A C-Vine Copula-Based Quantile Regression Method for Streamflow Forecasting in Xiangxi River Basin, China

Li, Huawei; Huang, Guohe; Li, Yongping; Sun, Jie; Gao, Pan

doi:10.3390/su13094627

Cited by 5 publications

(5 citation statements)

References 66 publications

(69 reference statements)

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“…The drawable vine (D-vine) and canonical vine (C-vine) are two common structures of vines (Li et al 2021). It is necessary to mention that the tree sequence of the vine copula in The main difference between C-and D-vine copulas is their tree sequence and the choice of roots and nodes in dimensions more than 3.…”

Section: C-and D-vine Structurementioning

confidence: 99%

“…It is necessary to mention that the tree sequence of the vine copula in The main difference between C-and D-vine copulas is their tree sequence and the choice of roots and nodes in dimensions more than 3. The C-vine copula has a star shape in the tree sequence, while the D-vine copula has a straight structure (Li et al 2021). Similarly, in the tree T2, in the C-vine copula (Figure 3, Left), e = 2,3|1 is edge, 1,3 and 1,2 call the node and root, respectively and for D-vine copula (Figure 3, Right), e = 3,2|1 is edge, 3,1 and 1,2 call the node…”

Section: C-and D-vine Structurementioning

confidence: 99%

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Application of vine copulas to estimate dew point temperature

Ramezani

Tahroudi

Sedighi

2023

Atm.

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In this study, using simulations based on vine copulas such as C-, D- and R-vine copulas, the accuracy of the copula-based model in simulation of the dew point temperature in various climates of Iran was investigated. By examining the various vine copulas and their tree sequences, the selection of the best copula and best tree sequence based on AIC, BIC and Loglike criteria was done. The results showed that among the C-, D- and R-vine copulas, C- and D-vine copulas fit well on the minimum and maximum air temperatures and dew point temperature. The simulation results were analyzed using RMSE, NSE and violin plot. The results showed that the copula-based model has a high accuracy at all stations. The min (max) RMSE is related to Kerman (Ahvaz) station with RMSE= 0.396 oC (0.617 oC). Also, the min (max) NSE is related to Ahvaz (Urmia) station with NSE= 0.925 (0.955). Also, according to the violin plot, it is possible to see the acceptable certainty of the copula-based model. Due to the diversity of the tree sequences of vine copulas and the use of the rotational states of the internal vine copulas, as well as the possibility of interfering with the effective parameters in high dimensions, the simulation results are reliable and have no restrictions. This model can be used as the best model to estimate dew point temperature due to the full coverage of the range of changes in data.

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Section: C-and D-vine Structurementioning

confidence: 99%

Section: C-and D-vine Structurementioning

confidence: 99%

Application of vine copulas to estimate dew point temperature

Ramezani

Tahroudi

Sedighi

2023

Atm.

View full text Add to dashboard Cite

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“…From the many studies that were performed in previous years, it is now known that flow and watershed characteristics should be log-transformed to form a near-linear relationship. Many studies have also previously shown the application of the Ordinary Least Squares technique for estimating streamflow by employing stream gauging stations [11][12][13]. However, grouping the stations according to their watershed and meteorological characteristics and then engaging them in a regression analysis, whether linear or non-linear, is a concept yet to be fully explored in different climatic regions of the world.…”

Section: Regression Analysismentioning

confidence: 99%

“…The application of many global and regional climate models in CORDEX has helped to characterize the strength and the weaknesses of several regional climate models in terms of their climate projection capabilities for the selected basins [10,11]. Regression approaches in ungauged regions often rely on empirical data, despite not offering an understanding of the watershed hydrology and characteristics, but their data-driven methodology helps obtain useful correlations between the precipitation and streamflow without the need of intense variable availability that is required for physical models [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Streamflow Estimation through Coupling of Hieararchical Clustering Analysis and Regression Analysis—A Case Study in Euphrates-Tigris Basin

Guzey,

Onoz

2023

Analytics

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In this study, the resilience of designed water systems in the face of limited streamflow gauging stations and escalating global warming impacts were investigated. By performing a regression analysis, simulated meteorological data with observed streamflow from 1971 to 2020 across 33 stream gauging stations in the Euphrates-Tigris Basin were correlated. Utilizing the Ordinary Least Squares regression method, streamflow for 2020–2100 using simulated meteorological data under RCP 4.5 and RCP 8.5 scenarios in CORDEX-EURO and CORDEX-MENA domains were also predicted. Streamflow variability was calculated based on meteorological variables and station morphological characteristics, particularly evapotranspiration. Hierarchical clustering analysis identified two clusters among the stream gauging stations, and for each cluster, two streamflow equations were derived. The regression analysis achieved robust streamflow predictions using six representative climate variables, with adj. R2 values of 0.7–0.85 across all models, primarily influenced by evapotranspiration. The use of a global model led to a 10% decrease in prediction capabilities for all CORDEX models based on R2 performance. This study emphasizes the importance of region homogeneity in estimating streamflow, encompassing both geographical and hydro-meteorological characteristics.

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“…Copula theory, which has been rapidly developed in hydrological and geotechnical engineering in recent years, provides an effective way to solve the parameter correlation problem [11][12][13][14]. The basic content of Copula theory can be summarized as follows: identify the marginal distribution of each variable as well as pick a copula function to link the marginal distribution functions together.…”

Section: Introductionmentioning

confidence: 99%

Identification Method of Optimal Copula Correlation Characteristic for Geological Parameters of Roof Structure

Cao,

Wang,

Zhu

et al. 2023

Sustainability

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Limited by the actual investigation of coal mine engineering, the measured data obtained are often based on small sample characteristics. How to probabilistically de-integrate the prior information to obtain meaningful statistical values has received increasing attention from geotechnical engineers. In this study, an optimal copula function identification method for multidimensional geotechnical structures of coal mine roofs under the Bayesian approach is proposed. Firstly, the characterization method of multidimensional roof parameter correlation structures is proposed based on copula theory, and 167 sets of measured data from 24 coal mines at home and abroad are collected to study the measured identification results using the Bayesian method. Secondly, Monte Carlo simulation is utilized to compare the correct recognition rates of the commonly used AIC criterion and the Bayesian approach under different correlation structures. Finally, the influencing factors affecting the successful recognition rate of the Bayesian approach are analyzed. The results show that compared with the traditional AIC criterion, the Bayesian approach has more marked advantages in correctly recognizing the multidimensional parameter structures of roofs, and the number of measured samples, the strength of correlation coefficients, and the prior information have a major effect on the correct recognition rate of the optimal copula function under different real copula functions. In addition, the commonly used Gaussian copula has a better characterization effect in characterizing the multidimensional parameter correlation structure of the coal mine roofs, which can be prioritized to be used as a larger prior probability function in the evaluation process.

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A C-Vine Copula-Based Quantile Regression Method for Streamflow Forecasting in Xiangxi River Basin, China

Cited by 5 publications

References 66 publications

Application of vine copulas to estimate dew point temperature

Application of vine copulas to estimate dew point temperature

Streamflow Estimation through Coupling of Hieararchical Clustering Analysis and Regression Analysis—A Case Study in Euphrates-Tigris Basin

Identification Method of Optimal Copula Correlation Characteristic for Geological Parameters of Roof Structure

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