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ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 9, Issue 2 (February 2022), Pages: 104-108

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 Original Research Paper

 Title: Parameter estimate for three-parameter kappa distribution using LH-moments approach

 Author(s): Zahrahtul Amani Zakaria 1, *, Jarah Moath Suleiman Ali 1, Wan Nur Syahidah Wan Yusoff 2

 Affiliation(s):

 1Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Kuala Terengganu, Malaysia
 2Centre for Mathematical Sciences, College of Computing and Applied Sciences, Universiti Malaysia Pahang, Pahang, Malaysia

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0002-7198-1403

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2022.02.011

 Abstract:

The method of higher-order L-moments (LH-moment) was proposed as a more robust alternative compared to classical L-moments to characterize extreme events. The new derivation will be done for Mielke-Johnson’s Kappa and Three-Parameters Kappa Type-II (K3D-II) distributions based on the LH-moments approach. The data of maximum monthly rainfall for Embong station in Terengganu were used as a case study. The analyses were conducted using the classical L-moments method with 𝜂 = 0 and LH-moments methods with 𝜂 = 1, 𝜂 = 2, 𝜂 = 3 and 𝜂 = 4 for a complete data series and upper parts of the distributions. The most suitable distributions were determined based on the Mean Absolute Deviation Index (MADI), Mean Square Deviation Index (MSDI), and Correlation (𝑟). Also, L-moment and LH-moment ratio diagrams were used to represent visual proofs of the results. The analysis showed that LH-moments methods at a higher order of K3D-II distribution best fit the data of maximum monthly rainfalls for the Embong station for the upper parts of the distribution compared to L-moments. The results also proved that whenever 𝜂 increases, LH-moments reflect more and more characteristics of the upper part of the distribution. This seems to suggest that LH-moments estimates for the upper part of the distribution events are superior to L-moments in fitting the data of maximum monthly rainfalls. 

 © 2022 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Higher-order moments, L-moments, Probability distribution, Rainfall forecasting

 Article History: Received 27 June 2021, Received in revised form 27 September 2021, Accepted 9 December 2021

 Acknowledgment 

The authors would like to thank the Department of Irrigation and Drainage, Ministry of Natural Resources and Environment, Malaysia for providing the rainfall data and Center for Research Excellence and Incubation Management, Universiti Sultan Zainal Abidin, Malaysia for funding this research.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Zakaria ZA, Ali JMS, and Yusoff WNSW (2022). Parameter estimate for three-parameter kappa distribution using LH-moments approach. International Journal of Advanced and Applied Sciences, 9(2): 104-108

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 Figures

 Fig. 1

 Tables

 Table 1 Table 2 Table 3   

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