Statistical modeling of RPCA-FCM in spatiotemporal rainfall patterns recognition
This study was conducted to identify the spatiotemporal torrential rainfall patterns of the East Coast of Peninsular Malaysia, as it is the region most affected by the torrential rainfall of the Northeast Monsoon season. Dimension reduction, such as the classical Principal Components Analysis (PCA)...
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| Format: | Article |
| Language: | English |
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MDPI
2022
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| Online Access: | http://eprints.utm.my/101148/1/NorhaizaAhmad2022_StatisticalModelingofRPCAFCMinSpatiotemporal.pdf |
| _version_ | 1796867009114275840 |
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| author | Che Mat Nor, Siti Mariana Shaharudin, Shazlyn Milleana Ismail, Shuhaida Mohd. Najib, Sumayyah Aimi Tan, Mou Leong Ahmad, Norhaiza |
| author_facet | Che Mat Nor, Siti Mariana Shaharudin, Shazlyn Milleana Ismail, Shuhaida Mohd. Najib, Sumayyah Aimi Tan, Mou Leong Ahmad, Norhaiza |
| author_sort | Che Mat Nor, Siti Mariana |
| collection | ePrints |
| description | This study was conducted to identify the spatiotemporal torrential rainfall patterns of the East Coast of Peninsular Malaysia, as it is the region most affected by the torrential rainfall of the Northeast Monsoon season. Dimension reduction, such as the classical Principal Components Analysis (PCA) coupled with the clustering approach, is often applied to reduce the dimension of the data while simultaneously performing cluster partitions. However, the classical PCA is highly insensitive to outliers, as it assigns equal weights to each set of observations. Hence, applying the classical PCA could affect the cluster partitions of the rainfall patterns. Furthermore, traditional clustering algorithms only allow each element to exclusively belong to one cluster, thus observations within overlapping clusters of the torrential rainfall datasets might not be captured effectively. In this study, a statistical model of torrential rainfall pattern recognition was proposed to alleviate these issues. Here, a Robust PCA (RPCA) based on Tukey’s biweight correlation was introduced and the optimum breakdown point to extract the number of components was identified. A breakdown point of 0.4 at 85% cumulative variance percentage efficiently extracted the number of components to avoid low-frequency variations or insignificant clusters on a spatial scale. Based on the extracted components, the rainfall patterns were further characterized based on cluster solutions attained using Fuzzy C-means clustering (FCM) to allow data elements to belong to more than one cluster, as the rainfall data structure permits this. Lastly, data generated using a Monte Carlo simulation were used to evaluate the performance of the proposed statistical modeling. It was found that the proposed RPCA-FCM performed better using RPCA-FCM compared to the classical PCA coupled with FCM in identifying the torrential rainfall patterns of Peninsular Malaysia’s East Coast. |
| first_indexed | 2024-03-05T21:20:45Z |
| format | Article |
| id | utm.eprints-101148 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T21:20:45Z |
| publishDate | 2022 |
| publisher | MDPI |
| record_format | dspace |
| spelling | utm.eprints-1011482023-06-01T09:00:41Z http://eprints.utm.my/101148/ Statistical modeling of RPCA-FCM in spatiotemporal rainfall patterns recognition Che Mat Nor, Siti Mariana Shaharudin, Shazlyn Milleana Ismail, Shuhaida Mohd. Najib, Sumayyah Aimi Tan, Mou Leong Ahmad, Norhaiza QA Mathematics This study was conducted to identify the spatiotemporal torrential rainfall patterns of the East Coast of Peninsular Malaysia, as it is the region most affected by the torrential rainfall of the Northeast Monsoon season. Dimension reduction, such as the classical Principal Components Analysis (PCA) coupled with the clustering approach, is often applied to reduce the dimension of the data while simultaneously performing cluster partitions. However, the classical PCA is highly insensitive to outliers, as it assigns equal weights to each set of observations. Hence, applying the classical PCA could affect the cluster partitions of the rainfall patterns. Furthermore, traditional clustering algorithms only allow each element to exclusively belong to one cluster, thus observations within overlapping clusters of the torrential rainfall datasets might not be captured effectively. In this study, a statistical model of torrential rainfall pattern recognition was proposed to alleviate these issues. Here, a Robust PCA (RPCA) based on Tukey’s biweight correlation was introduced and the optimum breakdown point to extract the number of components was identified. A breakdown point of 0.4 at 85% cumulative variance percentage efficiently extracted the number of components to avoid low-frequency variations or insignificant clusters on a spatial scale. Based on the extracted components, the rainfall patterns were further characterized based on cluster solutions attained using Fuzzy C-means clustering (FCM) to allow data elements to belong to more than one cluster, as the rainfall data structure permits this. Lastly, data generated using a Monte Carlo simulation were used to evaluate the performance of the proposed statistical modeling. It was found that the proposed RPCA-FCM performed better using RPCA-FCM compared to the classical PCA coupled with FCM in identifying the torrential rainfall patterns of Peninsular Malaysia’s East Coast. MDPI 2022 Article PeerReviewed application/pdf en http://eprints.utm.my/101148/1/NorhaizaAhmad2022_StatisticalModelingofRPCAFCMinSpatiotemporal.pdf Che Mat Nor, Siti Mariana and Shaharudin, Shazlyn Milleana and Ismail, Shuhaida and Mohd. Najib, Sumayyah Aimi and Tan, Mou Leong and Ahmad, Norhaiza (2022) Statistical modeling of RPCA-FCM in spatiotemporal rainfall patterns recognition. Atmosphere, 13 (1). pp. 1-21. ISSN 2073-4433 http://dx.doi.org/10.3390/atmos13010145 DOI : 10.3390/atmos13010145 |
| spellingShingle | QA Mathematics Che Mat Nor, Siti Mariana Shaharudin, Shazlyn Milleana Ismail, Shuhaida Mohd. Najib, Sumayyah Aimi Tan, Mou Leong Ahmad, Norhaiza Statistical modeling of RPCA-FCM in spatiotemporal rainfall patterns recognition |
| title | Statistical modeling of RPCA-FCM in spatiotemporal rainfall patterns recognition |
| title_full | Statistical modeling of RPCA-FCM in spatiotemporal rainfall patterns recognition |
| title_fullStr | Statistical modeling of RPCA-FCM in spatiotemporal rainfall patterns recognition |
| title_full_unstemmed | Statistical modeling of RPCA-FCM in spatiotemporal rainfall patterns recognition |
| title_short | Statistical modeling of RPCA-FCM in spatiotemporal rainfall patterns recognition |
| title_sort | statistical modeling of rpca fcm in spatiotemporal rainfall patterns recognition |
| topic | QA Mathematics |
| url | http://eprints.utm.my/101148/1/NorhaizaAhmad2022_StatisticalModelingofRPCAFCMinSpatiotemporal.pdf |
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