Characterization of water quality conditions in the Klang River Basin, Malaysia using self organizing map and K-means algorithm
This study aimed to determine the spatiotemporal pattern of the water quality data and identifying the sources of pollution in the Klang River Basin. The self organizing map (SOM) combined with the K-means algorithm arranged the data based on the relationships of 25 variables. The data from 2006 to...
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Elsevier
2015
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my.upm.eprints.431962016-05-03T05:40:46Z http://psasir.upm.edu.my/id/eprint/43196/ Characterization of water quality conditions in the Klang River Basin, Malaysia using self organizing map and K-means algorithm Mohd Sharif, Sharifah Mohd Kusin, Faradiella Asha’ari, Zulfa Hanan Aris, Ahmad Zaharin This study aimed to determine the spatiotemporal pattern of the water quality data and identifying the sources of pollution in the Klang River Basin. The self organizing map (SOM) combined with the K-means algorithm arranged the data based on the relationships of 25 variables. The data from 2006 to 2009 for 30 monitoring stations were classified into six clusters. Water pollution in this river basin originated primarily from urban runoff, construction sites, faulty septic systems and industrial activities. The application of machine learning approaches is highly recommended to extract valuable information from the data for a holistic river basin management Elsevier 2015 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/43196/1/1-s2.0-S1878029615006076-main.pdf Mohd Sharif, Sharifah and Mohd Kusin, Faradiella and Asha’ari, Zulfa Hanan and Aris, Ahmad Zaharin (2015) Characterization of water quality conditions in the Klang River Basin, Malaysia using self organizing map and K-means algorithm. Procedia Environmental Sciences, 30. pp. 73-78. ISSN 1878-0296 http://www.sciencedirect.com/science/article/pii/S1878029615006076 10.1016/j.proenv.2015.10.013 |
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Malaysia |
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Universiti Putra Malaysia |
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English |
| description |
This study aimed to determine the spatiotemporal pattern of the water quality data and identifying the sources of pollution in the Klang River Basin. The self organizing map (SOM) combined with the K-means algorithm arranged the data based on the relationships of 25 variables. The data from 2006 to 2009 for 30 monitoring stations were classified into six clusters. Water pollution in this river basin originated primarily from urban runoff, construction sites, faulty septic systems and industrial activities. The application of machine learning approaches is highly recommended to extract valuable information from the data for a holistic river basin management |
| format |
Article |
| author |
Mohd Sharif, Sharifah Mohd Kusin, Faradiella Asha’ari, Zulfa Hanan Aris, Ahmad Zaharin |
| spellingShingle |
Mohd Sharif, Sharifah Mohd Kusin, Faradiella Asha’ari, Zulfa Hanan Aris, Ahmad Zaharin Characterization of water quality conditions in the Klang River Basin, Malaysia using self organizing map and K-means algorithm |
| author_facet |
Mohd Sharif, Sharifah Mohd Kusin, Faradiella Asha’ari, Zulfa Hanan Aris, Ahmad Zaharin |
| author_sort |
Mohd Sharif, Sharifah |
| title |
Characterization of water quality conditions in the Klang River Basin, Malaysia using self organizing map and K-means algorithm |
| title_short |
Characterization of water quality conditions in the Klang River Basin, Malaysia using self organizing map and K-means algorithm |
| title_full |
Characterization of water quality conditions in the Klang River Basin, Malaysia using self organizing map and K-means algorithm |
| title_fullStr |
Characterization of water quality conditions in the Klang River Basin, Malaysia using self organizing map and K-means algorithm |
| title_full_unstemmed |
Characterization of water quality conditions in the Klang River Basin, Malaysia using self organizing map and K-means algorithm |
| title_sort |
characterization of water quality conditions in the klang river basin, malaysia using self organizing map and k-means algorithm |
| publisher |
Elsevier |
| publishDate |
2015 |
| url |
http://psasir.upm.edu.my/id/eprint/43196/1/1-s2.0-S1878029615006076-main.pdf http://psasir.upm.edu.my/id/eprint/43196/ http://www.sciencedirect.com/science/article/pii/S1878029615006076 |
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1643833494536716288 |
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13.211869 |