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Title: A multidisciplinary approach for evaluating spatial and temporal variations in water quality
Authors: Le, Viet Thang
Quan, Nguyen Hong
Loc, Ho Huu
Duyen, Nguyen Thi Thanh
Dung, Tran Duc
Nguyen, Hiep Duc
Do, Quang Hung
Keywords: DRNTU::Engineering::Environmental engineering
Water Quality
Temporal and Spatial Assessment
Issue Date: 2019
Source: 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
Series/Report no.: Water
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.
ISSN: 2073-4441
DOI: 10.3390/w11040853
Rights: © 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 (
Fulltext Permission: open
Fulltext Availability: With Fulltext
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