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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| 主要な著者: | , , , |
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| フォーマット: | 論文 |
| 言語: | English |
| 出版事項: |
Elsevier
2015
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| オンライン･アクセス: | 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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| 要約: | 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 |
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