Effect of data pre-treatment procedures on principal component analysis: A case study for mangrove surface sediment datasets

Sarva Mangala Praveena, and Kwan, Ong Wei and Ahmad Zaharin Aris, (2011) Effect of data pre-treatment procedures on principal component analysis: A case study for mangrove surface sediment datasets. Environmental Monitoring and Assessment . pp. 1-14. ISSN 0167-6369

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Official URL: http://dx.doi.org/10.1007/s10661-011-2463-2

Abstract

Principal component analysis (PCA) is capable of handling large sets of data. However, lack of consistent method in data pre-treatment and its importance are the limitations in PCA applications. This study examined pre-treatments methods (log (x + 1) transformation, outlier removal, and granulometric and geochemical normalization) on dataset of Mengkabong Lagoon, Sabah, mangrove surface sediment at high and low tides. The study revealed that geochemical normalization using Al with outliers removal resulted in a better classification of the mangrove surface sediment than that outliers removal, granulometric normalization using clay and log (x + 1) transformation. PCA output using geochemical normalization with outliers removal demonstrated associations between environmental variables and tides of mangrove surface sediment, Mengkabong Lagoon, Sabah. The PCA outputs at high and low tides also provided to better interpret information about the sediment and its controlling factors in the intertidal zone. The study showed data pre-treatment method to be a useful procedure to standardize the datasets and reducing the influence of outliers.

Item Type:Article
Uncontrolled Keywords:Data pre-treatment, Mangrove surface sediment, Principal component analysis, Tide
Subjects:G Geography. Anthropology. Recreation > GE Environmental Sciences
Divisions:SCHOOL > School of Science and Technology
ID Code:4102
Deposited By:IR Admin
Deposited On:09 May 2012 11:42
Last Modified:17 Feb 2015 11:44

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