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Journal articles on the topic 'Bivariate frequency analysis'

Author: Grafiati

Published: 10 March 2023

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1

Stamatatou, Nikoletta, Lampros Vasiliades, and Athanasios Loukas. "Bivariate Flood Frequency Analysis Using Copulas." Proceedings 2, no. 11 (2018): 635. http://dx.doi.org/10.3390/proceedings2110635.

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Flood frequency estimation for the design of hydraulic structures is usually performed as a univariate analysis of flood event magnitudes. However, recent studies show that for accurate return period estimation of the flood events, the dependence and the correlation pattern among flood attribute characteristics, such as peak discharge, volume and duration should be taken into account in a multivariate framework. The primary goal of this study is to compare univariate and joint bivariate return periods of floods that all rely on different probability concepts in Yermasoyia watershed, Cyprus. Pa

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2

Flamant, Julien, Nicolas Le Bihan, and Pierre Chainais. "Time–frequency analysis of bivariate signals." Applied and Computational Harmonic Analysis 46, no. 2 (2019): 351–83. http://dx.doi.org/10.1016/j.acha.2017.05.007.

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3

Mirakbari, M., A. Ganji, and S. R. Fallah. "Regional Bivariate Frequency Analysis of Meteorological Droughts." Journal of Hydrologic Engineering 15, no. 12 (2010): 985–1000. http://dx.doi.org/10.1061/(asce)he.1943-5584.0000271.

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4

Ziller, M., K. Frick, W. M. Herrmann, S. Kubicki, I. Spieweg, and G. Winterer. "Bivariate Global Frequency Analysis versus Chaos Theory." Neuropsychobiology 32, no. 1 (1995): 45–51. http://dx.doi.org/10.1159/000119211.

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5

Shiau, Jenq-Tzong, Hsin-Yi Wang, and Chang-Tai Tsai. "BIVARIATE FREQUENCY ANALYSIS OF FLOODS USING COPULAS1." Journal of the American Water Resources Association 42, no. 6 (2006): 1549–64. http://dx.doi.org/10.1111/j.1752-1688.2006.tb06020.x.

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6

Joo, Kyung-Won, Ju-Young Shin, and Jun-Haeng Heo. "Bivariate Frequency Analysis of Rainfall using Copula Model." Journal of Korea Water Resources Association 45, no. 8 (2012): 827–37. http://dx.doi.org/10.3741/jkwra.2012.45.8.827.

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7

Razmkhah, Homa, Alireza Fararouie, and Amin Rostami Ravari. "Multivariate Flood Frequency Analysis Using Bivariate Copula Functions." Water Resources Management 36, no. 2 (2022): 729–43. http://dx.doi.org/10.1007/s11269-021-03055-3.

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8

Dong, N. Dang, V. Agilan, and K. V. Jayakumar. "Bivariate Flood Frequency Analysis of Nonstationary Flood Characteristics." Journal of Hydrologic Engineering 24, no. 4 (2019): 04019007. http://dx.doi.org/10.1061/(asce)he.1943-5584.0001770.

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9

Volpi, E., and A. Fiori. "Design event selection in bivariate hydrological frequency analysis." Hydrological Sciences Journal 57, no. 8 (2012): 1506–15. http://dx.doi.org/10.1080/02626667.2012.726357.

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10

Zhang, L., and V. P. Singh. "Bivariate Flood Frequency Analysis Using the Copula Method." Journal of Hydrologic Engineering 11, no. 2 (2006): 150–64. http://dx.doi.org/10.1061/(asce)1084-0699(2006)11:2(150).

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11

Poulin, Annie, David Huard, Anne-Catherine Favre, and Stéphane Pugin. "Importance of Tail Dependence in Bivariate Frequency Analysis." Journal of Hydrologic Engineering 12, no. 4 (2007): 394–403. http://dx.doi.org/10.1061/(asce)1084-0699(2007)12:4(394).

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12

Yue, Sheng. "Applying Bivariate Normal Distribution to Flood Frequency Analysis." Water International 24, no. 3 (1999): 248–54. http://dx.doi.org/10.1080/02508069908692168.

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13

Mirabbasi, Rasoul, Ahmad Fakheri-Fard, and Yagob Dinpashoh. "Bivariate drought frequency analysis using the copula method." Theoretical and Applied Climatology 108, no. 1-2 (2011): 191–206. http://dx.doi.org/10.1007/s00704-011-0524-7.

