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Robust principal component analysis in water quality index development


Mohd Ali, Zalina and Ibrahim, Noor Akma and Mengersen, Kerrie and Shitan, Mahendran and Juahir, Hafizan (2013) Robust principal component analysis in water quality index development. In: 3rd International Conference on Mathematical Sciences (ICMS3), 17-19 Dec. 2013, Kuala Lumpur, Malaysia. (pp. 1091-1097).


Some statistical procedures already available in literature are employed in developing the water quality index, WQI. The nature of complexity and interdependency that occur in physical and chemical processes of water could be easier explained if statistical approaches were applied to water quality indexing. The most popular statistical method used in developing WQI is the principal component analysis (PCA). In literature, the WQI development based on the classical PCA mostly used water quality data that have been transformed and normalized. Outliers may be considered in or eliminated from the analysis. However, the classical mean and sample covariance matrix used in classical PCA methodology is not reliable if the outliers exist in the data. Since the presence of outliers may affect the computation of the principal component, robust principal component analysis, RPCA should be used. Focusing in Langat River, the RPCA-WQI was introduced for the first time in this study to re-calculate the DOE-WQI. Results show that the RPCA-WQI is capable to capture similar distribution in the existing DOE-WQI.

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Additional Metadata

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculty of Environmental Studies
Institute for Mathematical Research
DOI Number: https://doi.org/10.1063/1.4882620
Publisher: AIP Publishing LLC
Keywords: Robust principal component analysis; Water quality index; Langat River
Depositing User: Nabilah Mustapa
Date Deposited: 26 Sep 2017 04:04
Last Modified: 26 Sep 2017 04:04
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1063/1.4882620
URI: http://psasir.upm.edu.my/id/eprint/57324
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