Multivariate Frequency Analysis for Streamflow Drought Having Different Time Resolution Using Archimedean Copula Functions

2022 ◽  
Author(s):  
Jang Hyun Sung ◽  
Young Ryu ◽  
Eun-Sung Chung
Keyword(s):  
Frequency Analysis ◽  
Time Resolution ◽  
Archimedean Copula ◽  
Copula Functions ◽  
Multivariate Frequency Analysis

scholarly journals Comparison Between Bivariate and Trivariate Flood Frequency Analysis Using the Archimedean Copula Functions, A Case Study of the Karun River in Iran

2021 ◽  
Author(s):  
Mohamad Haytham Klaho ◽  
Hamid R. Safavi ◽  
Mohamad H. Golmohammadi ◽  
Maamoun Alkntar
Keyword(s):  
Frequency Analysis ◽  
Return Period ◽  
Goodness Of Fit ◽  
Flood Frequency ◽  
Archimedean Copula ◽  
Flood Frequency Analysis ◽  
Cumulative Probability ◽  
Copula Functions ◽  
Flood Peak ◽  
Multiple Variables

Abstract Historically, severe floods have caused great human and financial losses. Therefore, the flood frequency analysis based on the flood multiple variables including flood peak, volume and duration poses more motivation for hydrologists to study. In this paper, the bivariate and trivariate flood frequency analysis and modeling using Archimedean copula functions is focused. For this purpose, the annual flood data over a 55-year historical period recorded at the Dez Dam hydrometric station were used. The results showed that based on goodness of fit criteria, the Frank function built upon the couple of the flood peak-volume and the couple of the flood peak-duration as well as the Clayton function built upon the flood volume-duration were identified to be the best copula families to be adopted. The trivariate analysis was conducted and the Clayton family was chosen as the best copula function. Thereafter, the common and conditional cumulative probability distribution functions were built and analyzed to determine the periodic "and", "or" and "conditional" bivariate and trivariate flood return periods. The results suggest that the bivariate conditional return period obtained for short-term periods is more reliable than the trivariate conditional return period. Additionally, the trivariate conditional return period calculated for long-term periods is more reliable than the bivariate conditional return period.

scholarly journals Joint return periods in hydrology: a critical and practical review focusing on synthetic design hydrograph estimation

2012 ◽  
Vol 9 (5) ◽  
pp. 6781-6828 ◽  
Author(s):  
S. Vandenberghe ◽  
M. J. van den Berg ◽  
B. Gräler ◽  
A. Petroselli ◽  
S. Grimaldi ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Return Period ◽  
Three Dimensional ◽  
Distribution Functions ◽  
Return Periods ◽  
Design Variables ◽  
Joint Return ◽  
Design Values ◽  
Multivariate Frequency Analysis ◽  
Joint Return Period

Abstract. Most of the hydrological and hydraulic studies refer to the notion of a return period to quantify design variables. When dealing with multiple design variables, the well-known univariate statistical analysis is no longer satisfactory and several issues challenge the practitioner. How should one incorporate the dependence between variables? How should the joint return period be defined and applied? In this study, an overview of the state-of-the-art for defining joint return periods is given. The construction of multivariate distribution functions is done through the use of copulas, given their practicality in multivariate frequency analysis and their ability to model numerous types of dependence structures in a flexible way. A case study focusing on the selection of design hydrograph characteristics is presented and the design values of a three-dimensional phenomenon composed of peak discharge, volume and duration are derived. Joint return period methods based on regression analysis, bivariate conditional distributions, bivariate joint distributions, and Kendal distribution functions are investigated and compared highlighting theoretical and practical issues of multivariate frequency analysis. Also an ensemble-based method is introduced. For a given design return period, the method chosen clearly affects the calculated design event. Eventually, light is shed on the practical implications of a chosen method.

Multivariate Frequency Analysis of Meteorological Drought Using Copula

2018 ◽  
Vol 32 (5) ◽  
pp. 1741-1758 ◽  
Author(s):  
Lamneithem Hangshing ◽  
Parmendra P. Dabral
Keyword(s):  
Frequency Analysis ◽  
Meteorological Drought ◽  
Multivariate Frequency Analysis

scholarly journals Modelling High Dimensional Paddy Production Data using Copulas

2021 ◽  
Vol 29 (1) ◽  
Author(s):  
Nuranisyha Mohd Roslan ◽  
Wendy Ling Shinyie ◽  
Sim Siew Ling
Keyword(s):  
Time Series ◽  
Marginal Distribution ◽  
Reference Group ◽  
Estimation Method ◽  
Information Criterion ◽  
Copula Function ◽  
Archimedean Copula ◽  
Copula Functions ◽  
Estimation Errors ◽  
Elliptical Copula

