A multidisciplinary approach for evaluating spatial and temporal variations in water quality

The primary goal of this study is to investigate the classification capability of several artificial intelligence techniques, including the decision tree (DT), multilayer perceptron (MLP) network, Naïve Bayes, radial basis function (RBF) network, and support vector machine (SVM) for evaluating spati...

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Main Authors: Le, Viet Thang, Quan, Nguyen Hong, Loc, Ho Huu, Duyen, Nguyen Thi Thanh, Dung, Tran Duc, Nguyen, Hiep Duc, Do, Quang Hung
Other Authors: Nanyang Environment and Water Research Institute
Format: Article
Language:English
Published: 2019
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Online Access:https://hdl.handle.net/10356/107010
http://hdl.handle.net/10220/49034
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1070102020-09-26T22:01:00Z A multidisciplinary approach for evaluating spatial and temporal variations in water quality Le, Viet Thang Quan, Nguyen Hong Loc, Ho Huu Duyen, Nguyen Thi Thanh Dung, Tran Duc Nguyen, Hiep Duc Do, Quang Hung Nanyang Environment and Water Research Institute DRNTU::Engineering::Environmental engineering Water Quality Temporal and Spatial Assessment The primary goal of this study is to investigate the classification capability of several artificial intelligence techniques, including the decision tree (DT), multilayer perceptron (MLP) network, Naïve Bayes, radial basis function (RBF) network, and support vector machine (SVM) for evaluating spatial and temporal variations in water quality. The application case is the Song Quao-Ca Giang (SQ-CG) water system, a main domestic water supply source of the city of Phan Thiet in Binh Thuan province, Vietnam. To evaluate the water quality condition of the source, the government agency has initiated an extensive sampling project, collecting samples from 43 locations covering the SQ reservoir, the main canals, and the surrounding areas during 2015–2016. Different classifying models based on artificial intelligence techniques were developed to analyze the sampling data after the performances of the models were evaluated and compared using the confusion matrix, accuracy rate, and several error indexes. The results show that machine-learning techniques can be used to explicitly evaluate spatial and temporal variations in water quality. Published version 2019-07-01T03:31:38Z 2019-12-06T22:22:59Z 2019-07-01T03:31:38Z 2019-12-06T22:22:59Z 2019 Journal Article Le, V. T., Quan, N. H., Loc, H. H., Duyen, N. T. T., Dung, T. D., Nguyen, H. D., & Do, Q. H. (2019). A multidisciplinary approach for evaluating spatial and temporal variations in water quality. Water, 11(4), 853-. doi:10.3390/w11040853 2073-4441 https://hdl.handle.net/10356/107010 http://hdl.handle.net/10220/49034 10.3390/w11040853 en Water © 2019 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/). 16 p. application/pdf
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic DRNTU::Engineering::Environmental engineering
Water Quality
Temporal and Spatial Assessment
spellingShingle DRNTU::Engineering::Environmental engineering
Water Quality
Temporal and Spatial Assessment
Le, Viet Thang
Quan, Nguyen Hong
Loc, Ho Huu
Duyen, Nguyen Thi Thanh
Dung, Tran Duc
Nguyen, Hiep Duc
Do, Quang Hung
A multidisciplinary approach for evaluating spatial and temporal variations in water quality
description The primary goal of this study is to investigate the classification capability of several artificial intelligence techniques, including the decision tree (DT), multilayer perceptron (MLP) network, Naïve Bayes, radial basis function (RBF) network, and support vector machine (SVM) for evaluating spatial and temporal variations in water quality. The application case is the Song Quao-Ca Giang (SQ-CG) water system, a main domestic water supply source of the city of Phan Thiet in Binh Thuan province, Vietnam. To evaluate the water quality condition of the source, the government agency has initiated an extensive sampling project, collecting samples from 43 locations covering the SQ reservoir, the main canals, and the surrounding areas during 2015–2016. Different classifying models based on artificial intelligence techniques were developed to analyze the sampling data after the performances of the models were evaluated and compared using the confusion matrix, accuracy rate, and several error indexes. The results show that machine-learning techniques can be used to explicitly evaluate spatial and temporal variations in water quality.
author2 Nanyang Environment and Water Research Institute
author_facet Nanyang Environment and Water Research Institute
Le, Viet Thang
Quan, Nguyen Hong
Loc, Ho Huu
Duyen, Nguyen Thi Thanh
Dung, Tran Duc
Nguyen, Hiep Duc
Do, Quang Hung
format Article
author Le, Viet Thang
Quan, Nguyen Hong
Loc, Ho Huu
Duyen, Nguyen Thi Thanh
Dung, Tran Duc
Nguyen, Hiep Duc
Do, Quang Hung
author_sort Le, Viet Thang
title A multidisciplinary approach for evaluating spatial and temporal variations in water quality
title_short A multidisciplinary approach for evaluating spatial and temporal variations in water quality
title_full A multidisciplinary approach for evaluating spatial and temporal variations in water quality
title_fullStr A multidisciplinary approach for evaluating spatial and temporal variations in water quality
title_full_unstemmed A multidisciplinary approach for evaluating spatial and temporal variations in water quality
title_sort multidisciplinary approach for evaluating spatial and temporal variations in water quality
publishDate 2019
url https://hdl.handle.net/10356/107010
http://hdl.handle.net/10220/49034
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