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...
| Main Authors: | , , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2019
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/107010 http://hdl.handle.net/10220/49034 |
| _version_ | 1826124585210216448 |
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| author | Le, Viet Thang Quan, Nguyen Hong Loc, Ho Huu Duyen, Nguyen Thi Thanh Dung, Tran Duc Nguyen, Hiep Duc Do, Quang Hung |
| 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 |
| author_sort | Le, Viet Thang |
| collection | NTU |
| 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. |
| first_indexed | 2024-10-01T06:22:48Z |
| format | Journal Article |
| id | ntu-10356/107010 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:22:48Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-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 |
| 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 |
| title | 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_short | 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 |
| topic | DRNTU::Engineering::Environmental engineering Water Quality Temporal and Spatial Assessment |
| url | https://hdl.handle.net/10356/107010 http://hdl.handle.net/10220/49034 |
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