As the climate change is likely to be adversely affecting the yield of paddy production, thence it has brought a limelight of the probable challenges on human particularly regional food security issues. This paper aims to fit multivariate time series of paddy production variables using copula functions and predicts the next year event based on the data of five countries in southeast Asia. In particular, the most appropriate marginal distribution for each univariate time series was first identified using maximum likelihood parameter estimation method. Next, we performed multivariate copula fitting using two types of copula families, namely, elliptical copula family and Archimedean copula family. Elliptical copula family studied are normal and t copula, while Archimedean copula family considered are Joe, Clayton and Gumbel copulas. The performance of marginal distribution and copula fitting was examined using Akaike information criterion (AIC) values. Finally, we used the best fitted copula model to forecast the succeeding event. In order to assess the performance of copula function, we computed the forecast means and estimation errors of copula function with a generalized autoregressive conditional heteroskedasticity model as reference group. Based on the smallest AIC, the majority of the data favoured the Gumbel copula, which belongs to Archimedean copula family as well as extreme value copula family. Likewise, applying the historical data to forecast the future trends may assist all relevant stakeholders, for instance government, NGO agencies, and professional practitioners in making informed decisions without compromising the environmental as well as economical sustainability in the region.

scholarly journals Modelling of bivariate meteorological drought analysis in Lake Urmia Basin using Archimedean copula functions

2021 ◽  
Vol 28 (6) ◽  
Author(s):  
Farzad Khezri ◽  
Mohsen Irandoost ◽  
Navid Jalalkamali ◽  
Najme Yazdanpanah
Keyword(s):  
Meteorological Drought ◽  
Archimedean Copula ◽  
Lake Urmia ◽  
Copula Functions ◽  
Drought Analysis

Modeling of joint rainfall rate and rain attenuation statistics using Archimedean Copula functions

2014 ◽  
Author(s):  
Charilaos I. Kourogiorgas ◽  
Spiros N. Livieratos ◽  
Athanasios D. Panagopoulos ◽  
George E. Chatzarakis
Keyword(s):  
Rain Attenuation ◽  
Rainfall Rate ◽  
Archimedean Copula ◽  
Copula Functions

Application of bi-Gaussian S-transform in high-resolution seismic time-frequency analysis

2017 ◽  
Vol 5 (1) ◽  
pp. SC1-SC7 ◽  
Author(s):  
Zixiang Cheng ◽  
Wei Chen ◽  
Yangkang Chen ◽  
Ying Liu ◽  
Wei Liu ◽  
...  
Keyword(s):  
High Resolution ◽  
Frequency Analysis ◽  
Time Resolution ◽  
Wavelet Transforms ◽  
Superior Performance ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Gaussian Window ◽  
Hydrocarbon Reservoir ◽  
S Transform

The S-transform is one of the most widely used methods of time-frequency analysis. It combines the respective advantages of the short-time Fourier transform and wavelet transforms with scale-dependent resolution using Gaussian windows, scaled inversely with frequency. One of the problems with the traditional symmetric Gaussian window is the degradation of time resolution in the time-frequency spectrum due to the long front taper. We have studied the performance of an improved S-transform with an asymmetric bi-Gaussian window. The asymmetric bi-Gaussian window can obtain an increased time resolution in the front direction. The increased time resolution can make event picking high resolution, which will facilitate an improved time-frequency characterization for oil and gas trap prediction. We have applied the slightly modified bi-Gaussian S-transform to a synthetic trace, a 2D seismic section, and a 3D seismic cube to indicate the superior performance of the bi-Gaussian S-transform in analyzing nonstationary signal components, hydrocarbon reservoir predictions, and paleochannels delineations with an obviously higher resolution.

Reliability Prediction of RV Reducer Based on Fault Tree and Monte-Carlo Simulation

2013 ◽  
Vol 274 ◽  
pp. 663-666 ◽  
Author(s):  
Y.G. Sun ◽  
G.B. Yu ◽  
J. Zhang ◽  
F. Wang ◽  
Y.Q. Sun ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Frequency Analysis ◽  
Time Resolution ◽  
High Frequency ◽  
Fault Tree ◽  
Low Frequency ◽  
Frequency Resolution ◽  
Potential System ◽  
Rv Reducer ◽  
System Life

Wavelet de-nosing method for complex signal is put forward in this paper. The time resolution and frequency resolution are changed as wavelet transform for signal analysis. It uses high-frequency resolution and low time resolution in low frequency analysis, and uses low-frequency resolution and high time resolution in high frequency analysis. So it fit the uncertainty principle and realize the signal time domain and in frequency domain at the same time. In this paper, the configuration of RV reducer is briefly introduced, and its fault tree is constructed taking the fault of “Output shaft can not transfer torque” as top event through illuminating potential system unit failures and analyze the effect on whole system. And then system reliability qualitative and quantitative analysis are conducted. The fault tree qualitative analysis is operated based on the minimal cut set. Followed establishing simulation model of RV reducer, system life time is obtained using Monte-Carlo random sampling method. Furthermore, system life distribution is deduced, and point and confidence interval of distribution parameters and reliability characters are given by Maximum Likelihood Estimate. Finally, simulation experiment and results analysis are given to show the effectiveness of this method.

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