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An Enhanced K-Means Algorithm for Water Quality Analysis of The Haihe River in China

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  • Hui Zou

    (School of Economics and Management, Beihang University, Beijing 100191, China
    School of Science, China Agricultural University, Beijing 100083, China)

  • Zhihong Zou

    (School of Economics and Management, Beihang University, Beijing 100191, China)

  • Xiaojing Wang

    (School of Economics and Management, Beihang University, Beijing 100191, China)

Abstract

The increase and the complexity of data caused by the uncertain environment is today’s reality. In order to identify water quality effectively and reliably, this paper presents a modified fast clustering algorithm for water quality analysis. The algorithm has adopted a varying weights K-means cluster algorithm to analyze water monitoring data. The varying weights scheme was the best weighting indicator selected by a modified indicator weight self-adjustment algorithm based on K-means, which is named MIWAS-K-means. The new clustering algorithm avoids the margin of the iteration not being calculated in some cases. With the fast clustering analysis, we can identify the quality of water samples. The algorithm is applied in water quality analysis of the Haihe River (China) data obtained by the monitoring network over a period of eight years (2006–2013) with four indicators at seven different sites (2078 samples). Both the theoretical and simulated results demonstrate that the algorithm is efficient and reliable for water quality analysis of the Haihe River. In addition, the algorithm can be applied to more complex data matrices with high dimensionality.

Suggested Citation

  • Hui Zou & Zhihong Zou & Xiaojing Wang, 2015. "An Enhanced K-Means Algorithm for Water Quality Analysis of The Haihe River in China," IJERPH, MDPI, vol. 12(11), pages 1-14, November.
    • Handle: RePEc:gam:jijerp:v:12:y:2015:i:11:p:14400-14413:d:58730
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    References listed on IDEAS

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    1. Antonio Mucherino & Petraq J. Papajorgji & Panos M. Pardalos, 2009. "Data Mining in Agriculture," Springer Optimization and Its Applications, Springer, number 978-0-387-88615-2, December.
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    Cited by:

    1. Piasecki Adam & Pilarska Agnieszka & Golba Radosław, 2021. "The potential of data exploration methods in identifying the relationship between short-period (daily) water consumption and meteorological factors," Bulletin of Geography. Socio-economic Series, Sciendo, vol. 54(54), pages 113-122, December.
    2. Dana Rad & Lavinia Denisia Cuc & Ramona Lile & Valentina E. Balas & Cornel Barna & Mioara Florina Pantea & Graziella Corina Bâtcă-Dumitru & Silviu Gabriel Szentesi & Gavril Rad, 2022. "A Cognitive Systems Engineering Approach Using Unsupervised Fuzzy C-Means Technique, Exploratory Factor Analysis and Network Analysis—A Preliminary Statistical Investigation of the Bean Counter Profil," IJERPH, MDPI, vol. 19(19), pages 1-19, October.

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