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14

Yoo, Jiyoung, Hyun-Han Kwon, Tae-Woong Kim, and Jae-Hyun Ahn. "Drought frequency analysis using cluster analysis and bivariate probability distribution." Journal of Hydrology 420-421 (February 2012): 102–11. http://dx.doi.org/10.1016/j.jhydrol.2011.11.046.

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15

Lee, Chang Hwan, Tae-Woong Kim, Gunhui Chung, Minha Choi, and Chulsang Yoo. "Application of bivariate frequency analysis to the derivation of rainfall–frequency curves." Stochastic Environmental Research and Risk Assessment 24, no. 3 (2009): 389–97. http://dx.doi.org/10.1007/s00477-009-0328-9.

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16

Goodarzi, E., M. Mirzaei, L. T. Shui, and M. Ziaei. "Evaluation dam overtopping risk based on univariate and bivariate flood frequency analysis." Hydrology and Earth System Sciences Discussions 8, no. 6 (2011): 9757–96. http://dx.doi.org/10.5194/hessd-8-9757-2011.

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Abstract. There is a growing tendency to assess the safety levels of existing dams based on risk and uncertainty analysis using mathematical and statistical methods. This research presents the application of risk and uncertainty analysis to dam overtopping based on univariate and bivariate flood frequency analyses by applying Gumbel logistic distribution for the Doroudzan earth-fill dam in south of Iran. The bivariate frequency analysis resulted in six inflow hydrographs with a joint return period of 100-yr. The overtopping risks were computed for all of those hydrographs considering quantile

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17

Goodarzi, Ehsan, Majid Mirzaei, and Mina Ziaei. "Evaluation of dam overtopping risk based on univariate and bivariate flood frequency analyses." Canadian Journal of Civil Engineering 39, no. 4 (2012): 374–87. http://dx.doi.org/10.1139/l2012-012.

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There is a growing tendency to assess safety of dams by mathematical and statistical methods in hydrosystem engineering. This research presents the application of risk and uncertainty analysis to dam overtopping based on univariate and bivariate flood frequency analyses by applying Gumbel logistic distribution. The bivariate frequency analyses produced six inflow hydrographs with a joint return period of 100 years. Afterward, the overtopping risk of the Doroudzan Dam was evaluated for all six inflow hydrographs by considering quantile of flood peak discharge, initial depth of water in the rese

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18

Yue, Sheng. "A Bivariate Extreme Value Distribution Applied to Flood Frequency Analysis." Hydrology Research 32, no. 1 (2001): 49–64. http://dx.doi.org/10.2166/nh.2001.0004.

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This article presents a procedure for use of the Gumbel logistic model to represent the joint distribution of two correlated extreme events. Parameters of the distribution are estimated using the method of moments. On the basis of marginal distributions, the joint distribution, the conditional distributions, and the associated return periods can be deduced. The applicability of the model is demonstrated by using multiple episodic flood events of the Harricana River basin in the province of Quebec, Canada. It is concluded that the model is useful for describing joint probabilistic behavior of m

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19

Li, Tianyuan, Shenglian Guo, Lu Chen, and Jiali Guo. "Bivariate Flood Frequency Analysis with Historical Information Based on Copula." Journal of Hydrologic Engineering 18, no. 8 (2013): 1018–30. http://dx.doi.org/10.1061/(asce)he.1943-5584.0000684.

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20

Amirataee, Babak, Majid Montaseri, and Hossein Rezaie. "An advanced data collection procedure in bivariate drought frequency analysis." Hydrological Processes 34, no. 21 (2020): 4067–82. http://dx.doi.org/10.1002/hyp.13866.

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21

Park, Cheol-Soon, Chul-Sang Yoo, and Chang-Hyun Jun. "Bivariate Rainfall Frequency Analysis and Rainfall-runoff Analysis for Independent Rainfall Events." Journal of Korea Water Resources Association 45, no. 7 (2012): 713–27. http://dx.doi.org/10.3741/jkwra.2012.45.7.713.

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22

Farsadnia, F., B. Ghahreman, R. Modarres, and A. Moghaddam Nia. "Hydrologic Drought Frequency Analysis in Karkhe Basin Based on Bivariate Statistical Analysis." Journal of Water and Soil Science 22, no. 3 (2018): 339–55. http://dx.doi.org/10.29252/jstnar.22.3.339.

