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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Universiti Putra Malaysia Press
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/34949/1/A%20comparative%20effectiveness%20of%20hierarchical%20and%20nonhierarchical%20regionalisation%20algorithms.pdf http://umpir.ump.edu.my/id/eprint/34949/ https://doi.org/10.47836/PJST.30.1.18 |
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| author | Chuan, Zun Liang Wan Nur Syahidah, Wan Yusoff Azlyna, Senawi Mohd Akramin, Mohd Romlay Fam, Soo-Fen Shinyie, Wendy Ling Ken, Tan Lit |
| author_facet | Chuan, Zun Liang Wan Nur Syahidah, Wan Yusoff Azlyna, Senawi Mohd Akramin, Mohd Romlay Fam, Soo-Fen Shinyie, Wendy Ling Ken, Tan Lit |
| author_sort | Chuan, Zun Liang |
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| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
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| continent | Asia |
| country | Malaysia |
| 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. |
| format | Article |
| id | my.ump.umpir.34949 |
| institution | Universiti Malaysia Pahang |
| language | en |
| publishDate | 2022 |
| publisher | Universiti Putra Malaysia Press |
| record_format | eprints |
| spelling | my.ump.umpir.349492022-11-08T04:53:04Z http://umpir.ump.edu.my/id/eprint/34949/ A comparative effectiveness of hierarchical and nonhierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions Chuan, Zun Liang Wan Nur Syahidah, Wan Yusoff Azlyna, Senawi Mohd Akramin, Mohd Romlay Fam, Soo-Fen Shinyie, Wendy Ling Ken, Tan Lit QA Mathematics T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery 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 2022-01 Article PeerReviewed pdf en cc_by_nc_nd_4 http://umpir.ump.edu.my/id/eprint/34949/1/A%20comparative%20effectiveness%20of%20hierarchical%20and%20nonhierarchical%20regionalisation%20algorithms.pdf Chuan, Zun Liang and Wan Nur Syahidah, Wan Yusoff and Azlyna, Senawi and Mohd Akramin, Mohd Romlay and Fam, Soo-Fen and Shinyie, Wendy Ling and Ken, Tan Lit (2022) A comparative effectiveness of hierarchical and nonhierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions. Pertanika Journal of Science and Technology, 30 (1). pp. 319-342. ISSN 0128-7680. (Published) https://doi.org/10.47836/PJST.30.1.18 https://doi.org/10.47836/PJST.30.1.18 |
| spellingShingle | QA Mathematics T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery Chuan, Zun Liang Wan Nur Syahidah, Wan Yusoff Azlyna, Senawi Mohd Akramin, Mohd Romlay Fam, Soo-Fen Shinyie, Wendy Ling Ken, Tan Lit 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 |
| topic | QA Mathematics T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery |
| url | http://umpir.ump.edu.my/id/eprint/34949/1/A%20comparative%20effectiveness%20of%20hierarchical%20and%20nonhierarchical%20regionalisation%20algorithms.pdf http://umpir.ump.edu.my/id/eprint/34949/ https://doi.org/10.47836/PJST.30.1.18 https://doi.org/10.47836/PJST.30.1.18 |
| url_provider | http://umpir.ump.edu.my/ |