Cargando…
Machine-Learning Classification of a Number of Contaminant Sources in an Urban Water Network
In the case of a contamination event in water distribution networks, several studies have considered different methods to determine contamination scenario information. It would be greatly beneficial to know the exact number of contaminant injection locations since some methods can only be applied in...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7794947/ https://www.ncbi.nlm.nih.gov/pubmed/33401513 http://dx.doi.org/10.3390/s21010245 |
_version_ | 1783634328449712128 |
---|---|
author | Lučin, Ivana Grbčić, Luka Čarija, Zoran Kranjčević, Lado |
author_facet | Lučin, Ivana Grbčić, Luka Čarija, Zoran Kranjčević, Lado |
author_sort | Lučin, Ivana |
collection | PubMed |
description | In the case of a contamination event in water distribution networks, several studies have considered different methods to determine contamination scenario information. It would be greatly beneficial to know the exact number of contaminant injection locations since some methods can only be applied in the case of a single injection location and others have greater efficiency. In this work, the Neural Network and Random Forest classifying algorithms are used to predict the number of contaminant injection locations. The prediction model is trained with data obtained from simulated contamination event scenarios with random injection starting time, duration, concentration value, and the number of injection locations which varies from 1 to 4. Classification is made to determine if single or multiple injection locations occurred, and to predict the exact number of injection locations. Data was obtained for two different benchmark networks, medium-sized network Net3 and large-sized Richmond network. Additionally, an investigation of sensor layouts, demand uncertainty, and fuzzy sensors on model accuracy is conducted. The proposed approach shows excellent accuracy in predicting if single or multiple contaminant injections in a water supply network occurred and good accuracy for the exact number of injection locations. |
format | Online Article Text |
id | pubmed-7794947 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-77949472021-01-10 Machine-Learning Classification of a Number of Contaminant Sources in an Urban Water Network Lučin, Ivana Grbčić, Luka Čarija, Zoran Kranjčević, Lado Sensors (Basel) Article In the case of a contamination event in water distribution networks, several studies have considered different methods to determine contamination scenario information. It would be greatly beneficial to know the exact number of contaminant injection locations since some methods can only be applied in the case of a single injection location and others have greater efficiency. In this work, the Neural Network and Random Forest classifying algorithms are used to predict the number of contaminant injection locations. The prediction model is trained with data obtained from simulated contamination event scenarios with random injection starting time, duration, concentration value, and the number of injection locations which varies from 1 to 4. Classification is made to determine if single or multiple injection locations occurred, and to predict the exact number of injection locations. Data was obtained for two different benchmark networks, medium-sized network Net3 and large-sized Richmond network. Additionally, an investigation of sensor layouts, demand uncertainty, and fuzzy sensors on model accuracy is conducted. The proposed approach shows excellent accuracy in predicting if single or multiple contaminant injections in a water supply network occurred and good accuracy for the exact number of injection locations. MDPI 2021-01-01 /pmc/articles/PMC7794947/ /pubmed/33401513 http://dx.doi.org/10.3390/s21010245 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Lučin, Ivana Grbčić, Luka Čarija, Zoran Kranjčević, Lado Machine-Learning Classification of a Number of Contaminant Sources in an Urban Water Network |
title | Machine-Learning Classification of a Number of Contaminant Sources in an Urban Water Network |
title_full | Machine-Learning Classification of a Number of Contaminant Sources in an Urban Water Network |
title_fullStr | Machine-Learning Classification of a Number of Contaminant Sources in an Urban Water Network |
title_full_unstemmed | Machine-Learning Classification of a Number of Contaminant Sources in an Urban Water Network |
title_short | Machine-Learning Classification of a Number of Contaminant Sources in an Urban Water Network |
title_sort | machine-learning classification of a number of contaminant sources in an urban water network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7794947/ https://www.ncbi.nlm.nih.gov/pubmed/33401513 http://dx.doi.org/10.3390/s21010245 |
work_keys_str_mv | AT lucinivana machinelearningclassificationofanumberofcontaminantsourcesinanurbanwaternetwork AT grbcicluka machinelearningclassificationofanumberofcontaminantsourcesinanurbanwaternetwork AT carijazoran machinelearningclassificationofanumberofcontaminantsourcesinanurbanwaternetwork AT kranjceviclado machinelearningclassificationofanumberofcontaminantsourcesinanurbanwaternetwork |