The efficiency of average linkage hierarchical clustering algorithm associated multi-scale bootstrap resampling in identifying homogeneous precipitation catchments

International Conference on Innovative Technology, Engineering and Sciences 2018

工业技术;自然科学

Chuan, Zun Liang^1 ; Ismail, Noriszura^2 ; Shinyie, Wendy Ling^3 ; Ken, Tan Lit^4 ; Fam, Soo-Fen^5 ; Senawi, Azlyna^1 ; Yusoff, Wan Nur Syahidah Wan^1

Faculty Industrial Sciences and Technology, Universiti Malaysia Pahang, Lebuhraya Tun Razak, Pahang Gambang, Kuantan

26300, Malaysia^1

School of Mathematical Sciences, Faculty Science and Technology, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor

43600, Malaysia^2

Department of Mathematics, Faculty of Science, Universiti Putra Malaysia, UPM Serdang, Selangor

43400, Malaysia^3

Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia Kuala Lumpur, Jalan Sultan Yahya Petra (Jalan Semarak), Kuala Lumpur

54100, Malaysia^4

Centre of Technopreneurship Development, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal, Melaka

76100, Malaysia^5

关键词: Agglomerative hierarchical;    Agglomerative hierarchical clustering;    Bootstrap resampling;    Correlation coefficient;    Extreme precipitation events;    Hierarchical clustering algorithms;    Homogeneous Precipitation;    Precipitation time series;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/342/1/012070/pdf
DOI  :  10.1088/1757-899X/342/1/012070

来源: IOP

【 摘 要 】

Due to the limited of historical precipitation records, agglomerative hierarchical clustering algorithms widely used to extrapolate information from gauged to ungauged precipitation catchments in yielding a more reliable projection of extreme hydro-meteorological events such as extreme precipitation events. However, identifying the optimum number of homogeneous precipitation catchments accurately based on the dendrogram resulted using agglomerative hierarchical algorithms are very subjective. The main objective of this study is to propose an efficient regionalized algorithm to identify the homogeneous precipitation catchments for non-stationary precipitation time series. The homogeneous precipitation catchments are identified using average linkage hierarchical clustering algorithm associated multi-scale bootstrap resampling, while uncentered correlation coefficient as the similarity measure. The regionalized homogeneous precipitation is consolidated using K-sample Anderson Darling non-parametric test. The analysis result shows the proposed regionalized algorithm performed more better compared to the proposed agglomerative hierarchical clustering algorithm in previous studies.