Long-term daily rainfall pattern recognition: application of principal component analysis
This study aims to identify the daily rainfall pattern over a 20 year period (1994–2013) using data from 89 stations positioned throughout Malaysia by applying Principal Component Analysis (PCA). Six components were retained using PCA with total variance of 53.43%. The first and the second component...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2015
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| Online Access: | http://psasir.upm.edu.my/id/eprint/42989/1/1-s2.0-S1878029615006167-main.pdf |
| _version_ | 1825929270394880000 |
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| author | Othman, Melawani Ash’aari, Zulfa Hanan Mohamad, Nur Diyana |
| author_facet | Othman, Melawani Ash’aari, Zulfa Hanan Mohamad, Nur Diyana |
| author_sort | Othman, Melawani |
| collection | UPM |
| description | This study aims to identify the daily rainfall pattern over a 20 year period (1994–2013) using data from 89 stations positioned throughout Malaysia by applying Principal Component Analysis (PCA). Six components were retained using PCA with total variance of 53.43%. The first and the second component encompassed regions that show characteristics of Northeast and Southwest monsoons respectively. The fourth component, which covers the northern regions of peninsular Malaysia, shows two peaks in rainfall amount received per year. The third, fifth and sixth components show distinction between regions that mostly cover Sabah and Sarawak |
| first_indexed | 2024-03-06T08:54:20Z |
| format | Article |
| id | upm.eprints-42989 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T08:54:20Z |
| publishDate | 2015 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | upm.eprints-429892016-05-03T06:39:15Z http://psasir.upm.edu.my/id/eprint/42989/ Long-term daily rainfall pattern recognition: application of principal component analysis Othman, Melawani Ash’aari, Zulfa Hanan Mohamad, Nur Diyana This study aims to identify the daily rainfall pattern over a 20 year period (1994–2013) using data from 89 stations positioned throughout Malaysia by applying Principal Component Analysis (PCA). Six components were retained using PCA with total variance of 53.43%. The first and the second component encompassed regions that show characteristics of Northeast and Southwest monsoons respectively. The fourth component, which covers the northern regions of peninsular Malaysia, shows two peaks in rainfall amount received per year. The third, fifth and sixth components show distinction between regions that mostly cover Sabah and Sarawak Elsevier 2015 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/42989/1/1-s2.0-S1878029615006167-main.pdf Othman, Melawani and Ash’aari, Zulfa Hanan and Mohamad, Nur Diyana (2015) Long-term daily rainfall pattern recognition: application of principal component analysis. Procedia Environmental Sciences, 30. pp. 127-132. ISSN 1878-0296 http://www.sciencedirect.com/science/article/pii/S1878029615006167 10.1016/j.proenv.2015.10.022 |
| spellingShingle | Othman, Melawani Ash’aari, Zulfa Hanan Mohamad, Nur Diyana Long-term daily rainfall pattern recognition: application of principal component analysis |
| title | Long-term daily rainfall pattern recognition: application of principal component analysis |
| title_full | Long-term daily rainfall pattern recognition: application of principal component analysis |
| title_fullStr | Long-term daily rainfall pattern recognition: application of principal component analysis |
| title_full_unstemmed | Long-term daily rainfall pattern recognition: application of principal component analysis |
| title_short | Long-term daily rainfall pattern recognition: application of principal component analysis |
| title_sort | long term daily rainfall pattern recognition application of principal component analysis |
| url | http://psasir.upm.edu.my/id/eprint/42989/1/1-s2.0-S1878029615006167-main.pdf |
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