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Descriptive Characteristics of Surface Water Quality in Hong Kong by a Self-Organising Map

Author

Listed:
  • Yan An

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

  • Zhihong Zou

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

  • Ranran Li

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

Abstract

In this study, principal component analysis (PCA) and a self-organising map (SOM) were used to analyse a complex dataset obtained from the river water monitoring stations in the Tolo Harbor and Channel Water Control Zone (Hong Kong), covering the period of 2009–2011. PCA was initially applied to identify the principal components (PCs) among the nonlinear and complex surface water quality parameters. SOM followed PCA, and was implemented to analyze the complex relationships and behaviors of the parameters. The results reveal that PCA reduced the multidimensional parameters to four significant PCs which are combinations of the original ones. The positive and inverse relationships of the parameters were shown explicitly by pattern analysis in the component planes. It was found that PCA and SOM are efficient tools to capture and analyze the behavior of multivariable, complex, and nonlinear related surface water quality data.

Suggested Citation

  • Yan An & Zhihong Zou & Ranran Li, 2016. "Descriptive Characteristics of Surface Water Quality in Hong Kong by a Self-Organising Map," IJERPH, MDPI, vol. 13(1), pages 1-13, January.
    • Handle: RePEc:gam:jijerp:v:13:y:2016:i:1:p:115-:d:61929
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    1. Amirkhani, S. & Nasirivatan, Sh. & Kasaeian, A.B. & Hajinezhad, A., 2015. "ANN and ANFIS models to predict the performance of solar chimney power plants," Renewable Energy, Elsevier, vol. 83(C), pages 597-607.
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    Cited by:

    1. Ekaterini Hadjisolomou & Konstantinos Stefanidis & George Papatheodorou & Evanthia Papastergiadou, 2018. "Assessment of the Eutrophication-Related Environmental Parameters in Two Mediterranean Lakes by Integrating Statistical Techniques and Self-Organizing Maps," IJERPH, MDPI, vol. 15(3), pages 1-16, March.

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