W2290702960C2988574769 @default.
- W2290702960 hasConceptScore W2290702960C39432304 @default.
- W2290702960 hasConceptScore W2290702960C41008148 @default.
- W2290702960 hasConceptScore W2290702960C79403827 @default.
- W2290702960 hasConceptScore W2290702960C99498987 @default.
- W2290702960 hasLocation W22907029601 @default.
- W2290702960 hasOpenAccess W2290702960 @default.
- W2290702960 hasPrimaryLocation W22907029601 @default.
- W2290702960 hasRelatedWork W1514635699 @default.
- W2290702960 hasRelatedWork W1970925404 @default.
- W2290702960 hasRelatedWork W2139685685 @default.
- W2290702960 hasRelatedWork W2148187321 @default.
- W2290702960 hasRelatedWork W2185292989 @default.
- W2290702960 hasRelatedWork W2212655132 @default.
- W2290702960 hasRelatedWork W2342979346 @default.
- W2290702960 hasRelatedWork W2554210851 @default.
- W2290702960 hasRelatedWork W2578015020 @default.
- W2290702960 hasRelatedWork W2603866843 @default.
- W2290702960 hasRelatedWork W2750219942 @default.
- W2290702960 hasRelatedWork W2763635194 @default.
- W2290702960 hasRelatedWork W2782990763 @default.
- W2290702960 hasRelatedWork W2799155122 @default.
- W2290702960 hasRelatedWork W2799758636 @default.
- W2290702960 hasRelatedWork W2962882210 @default.
- W2290702960 hasRelatedWork W2996365800 @default.
- W2290702960 hasRelatedWork W3011329609 @default.
- W2290702960 hasRelatedWork W3193685497 @default.
- W2290702960 hasRelatedWork W2992893846 @default.
- W2290702960 isParatext "false" @default.
- W2290702960 isRetracted "false" @default.
- W2290702960 magId "2290702960" @default.
- W2290702960 workType "article" @default.