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23

Kar, Anil Kumar, Pradip Kumar Das, and Raj Beer Padhee. "Bivariate flood frequency analysis a case study of Hirakud reservoir inflow." International Journal of Hydrology Science and Technology 1, no. 1 (2021): 1. http://dx.doi.org/10.1504/ijhst.2021.10039223.

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24

Cheolsoo Park, D. Looney, P. Kidmose, M. Ungstrup, and D. P. Mandic. "Time-Frequency Analysis of EEG Asymmetry Using Bivariate Empirical Mode Decomposition." IEEE Transactions on Neural Systems and Rehabilitation Engineering 19, no. 4 (2011): 366–73. http://dx.doi.org/10.1109/tnsre.2011.2116805.

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25

Lunsford, P. J., G. W. Rhyne, and M. B. Steer. "Frequency-domain bivariate generalized power series analysis of nonlinear analog circuits." IEEE Transactions on Microwave Theory and Techniques 38, no. 6 (1990): 815–18. http://dx.doi.org/10.1109/22.130986.

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26

Li, Min, Ting Zhang, and Ping Feng. "Bivariate frequency analysis of seasonal runoff series under future climate change." Hydrological Sciences Journal 65, no. 14 (2020): 2439–52. http://dx.doi.org/10.1080/02626667.2020.1817927.

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27

Yue, Sheng, and Peter Rasmussen. "Bivariate frequency analysis: discussion of some useful concepts in hydrological application." Hydrological Processes 16, no. 14 (2002): 2881–98. http://dx.doi.org/10.1002/hyp.1185.

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28

Yue, Sheng. "A bivariate gamma distribution for use in multivariate flood frequency analysis." Hydrological Processes 15, no. 6 (2001): 1033–45. http://dx.doi.org/10.1002/hyp.259.

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29

Padhee, Raj Beer, Anil Kumar Kar, and Pradip Kumar Das. "Bivariate flood frequency analysis - a case study of Hirakud reservoir inflow." International Journal of Hydrology Science and Technology 14, no. 4 (2022): 390. http://dx.doi.org/10.1504/ijhst.2022.126433.

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30

Tsakiris, G., N. Kordalis, and V. Tsakiris. "Flood Double Frequency Analysis: 2D-Archimedean Copulas vs Bivariate Probability Distributions." Environmental Processes 2, no. 4 (2015): 705–16. http://dx.doi.org/10.1007/s40710-015-0078-2.

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31

Hidalgo, J. "Spectral Analysis for Bivariate Time Series with Long Memory." Econometric Theory 12, no. 5 (1996): 773–92. http://dx.doi.org/10.1017/s0266466600007155.

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This paper provides limit theorems for spectral density matrix estimators and functionals of it for a bivariate covariance stationary process whose spectral density matrix has singularities not only at the origin but possibly at some other frequencies and, thus, applies to time series exhibiting long memory. In particular, we show that the consistency and asymptotic normality of the spectral density matrix estimator at a frequency, say λ, which hold for weakly dependent time series, continue to hold for long memory processes when λ lies outside any arbitrary neighborhood of the singularities.

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32

Zhou, Ting, Zhiyong Liu, Juliang Jin, and Hongxiang Hu. "Assessing the Impacts of Univariate and Bivariate Flood Frequency Approaches to Flood Risk Accounting for Reservoir Operation." Water 11, no. 3 (2019): 475. http://dx.doi.org/10.3390/w11030475.

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Flood frequency analysis plays a fundamental role in dam planning, reservoir operation, and risk assessment. However, conventional univariate flood frequency analysis carried out by flood peak inflow or volume does not account for the dependence between flood properties. In this paper, we proposed an integrated approach to estimate reservoir risk by combining the copula-based bivariate flood frequency (peak and volume) and reservoir routing. Through investigating the chain reaction of “flood frequency—reservoir operation-flood risk”, this paper demonstrated how to simulate flood hydrographs us

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33

Campos-Aranda, Daniel Francisco. "Aplicación de la distribución GVE bivariada en el Análisis de Frecuencias Conjunto de Crecientes." Tecnología y ciencias del agua 13, no. 6 (2022): 534–602. http://dx.doi.org/10.24850/j-tyca-13-06-11.

