Prediction of Bacterial Contamination Outbursts in Water Wells through Sparse Coding

Abstract Maintaining water quality is critical for any water distribution company. One of the major concerns in water quality assurance, is bacterial contamination in water sources. To date, bacteria growth models cannot predict with sufficient accuracy when a bacteria outburst will occur in a water...

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Autores principales:	Levi Frolich, Dalit Vaizel-Ohayon, Barak Fishbain
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c5f6686f428e4be19f0202d15ddc488a
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spelling	oai:doaj.org-article:c5f6686f428e4be19f0202d15ddc488a2021-12-02T16:06:03ZPrediction of Bacterial Contamination Outbursts in Water Wells through Sparse Coding10.1038/s41598-017-00830-42045-2322https://doaj.org/article/c5f6686f428e4be19f0202d15ddc488a2017-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-00830-4https://doaj.org/toc/2045-2322Abstract Maintaining water quality is critical for any water distribution company. One of the major concerns in water quality assurance, is bacterial contamination in water sources. To date, bacteria growth models cannot predict with sufficient accuracy when a bacteria outburst will occur in a water well. This is partly due to the natural sparsity of the bacteria count time series, which hinders the observation of deviations from normal behavior. This precludes the application of mathematical models nor statistical quality control methods for the detection of high bacteria counts before contamination occurs. As a result, currently a future outbreak prediction is a subjective process. This research developed a new cost-effective method that capitalizes on the sparsity of the bacteria count time series. The presented method first transforms the data into its spectral representation, where it is no longer sparse. Capitalizing on the spectral representation the dimensions of the problem are reduced. Machine learning methods are then applied on the reduced representations for predicting bacteria outbursts from the bacterial counts history of a well. The results show that these tools can be implemented by the water quality engineering community to create objective, more robust, quality control techniques to ensure safer water distribution.Levi FrolichDalit Vaizel-OhayonBarak FishbainNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-11 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Levi Frolich Dalit Vaizel-Ohayon Barak Fishbain Prediction of Bacterial Contamination Outbursts in Water Wells through Sparse Coding
description	Abstract Maintaining water quality is critical for any water distribution company. One of the major concerns in water quality assurance, is bacterial contamination in water sources. To date, bacteria growth models cannot predict with sufficient accuracy when a bacteria outburst will occur in a water well. This is partly due to the natural sparsity of the bacteria count time series, which hinders the observation of deviations from normal behavior. This precludes the application of mathematical models nor statistical quality control methods for the detection of high bacteria counts before contamination occurs. As a result, currently a future outbreak prediction is a subjective process. This research developed a new cost-effective method that capitalizes on the sparsity of the bacteria count time series. The presented method first transforms the data into its spectral representation, where it is no longer sparse. Capitalizing on the spectral representation the dimensions of the problem are reduced. Machine learning methods are then applied on the reduced representations for predicting bacteria outbursts from the bacterial counts history of a well. The results show that these tools can be implemented by the water quality engineering community to create objective, more robust, quality control techniques to ensure safer water distribution.
format	article
author	Levi Frolich Dalit Vaizel-Ohayon Barak Fishbain
author_facet	Levi Frolich Dalit Vaizel-Ohayon Barak Fishbain
author_sort	Levi Frolich
title	Prediction of Bacterial Contamination Outbursts in Water Wells through Sparse Coding
title_short	Prediction of Bacterial Contamination Outbursts in Water Wells through Sparse Coding
title_full	Prediction of Bacterial Contamination Outbursts in Water Wells through Sparse Coding
title_fullStr	Prediction of Bacterial Contamination Outbursts in Water Wells through Sparse Coding
title_full_unstemmed	Prediction of Bacterial Contamination Outbursts in Water Wells through Sparse Coding
title_sort	prediction of bacterial contamination outbursts in water wells through sparse coding
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/c5f6686f428e4be19f0202d15ddc488a
work_keys_str_mv	AT levifrolich predictionofbacterialcontaminationoutburstsinwaterwellsthroughsparsecoding AT dalitvaizelohayon predictionofbacterialcontaminationoutburstsinwaterwellsthroughsparsecoding AT barakfishbain predictionofbacterialcontaminationoutburstsinwaterwellsthroughsparsecoding
_version_	1718385175122935808

Prediction of Bacterial Contamination Outbursts in Water Wells through Sparse Coding

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