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A Multidisciplinary Approach for Evaluating Spatial and Temporal Variations in Water Quality

1
Institute of Environment Science, Engineering and Management, Industrial University of Ho Chi Minh City, 12 Nguyen Van Bao Street, Go Vap District, Ho Chi Minh City 700000, Vietnam
2
Center for Water Management and Climate Change, Vietnam National University, Ho Chi Minh City 700000, Vietnam
3
Nanyang Environment & Water Research Institute, Nanyang Technological University, 1 Cleantech Loop, CleanTech One, Singapore #06-08637141, Singapore
4
NTT Hi-Tech Institute, Nguyen Tat Thanh University, Ho Chi Minh City 700000, Vietnam
5
Environmental Quality, Atmospheric Science and Climate Change Research Group, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
6
Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
7
Faculty of Information Technology, University of Transport Technology, Hanoi 100000, Vietnam
*
Author to whom correspondence should be addressed.
Corresponding author is currently at Nanyang Environment & Water Research Institute, Nanyang Technological University, Singapore (NEWRI/NTU).
Water 2019, 11(4), 853; https://doi.org/10.3390/w11040853
Received: 14 March 2019 / Revised: 8 April 2019 / Accepted: 12 April 2019 / Published: 24 April 2019
(This article belongs to the Section Water Quality and Ecosystems)
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Abstract

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. View Full-Text
Keywords: water quality; temporal and spatial assessment; multilayer perceptron (MLP) network; radial basis function (RBF) network; decision tree (DT) water quality; temporal and spatial assessment; multilayer perceptron (MLP) network; radial basis function (RBF) network; decision tree (DT)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Le, V.T.; Quan, N.H.; Loc, H.H.; Thanh Duyen, N.T.; Dung, T.D.; Nguyen, H.D.; Do, Q.H. A Multidisciplinary Approach for Evaluating Spatial and Temporal Variations in Water Quality. Water 2019, 11, 853.

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