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Las crecientes que ocurren en nuestro país cada año generan daños y ponen en peligro a los embalses, cuyo dimensionamiento hidrológico está basado en el hidrograma de la creciente de diseño. La estimación más simple de tal hidrograma se basa en el análisis de frecuencias conjunto del gasto pico y volumen anuales. En este estudio se ajustó la distribución general de valores extremos bivariada (GVEb), al registro de 55 crecientes anuales en la estación hidrométrica La Cuña, sobre el Río Verde de la Región Hidrológica No. 12-3, México. Este proceso abarca nueve etapas: (1) selección y prueba de l

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34

Chun, Si-Young, Yong-Tak Kim, and Hyun-Han Kwon. "Drought Frequency Analysis Using Hidden Markov Chain Model and Bivariate Copula Function." Journal of the Korean Water Resources Association 48, no. 12 (2015): 969–79. http://dx.doi.org/10.3741/jkwra.2015.48.12.969.

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35

Pathak, Abhishek A., and B. M. Dodamani. "Connection between Meteorological and Groundwater Drought with Copula-Based Bivariate Frequency Analysis." Journal of Hydrologic Engineering 26, no. 7 (2021): 05021015. http://dx.doi.org/10.1061/(asce)he.1943-5584.0002089.

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36

Tang, Mingyu, and Grant B. Weller. "Bivariate tail risk analysis for high-frequency returns via extreme value theory." Model Assisted Statistics and Applications 12, no. 1 (2017): 1–14. http://dx.doi.org/10.3233/mas-160379.

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37

Park, Minkyu, Chulsang Yoo, Hyeonjun Kim, and Changhyun Jun. "Bivariate Frequency Analysis of Annual Maximum Rainfall Event Series in Seoul, Korea." Journal of Hydrologic Engineering 19, no. 6 (2014): 1080–88. http://dx.doi.org/10.1061/(asce)he.1943-5584.0000891.

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38

Yu, Jisoo, Si-Jung Choi, Hyun-Han Kwon, and Tae-Woong Kim. "Assessment of regional drought risk under climate change using bivariate frequency analysis." Stochastic Environmental Research and Risk Assessment 32, no. 12 (2018): 3439–53. http://dx.doi.org/10.1007/s00477-018-1582-5.

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39

Reddy, M. Janga, and Poulomi Ganguli. "Bivariate Flood Frequency Analysis of Upper Godavari River Flows Using Archimedean Copulas." Water Resources Management 26, no. 14 (2012): 3995–4018. http://dx.doi.org/10.1007/s11269-012-0124-z.

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40

Santhosh, D., and V. V. Srinivas. "Bivariate frequency analysis of floods using a diffusion based kernel density estimator." Water Resources Research 49, no. 12 (2013): 8328–43. http://dx.doi.org/10.1002/2011wr010777.

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41

San Antolín, A., and R. A. Zalik. "Some Bivariate Smooth Compactly Supported Tight Framelets with Three Generators." Abstract and Applied Analysis 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/818907.

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For any dilation matrix with integer entries and , we construct a family of smooth compactly supported tight wavelet frames with three generators in . Our construction involves some compactly supported refinable functions, the oblique extension principle, and a slight generalization of a theorem of Lai and Stöckler. Estimates for the degrees of smoothness are given. With the exception of a polynomial whose coefficients must in general be computed by spectral factorization, the framelets are expressed in closed form in the frequency domain, in terms of elementary transcendental functions. By me

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42

Won, Jeongeun, Jeonghyeon Choi, Okjeong Lee, Moo Jong Park, and Sangdan Kim. "Two Ways to Quantify Korean Drought Frequency: Partial Duration Series and Bivariate Exponential Distribution, and Application to Climate Change." Atmosphere 11, no. 5 (2020): 476. http://dx.doi.org/10.3390/atmos11050476.

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Studies using drought index to examine return levels of drought can be classified into two approaches: univariate frequency analysis using annual series extracted from drought index time series and multivariate frequency analysis that simultaneously reflects various characteristics of drought. In the case of drought analysis, it is important to properly consider the duration, so, in this study, univariate frequency analysis is performed using the partial duration series. In addition, a bivariate frequency analysis is performed using a relatively simple bivariate exponential distribution to giv

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43

Huqqani, Ilyas A., Lea Tien Tay, and Junita Mohamad Saleh. "Analysis of landslide hazard mapping of penang island malaysia using bivariate statistical methods." Indonesian Journal of Electrical Engineering and Computer Science 16, no. 2 (2019): 781. http://dx.doi.org/10.11591/ijeecs.v16.i2.pp781-786.

