Characterization of water quality conditions in the Klang River Basin, Malaysia using self organizing map and K-means algorithm

被引：27

作者：

Sharif, Sharifah Mohd. ^{[1
]}

Kusin, Faradiella Mohd. ^{[1
,2
]}

Asha'ari, Zulfa Hanan ^{[1
]}

Aris, Ahmad Zaharin ^{[1
,2
]}

机构：

[1] Univ Putra Malaysia, Fac Environm Studies, Dept Environm Sci, Serdang 43400, Malaysia

[2] Univ Putra Malaysia, Fac Environm Studies, Environm Forens Res Ctr, Upm Serdang 43400, Malaysia

来源：

ENVIRONMENTAL FORENSICS 2015 | 2015年 / 30卷

关键词：

Water quality; spatiotemporal pattern; pollution sources; machine learning; self organizing map; K-means;

D O I：

10.1016/j.proenv.2015.10.013

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

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. (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license.

引用

页码：73 / 78

页数：6

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