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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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Main Authors: Chuan, Zun Liang, Wan Nur Syahidah, Wan Yusoff, Azlyna, Senawi, Mohd Akramin, Mohd Romlay, Fam, Soo-Fen, Shinyie, Wendy Ling, Ken, Tan Lit
Format: Article
Language:English
Published: Universiti Putra Malaysia Press 2022
Subjects:
QA Mathematics
T Technology (General)
TA Engineering (General). Civil engineering (General)
TJ Mechanical engineering and machinery
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
https://doi.org/10.47836/PJST.30.1.18
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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 https://doi.org/10.47836/PJST.30.1.18 https://doi.org/10.47836/PJST.30.1.18
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA Mathematics
T Technology (General)
TA Engineering (General). Civil engineering (General)
TJ Mechanical engineering and machinery
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
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
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
title 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_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_sort comparative effectiveness of hierarchical and nonhierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions
publisher Universiti Putra Malaysia Press
publishDate 2022
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
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score 13.211869

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