A comparative effectiveness of hierarchical and nonhierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions
Descriptive data mining has been widely applied in hydrology as the regionalisation algorithms to identify the statistically homogeneous rainfall regions. However, previous studies employed regionalisation algorithms, namely agglomerative hierarchical and non-hierarchical regionalisation algorithms...
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| Format: | Article |
| Language: | English |
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Universiti Putra Malaysia Press
2022
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| Online Access: | http://psasir.upm.edu.my/id/eprint/98159/ http://psasir.upm.edu.my/id/eprint/98159/1/18%20JST-2029-2020 |
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| author | Zun, Liang Chuan Wan Yusof, Wan Nur Syahidah Senawi, Azlyna Mohd Akramin, Mohd Romlay Soo, Fen Fam Wendy, Ling Shinyie Tan, Lit Ken |
| author_facet | Zun, Liang Chuan Wan Yusof, Wan Nur Syahidah Senawi, Azlyna Mohd Akramin, Mohd Romlay Soo, Fen Fam Wendy, Ling Shinyie Tan, Lit Ken |
| author_sort | Zun, Liang Chuan |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Descriptive data mining has been widely applied in hydrology as the regionalisation algorithms to identify the statistically homogeneous rainfall regions. However, previous studies employed regionalisation algorithms, namely agglomerative hierarchical and non-hierarchical regionalisation algorithms requiring post-processing techniques to validate and interpret the analysis results. The main objective of this study is to investigate the effectiveness of the automated agglomerative hierarchical and non-hierarchical regionalisation algorithms in identifying the homogeneous rainfall regions based on a new statistically significant difference regionalised feature set. To pursue this objective, this study collected 20 historical monthly rainfall time-series data from the rain gauge stations located in the Kuantan district. In practice, these 20 rain gauge stations can be categorised into two statistically homogeneous rainfall regions, namely distinct spatial and temporal variability in the rainfall amounts. The results of the analysis show that Forgy K-means non-hierarchical (FKNH), HartiganWong K-means non-hierarchical (HKNH), and Lloyd K-means non-hierarchical (LKNH) regionalisation algorithms are superior to other automated agglomerative hierarchical and non-hierarchical regionalisation algorithms. Furthermore, FKNH, HKNH, and LKNH yielded the highest regionalisation accuracy compared to other automated agglomerative hierarchical and non-hierarchical regionalisation algorithms. Based on the regionalisation results yielded in this study, the reliability and accuracy that assessed the risk of extreme hydro-meteorological events for the Kuantan district can be improved. In particular, the regional quantile estimates can provide a more accurate estimation compared to at-site quantile estimates using an appropriate statistical distribution. |
| first_indexed | 2025-11-15T13:22:41Z |
| format | Article |
| id | upm-98159 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T13:22:41Z |
| publishDate | 2022 |
| publisher | Universiti Putra Malaysia Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-981592022-08-13T00:37:18Z http://psasir.upm.edu.my/id/eprint/98159/ A comparative effectiveness of hierarchical and nonhierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions Zun, Liang Chuan Wan Yusof, Wan Nur Syahidah Senawi, Azlyna Mohd Akramin, Mohd Romlay Soo, Fen Fam Wendy, Ling Shinyie Tan, Lit Ken Descriptive data mining has been widely applied in hydrology as the regionalisation algorithms to identify the statistically homogeneous rainfall regions. However, previous studies employed regionalisation algorithms, namely agglomerative hierarchical and non-hierarchical regionalisation algorithms requiring post-processing techniques to validate and interpret the analysis results. The main objective of this study is to investigate the effectiveness of the automated agglomerative hierarchical and non-hierarchical regionalisation algorithms in identifying the homogeneous rainfall regions based on a new statistically significant difference regionalised feature set. To pursue this objective, this study collected 20 historical monthly rainfall time-series data from the rain gauge stations located in the Kuantan district. In practice, these 20 rain gauge stations can be categorised into two statistically homogeneous rainfall regions, namely distinct spatial and temporal variability in the rainfall amounts. The results of the analysis show that Forgy K-means non-hierarchical (FKNH), HartiganWong K-means non-hierarchical (HKNH), and Lloyd K-means non-hierarchical (LKNH) regionalisation algorithms are superior to other automated agglomerative hierarchical and non-hierarchical regionalisation algorithms. Furthermore, FKNH, HKNH, and LKNH yielded the highest regionalisation accuracy compared to other automated agglomerative hierarchical and non-hierarchical regionalisation algorithms. Based on the regionalisation results yielded in this study, the reliability and accuracy that assessed the risk of extreme hydro-meteorological events for the Kuantan district can be improved. In particular, the regional quantile estimates can provide a more accurate estimation compared to at-site quantile estimates using an appropriate statistical distribution. Universiti Putra Malaysia Press 2022 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/98159/1/18%20JST-2029-2020 Zun, Liang Chuan and Wan Yusof, Wan Nur Syahidah and Senawi, Azlyna and Mohd Akramin, Mohd Romlay and Soo, Fen Fam and Wendy, Ling Shinyie and Tan, Lit Ken (2022) A comparative effectiveness of hierarchical and nonhierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions. Pertanika Journal of Science & Technology, 30 (1). pp. 319-342. ISSN 0128-7680; ESSN: 2231-8526 http://www.pertanika.upm.edu.my/pjst/browse/regular-issue?article=JST-2029-2020 10.47836/pjst.30.1.18 |
| spellingShingle | Zun, Liang Chuan Wan Yusof, Wan Nur Syahidah Senawi, Azlyna Mohd Akramin, Mohd Romlay Soo, Fen Fam Wendy, Ling Shinyie Tan, Lit Ken A comparative effectiveness of hierarchical and nonhierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions |
| title | A comparative effectiveness of hierarchical and nonhierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions |
| title_full | A comparative effectiveness of hierarchical and nonhierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions |
| title_fullStr | A comparative effectiveness of hierarchical and nonhierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions |
| title_full_unstemmed | A comparative effectiveness of hierarchical and nonhierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions |
| title_short | A comparative effectiveness of hierarchical and nonhierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions |
| title_sort | comparative effectiveness of hierarchical and nonhierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions |
| url | http://psasir.upm.edu.my/id/eprint/98159/ http://psasir.upm.edu.my/id/eprint/98159/ http://psasir.upm.edu.my/id/eprint/98159/ http://psasir.upm.edu.my/id/eprint/98159/1/18%20JST-2029-2020 |