Detailed Leak Localization in Water Distribution Networks Using Random Forest Classifier and Pipe Segmentation

In this paper, a Random Forest classifier was used to predict leak locations for two differently sized water distribution networks based on pressure sensor measurements. The prediction model is trained on simulated leak scenarios with randomly chosen parameters - leak location, leak size, and base n...

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Autores principales:	Ivana Lucin, Zoran Carija, Sinisa Druzeta, Boze Lucin
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Leak localization pipe segmentation prediction modeling random forest water distribution networks Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/0e0e7ae92a1e4d30aed9903890073c38
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spelling	oai:doaj.org-article:0e0e7ae92a1e4d30aed9903890073c382021-11-26T00:00:33ZDetailed Leak Localization in Water Distribution Networks Using Random Forest Classifier and Pipe Segmentation2169-353610.1109/ACCESS.2021.3129703https://doaj.org/article/0e0e7ae92a1e4d30aed9903890073c382021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9622760/https://doaj.org/toc/2169-3536In this paper, a Random Forest classifier was used to predict leak locations for two differently sized water distribution networks based on pressure sensor measurements. The prediction model is trained on simulated leak scenarios with randomly chosen parameters - leak location, leak size, and base node demand uncertainty. Leak localization methods found in literature that rely on numerical simulations can only predict network nodes as leak nodes; however, since a leak can occur at any point along a pipe segment, additional spatial discretization of suspect pipe is proposed in this paper. It was observed that pipe segmentation of the whole network is a non-feasible approach since it rapidly increases the number of potential leak locations, consequently increasing the complexity of the prediction model. Therefore, a novel approach is proposed, in which a prediction model is trained on scenarios with leaks occurring in original network nodes only, but with its accuracy assessed against pressure sensor measurements from scenarios in which leaks occur in points between network nodes. It was observed that this approach can successfully narrow down the suspect leak area and, followed by additional segmentation of that network area and subsequent prediction, a precise leak localization can be achieved. The proposed approach enables incorporation of various uncertainties by simulating leak scenarios under different conditions. Investigation of leak size uncertainty and base demand variation showed that several different scenarios can produce similar sensor measurements which makes it difficult to unambiguously determine leak location using the prediction model. Therefore, future approaches of coupling prediction modeling with optimization methods are proposed.Ivana LucinZoran CarijaSinisa DruzetaBoze LucinIEEEarticleLeak localizationpipe segmentationprediction modelingrandom forestwater distribution networksElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 155113-155122 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Leak localization pipe segmentation prediction modeling random forest water distribution networks Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Leak localization pipe segmentation prediction modeling random forest water distribution networks Electrical engineering. Electronics. Nuclear engineering TK1-9971 Ivana Lucin Zoran Carija Sinisa Druzeta Boze Lucin Detailed Leak Localization in Water Distribution Networks Using Random Forest Classifier and Pipe Segmentation
description	In this paper, a Random Forest classifier was used to predict leak locations for two differently sized water distribution networks based on pressure sensor measurements. The prediction model is trained on simulated leak scenarios with randomly chosen parameters - leak location, leak size, and base node demand uncertainty. Leak localization methods found in literature that rely on numerical simulations can only predict network nodes as leak nodes; however, since a leak can occur at any point along a pipe segment, additional spatial discretization of suspect pipe is proposed in this paper. It was observed that pipe segmentation of the whole network is a non-feasible approach since it rapidly increases the number of potential leak locations, consequently increasing the complexity of the prediction model. Therefore, a novel approach is proposed, in which a prediction model is trained on scenarios with leaks occurring in original network nodes only, but with its accuracy assessed against pressure sensor measurements from scenarios in which leaks occur in points between network nodes. It was observed that this approach can successfully narrow down the suspect leak area and, followed by additional segmentation of that network area and subsequent prediction, a precise leak localization can be achieved. The proposed approach enables incorporation of various uncertainties by simulating leak scenarios under different conditions. Investigation of leak size uncertainty and base demand variation showed that several different scenarios can produce similar sensor measurements which makes it difficult to unambiguously determine leak location using the prediction model. Therefore, future approaches of coupling prediction modeling with optimization methods are proposed.
format	article
author	Ivana Lucin Zoran Carija Sinisa Druzeta Boze Lucin
author_facet	Ivana Lucin Zoran Carija Sinisa Druzeta Boze Lucin
author_sort	Ivana Lucin
title	Detailed Leak Localization in Water Distribution Networks Using Random Forest Classifier and Pipe Segmentation
title_short	Detailed Leak Localization in Water Distribution Networks Using Random Forest Classifier and Pipe Segmentation
title_full	Detailed Leak Localization in Water Distribution Networks Using Random Forest Classifier and Pipe Segmentation
title_fullStr	Detailed Leak Localization in Water Distribution Networks Using Random Forest Classifier and Pipe Segmentation
title_full_unstemmed	Detailed Leak Localization in Water Distribution Networks Using Random Forest Classifier and Pipe Segmentation
title_sort	detailed leak localization in water distribution networks using random forest classifier and pipe segmentation
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/0e0e7ae92a1e4d30aed9903890073c38
work_keys_str_mv	AT ivanalucin detailedleaklocalizationinwaterdistributionnetworksusingrandomforestclassifierandpipesegmentation AT zorancarija detailedleaklocalizationinwaterdistributionnetworksusingrandomforestclassifierandpipesegmentation AT sinisadruzeta detailedleaklocalizationinwaterdistributionnetworksusingrandomforestclassifierandpipesegmentation AT bozelucin detailedleaklocalizationinwaterdistributionnetworksusingrandomforestclassifierandpipesegmentation
_version_	1718409947925970944

Detailed Leak Localization in Water Distribution Networks Using Random Forest Classifier and Pipe Segmentation

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