Identifying homogeneous rainfall catchments for non-stationary time series using TOPSIS algorithm and bootstrap K-sample Anderson-Darling test

Identifying homogeneous rainfall catchments for non-stationary time series using TOPSIS algorithm and bootstrap K-sample Anderson-Darling test

The reliability of extreme estimates of hydro-meteorological events such as extreme rainfalls may be questionable due to limited historical rainfall records. The problem of limited rainfall records, however, can be overcome by extrapolating information from gauged to ungauged rainfall catchments, wh...

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Main Authors: Chuan, Zun Liang, Noriszura, Ismail, Wan Nur Syahidah, Wan Yusoff, Soo-Fen, Fam, Mohd Akramin, Mohd Romlay
Format: Article
Language:English
Published: Science Publishing Corporation 2018
Subjects:
QA Mathematics
Online Access:http://umpir.ump.edu.my/id/eprint/22702/
http://umpir.ump.edu.my/id/eprint/22702/
http://umpir.ump.edu.my/id/eprint/22702/1/Identifying%20homogeneous%20rainfall%20catchments%20for%20non-%20stationary%20time%20series%20using%20tops%20is%20algorithm%20and%20bootstrap%20k-sample%20Anderson%20darling%20test.pdf
id ump-22702
recordtype eprints
spelling ump-227022019-01-09T03:06:02Z http://umpir.ump.edu.my/id/eprint/22702/ Identifying homogeneous rainfall catchments for non-stationary time series using TOPSIS algorithm and bootstrap K-sample Anderson-Darling test Chuan, Zun Liang Noriszura, Ismail Wan Nur Syahidah, Wan Yusoff Soo-Fen, Fam Mohd Akramin, Mohd Romlay QA Mathematics The reliability of extreme estimates of hydro-meteorological events such as extreme rainfalls may be questionable due to limited historical rainfall records. The problem of limited rainfall records, however, can be overcome by extrapolating information from gauged to ungauged rainfall catchments, which requires information on the homogeneity among rainfall catchments. The purpose of this study is to introduce a new regionalization algorithm to identify the most suitable agglomerative hierarchical clustering (AHC) algorithm and the optimum number of homogeneous rainfall catchments for non-stationary rainfall time series. The new algorithm is based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm. This study also suggests the use of Bootstrap K-sample Anderson Darling (BKAD) test for validating regionalized homogeneous rainfall catchments. The Cophenetic Correlation Coefficients (CCC) from ten similarity measures are used as attributes for the TOPSIS algorithm to identify the most suitable AHC algorithm out of seven algorithms considered. The C-index (δCI), Davies-Bouldin index (δDB), Dunn index (δDI) and Gamma index (δGI) are then used as attributes for the TOPSIS algorithm to determine the optimum number of homogeneous rainfall catchments. The results show that the most suitable AHC algorithm is able to cluster twenty rainfall catchments in Kuantan River Basin, Malaysia into two optimum significant homogeneous clusters. The results also imply that the BKAD test is invariant towards the number of Bootstrap samples in the validation of homogeneous rainfall catchments. Science Publishing Corporation 2018-11-16 Article PeerReviewed pdf en cc_by http://umpir.ump.edu.my/id/eprint/22702/1/Identifying%20homogeneous%20rainfall%20catchments%20for%20non-%20stationary%20time%20series%20using%20tops%20is%20algorithm%20and%20bootstrap%20k-sample%20Anderson%20darling%20test.pdf Chuan, Zun Liang and Noriszura, Ismail and Wan Nur Syahidah, Wan Yusoff and Soo-Fen, Fam and Mohd Akramin, Mohd Romlay (2018) Identifying homogeneous rainfall catchments for non-stationary time series using TOPSIS algorithm and bootstrap K-sample Anderson-Darling test. International Journal of Engineering & Technology, 7 (4). pp. 3228-3237. ISSN 2227-524X https://www.sciencepubco.com/index.php/ijet/article/view/21569
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic QA Mathematics
spellingShingle QA Mathematics
Chuan, Zun Liang
Noriszura, Ismail
Wan Nur Syahidah, Wan Yusoff
Soo-Fen, Fam
Mohd Akramin, Mohd Romlay
Identifying homogeneous rainfall catchments for non-stationary time series using TOPSIS algorithm and bootstrap K-sample Anderson-Darling test
description The reliability of extreme estimates of hydro-meteorological events such as extreme rainfalls may be questionable due to limited historical rainfall records. The problem of limited rainfall records, however, can be overcome by extrapolating information from gauged to ungauged rainfall catchments, which requires information on the homogeneity among rainfall catchments. The purpose of this study is to introduce a new regionalization algorithm to identify the most suitable agglomerative hierarchical clustering (AHC) algorithm and the optimum number of homogeneous rainfall catchments for non-stationary rainfall time series. The new algorithm is based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm. This study also suggests the use of Bootstrap K-sample Anderson Darling (BKAD) test for validating regionalized homogeneous rainfall catchments. The Cophenetic Correlation Coefficients (CCC) from ten similarity measures are used as attributes for the TOPSIS algorithm to identify the most suitable AHC algorithm out of seven algorithms considered. The C-index (δCI), Davies-Bouldin index (δDB), Dunn index (δDI) and Gamma index (δGI) are then used as attributes for the TOPSIS algorithm to determine the optimum number of homogeneous rainfall catchments. The results show that the most suitable AHC algorithm is able to cluster twenty rainfall catchments in Kuantan River Basin, Malaysia into two optimum significant homogeneous clusters. The results also imply that the BKAD test is invariant towards the number of Bootstrap samples in the validation of homogeneous rainfall catchments.
format Article
author Chuan, Zun Liang
Noriszura, Ismail
Wan Nur Syahidah, Wan Yusoff
Soo-Fen, Fam
Mohd Akramin, Mohd Romlay
author_facet Chuan, Zun Liang
Noriszura, Ismail
Wan Nur Syahidah, Wan Yusoff
Soo-Fen, Fam
Mohd Akramin, Mohd Romlay
author_sort Chuan, Zun Liang
title Identifying homogeneous rainfall catchments for non-stationary time series using TOPSIS algorithm and bootstrap K-sample Anderson-Darling test
title_short Identifying homogeneous rainfall catchments for non-stationary time series using TOPSIS algorithm and bootstrap K-sample Anderson-Darling test
title_full Identifying homogeneous rainfall catchments for non-stationary time series using TOPSIS algorithm and bootstrap K-sample Anderson-Darling test
title_fullStr Identifying homogeneous rainfall catchments for non-stationary time series using TOPSIS algorithm and bootstrap K-sample Anderson-Darling test
title_full_unstemmed Identifying homogeneous rainfall catchments for non-stationary time series using TOPSIS algorithm and bootstrap K-sample Anderson-Darling test
title_sort identifying homogeneous rainfall catchments for non-stationary time series using topsis algorithm and bootstrap k-sample anderson-darling test
publisher Science Publishing Corporation
publishDate 2018
url http://umpir.ump.edu.my/id/eprint/22702/
http://umpir.ump.edu.my/id/eprint/22702/
http://umpir.ump.edu.my/id/eprint/22702/1/Identifying%20homogeneous%20rainfall%20catchments%20for%20non-%20stationary%20time%20series%20using%20tops%20is%20algorithm%20and%20bootstrap%20k-sample%20Anderson%20darling%20test.pdf
first_indexed 2023-09-18T22:33:58Z
last_indexed 2023-09-18T22:33:58Z
_version_ 1777416471285071872

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