Next Article in Journal
Modelling Snowmelt in Ungauged Catchments
Previous Article in Journal
Analysis of Drought Progression Physiognomies in South Africa
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Water
Volume 11
Issue 2
10.3390/w11020300
water-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Calculation of Joint Return Period for Connected Edge Data

by
Guilin Liu
1,
Baiyu Chen
2,*,
Zhikang Gao
1,
Hanliang Fu
3,
Song Jiang
3,
Liping Wang
4 and
Kou Yi
5
1
College of Engineering, Ocean University of China, Qingdao 266100, China
2
College of Engineering, University of California Berkeley, Berkeley, CA 94720, USA
3
School of Management, Xi’an University of Architecture and Technology, Xi’an 710055, China
4
School of Mathematical Sciences, Ocean University of China, Qingdao 266100, China
5
Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
*
Author to whom correspondence should be addressed.
Water 2019, 11(2), 300; https://doi.org/10.3390/w11020300
Submission received: 11 December 2018 / Revised: 31 January 2019 / Accepted: 1 February 2019 / Published: 11 February 2019
(This article belongs to the Section Hydraulics and Hydrodynamics)
Browse Figures
Versions Notes

Abstract

For better displaying the statistical properties of measured data, it is particularly important to select a suitable multivariate joint distribution model in ocean engineering. According to the characteristics and properties of Copula functions and the correlation analysis of measured data, the nonlinear relationship between random variables can be captured. Additionally, the models based on the Copula theory have more general applicability. A series of correlation measure index, derived from Copula functions, can expand the correlation measure range among variables. In this paper, by means of the correlation analysis between the annual extreme wave height and the corresponding wind speed, their joint distribution models were studied. The newly established two-dimensional joint distribution functions of the extreme wave height and the corresponding wind speed were compared with the existing two-dimensional joint distributions.
Keywords: Copula functions; mixed Gumbel distribution; Gumbel-logistic distribution; the joint return period Copula functions; mixed Gumbel distribution; Gumbel-logistic distribution; the joint return period

Share and Cite

MDPI and ACS Style

Liu, G.; Chen, B.; Gao, Z.; Fu, H.; Jiang, S.; Wang, L.; Yi, K. Calculation of Joint Return Period for Connected Edge Data. Water 2019, 11, 300. https://doi.org/10.3390/w11020300

AMA Style

Liu G, Chen B, Gao Z, Fu H, Jiang S, Wang L, Yi K. Calculation of Joint Return Period for Connected Edge Data. Water. 2019; 11(2):300. https://doi.org/10.3390/w11020300

Chicago/Turabian Style

Liu, Guilin, Baiyu Chen, Zhikang Gao, Hanliang Fu, Song Jiang, Liping Wang, and Kou Yi. 2019. "Calculation of Joint Return Period for Connected Edge Data" Water 11, no. 2: 300. https://doi.org/10.3390/w11020300

APA Style

Liu, G., Chen, B., Gao, Z., Fu, H., Jiang, S., Wang, L., & Yi, K. (2019). Calculation of Joint Return Period for Connected Edge Data. Water, 11(2), 300. https://doi.org/10.3390/w11020300

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop