Robust principal component analysis in water quality index development
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 i...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
AIP Publishing LLC
2013
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| Online Access: | http://psasir.upm.edu.my/id/eprint/57324/ http://psasir.upm.edu.my/id/eprint/57324/1/Robust%20principal%20component%20analysis%20in%20water%20quality%20index%20development.pdf |
| _version_ | 1848853333283962880 |
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| author | Mohd Ali, Zalina Ibrahim, Noor Akma Mengersen, Kerrie Shitan, Mahendran Juahir, Hafizan |
| author_facet | Mohd Ali, Zalina Ibrahim, Noor Akma Mengersen, Kerrie Shitan, Mahendran Juahir, Hafizan |
| author_sort | Mohd Ali, Zalina |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T10:52:18Z |
| format | Conference or Workshop Item |
| id | upm-57324 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T10:52:18Z |
| publishDate | 2013 |
| publisher | AIP Publishing LLC |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-573242017-09-26T04:04:39Z http://psasir.upm.edu.my/id/eprint/57324/ Robust principal component analysis in water quality index development Mohd Ali, Zalina Ibrahim, Noor Akma Mengersen, Kerrie Shitan, Mahendran Juahir, Hafizan 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. AIP Publishing LLC 2013 Conference or Workshop Item PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/57324/1/Robust%20principal%20component%20analysis%20in%20water%20quality%20index%20development.pdf 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). 10.1063/1.4882620 |
| spellingShingle | Mohd Ali, Zalina Ibrahim, Noor Akma Mengersen, Kerrie Shitan, Mahendran Juahir, Hafizan Robust principal component analysis in water quality index development |
| title | Robust principal component analysis in water quality index development |
| title_full | Robust principal component analysis in water quality index development |
| title_fullStr | Robust principal component analysis in water quality index development |
| title_full_unstemmed | Robust principal component analysis in water quality index development |
| title_short | Robust principal component analysis in water quality index development |
| title_sort | robust principal component analysis in water quality index development |
| url | http://psasir.upm.edu.my/id/eprint/57324/ http://psasir.upm.edu.my/id/eprint/57324/ http://psasir.upm.edu.my/id/eprint/57324/1/Robust%20principal%20component%20analysis%20in%20water%20quality%20index%20development.pdf |