Identification of rainfall patterns on hydrological simulation using robust principal component analysis
A robust dimension reduction method in Principal Component Analysis (PCA) was used to rectify the issue of unbalanced clusters in rainfall patterns due to the skewed nature of rainfall data. A robust measure in PCA using Tukey’s biweight correlation to downweigh observations was introduced and the o...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Institute of Advanced Engineering and Science
2018
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/84561/1/NorhaizaAhmad2018_IdentificationofRainfallPatternsonHydrologicalSimulation.pdf http://eprints.utm.my/id/eprint/84561/ http://ijeecs.iaescore.com/index.php/IJEECS/article/view/13473 |
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| Summary: | A robust dimension reduction method in Principal Component Analysis (PCA) was used to rectify the issue of unbalanced clusters in rainfall patterns due to the skewed nature of rainfall data. A robust measure in PCA using Tukey’s biweight correlation to downweigh observations was introduced and the optimum breakdown point to extract the number of components in PCA using this approach is proposed. A set of simulated data matrix that mimicked the real data set was used to determine an appropriate breakdown point for robust PCA and compare the performance of the both approaches. The simulated data indicated a breakdown point of 70% cumulative percentage of variance gave a good balance in extracting the number of components. The results showed a more significant and substantial improvement with the robust PCA than the PCA based Pearson correlation in terms of the average number of clusters obtained and its cluster quality. |
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