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Landslide is one of the disasters which cause property damages, infrastructure destruction, injury and death. This paper presents the analysis of landslide hazard mapping of Penang Island Malaysia using bivariate statistical methods. Bivariate statistical methods are simple approach which are capable to produce good results in short computational time. In this study, three bivariate statistical methods, i.e. Frequency Ratio (FR), Information Value (IV) and Modified Information Value (MIV) are used to generate the landslide hazard maps of Penang Island. These bivariate statistical methods are c

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44

Mohammadi, Tayeb, Soleiman Kheiri, and Morteza Sedehi. "Analysis of Blood Transfusion Data Using Bivariate Zero-Inflated Poisson Model: A Bayesian Approach." Computational and Mathematical Methods in Medicine 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/7878325.

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Recognizing the factors affecting the number of blood donation and blood deferral has a major impact on blood transfusion. There is a positive correlation between the variables “number of blood donation” and “number of blood deferral”: as the number of return for donation increases, so does the number of blood deferral. On the other hand, due to the fact that many donors never return to donate, there is an extra zero frequency for both of the above-mentioned variables. In this study, in order to apply the correlation and to explain the frequency of the excessive zero, the bivariate zero-inflat

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45

Stamatatou, Nikoletta, Lampros Vasiliades, and Athanasios Loukas. "The Effect of Sample Size on Bivariate Rainfall Frequency Analysis of Extreme Precipitation." Proceedings 7, no. 1 (2018): 19. http://dx.doi.org/10.3390/ecws-3-05815.

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The objective of this study is to compare univariate and joint bivariate return periods of extreme precipitation that all rely on different probability concepts in selected meteorological stations in Cyprus. Pairs of maximum rainfall depths with corresponding durations are estimated and compared using annual maximum series (AMS) for the complete period of the analysis and 30-year subsets for selected data periods. Marginal distributions of extreme precipitation are examined and used for the estimation of typical design periods. The dependence between extreme rainfall and duration is then asses

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46

Yu, Ji Soo, Ji Young Yoo, Joo-Heon Lee, and Tea-Woong Kim. "Estimation of drought risk through the bivariate drought frequency analysis using copula functions." Journal of Korea Water Resources Association 49, no. 3 (2016): 217–25. http://dx.doi.org/10.3741/jkwra.2016.49.3.217.

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47

Tosunoglu, Fatih, and Ibrahim Can. "Application of copulas for regional bivariate frequency analysis of meteorological droughts in Turkey." Natural Hazards 82, no. 3 (2016): 1457–77. http://dx.doi.org/10.1007/s11069-016-2253-9.

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48

Jun, Changhyun, Xiaosheng Qin, Thian Yew Gan, Yeou-Koung Tung, and Carlo De Michele. "Bivariate frequency analysis of rainfall intensity and duration for urban stormwater infrastructure design." Journal of Hydrology 553 (October 2017): 374–83. http://dx.doi.org/10.1016/j.jhydrol.2017.08.004.

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49

Yu, Ji Soo, Ji Yae Shin, Minsung Kwon, and Tea-Woong Kim. "Bivariate Drought Frequency Analysis to Evaluate Water Supply Capacity of Multi-Purpose Dams." Journal of The Korean Society of Civil Engineers 37, no. 1 (2017): 231–38. http://dx.doi.org/10.12652/ksce.2017.37.1.0231.

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50

Yoo and Cho. "Effect of Multicollinearity on the Bivariate Frequency Analysis of Annual Maximum Rainfall Events." Water 11, no. 5 (2019): 905. http://dx.doi.org/10.3390/w11050905.

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A rainfall event, simplified by a rectangular pulse, is defined by three components: the rainfall duration, the total rainfall depth, and mean rainfall intensity. However, as the mean rainfall intensity can be calculated by the total rainfall depth divided by the rainfall duration, any two components can fully define the rainfall event (i.e., one component must be redundant). The frequency analysis of a rainfall event also considers just two components selected rather arbitrarily out of these three components. However, this study argues that the two components should be selected properly or th

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