Recent development in copula and its applications to the energy, forestry and environmental sciences

Bhatti, M. Ishaq; Quang, Hung

doi:10.1016/j.ijhydene.2019.06.015

Cited by 77 publications

(29 citation statements)

References 105 publications

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“…In particular, Aas et al (2009) have popularized two classes of vine copulas, namely canonical vines (C-vines) and drawable vines (Dvines), by allowing diverse pair copula families, such as the bivariate Student t copula and bivariate Clayton copula. After a seminal paper, those two vines have been used in many fields, including economics (Arreola Hernandez et al, 2017;Zimmer, 2015), finance (Dissmann et al, 2013;Lu, 2013) and engineering (Bhatti and Do, 2019;Erhardt et al, 2015;Xu et al, 2017). Similarly, a few studies have used vine copulas in hydrologic applications with diverse purposes (Daneshkhah et al, 2016;Liu et al, 2015;Vernieuwe et al, 2015;Shafaei et al, 2017), although they have not been introduced to infill missing data.…”

Section: Introductionmentioning

confidence: 99%

Streamflow estimation at partially gaged sites using multiple-dependence conditions via vine copulas

Ahn

2021

Hydrol. Earth Syst. Sci.

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Abstract. Reliable estimates of missing streamflow values are relevant for water resource planning and management. This study proposes a multiple-dependence condition model via vine copulas for the purpose of estimating streamflow at partially gaged sites. The proposed model is attractive in modeling the high-dimensional joint distribution by building a hierarchy of conditional bivariate copulas when provided a complex streamflow gage network. The usefulness of the proposed model is firstly highlighted using a synthetic streamflow scenario. In this analysis, the bivariate copula model and a variant of the vine copulas are also employed to show the ability of the multiple-dependence structure adopted in the proposed model. Furthermore, the evaluations are extended to a case study of 54 gages located within the Yadkin–Pee Dee River basin in the eastern USA. Both results inform that the proposed model is better suited for infilling missing values. To be specific, the proposed multiple-dependence model shows the improvement of 9.2 % on average compared to the bivariate model from the historical case study. The performance of the vine copula is further compared with six other infilling approaches to confirm its applicability. Results demonstrate that the proposed model produces more reliable streamflow estimates than the other approaches. In particular, when applied to partially gaged sites with sufficient available data, the proposed model clearly outperforms the other models. Even though the model is illustrated by a specific case, it can be extended to other regions with diverse hydro-climatological variables for the objective of infilling.

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Section: Introductionmentioning

confidence: 99%

Streamflow estimation at partially gaged sites using multiple-dependence conditions via vine copulas

Ahn

2021

Hydrol. Earth Syst. Sci.

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“…Plants use soil water to meet their growth requirements. In contrast, plants can convert carbon dioxide and water into oxygen and organic matter after photosynthesis, releasing a large amount of oxygen while providing energy for phytophagous animals and maintaining the balance of nature [5]. erefore, it will be of great strategic importance to the sustainable development of agriculture to reasonably develop and utilize soil water resources, to explore the potential of soil water resources development and its ability to support agricultural production and maintain a good agricultural ecological environment while giving full play to the maximum advantages of soil water resources [6].…”

Section: Status Of Researchmentioning

confidence: 99%

High-Performance Computational Early Warning Analysis of Agricultural Economy Relying on Binary Fuzzy Cluster Analysis Algorithm

Tang

2021

Journal of Mathematics

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In this paper, a binary fuzzy cluster analysis algorithm is used for an in-depth study and analysis of high-performance computational early warning in the agricultural economy. The definition of interval type-two fuzzy set and its operation are summarized. Considering the uncertain information that will be encountered in the evaluation process, this paper constructs the evaluation model of rural informatization construction performance based on interval type-two fuzzy numbers, which improves the accuracy of the evaluation results. The fuzzy clustering centres are modified according to the nature of the fuzzy clustering centre matrix, and the optimal fuzzy clustering centres and optimal fuzzy clustering division matrix with consistent order are solved. Using the level eigenvalues to find out in the abundant water period, there are 4 monitoring sections of water quality evaluation results for still clean and 5 monitoring sections of water quality evaluation results for slight pollution. In the flat-water period, there are 2 monitoring sections of the water quality evaluation results that are still clean and 7 monitoring sections of the water quality evaluation results are slightly polluted. In the dry water period, the water quality evaluation results of the 9 monitoring sections are slightly polluted. The results and the use of integrated pollutant index evaluation method results are consistent, indicating that the use of fuzzy clustering model for water quality evaluation is practical and effective. Its functional structure includes management business, innovation business, service business, production business, and operation business functions, in addition to the design of the collection and storage scheme based on Hadoop data warehouse, to achieve accurate matching of information of science and technology agricultural services in the whole agricultural industry chain. Finally, the implementation and maintenance of the cloud-based science and technology agricultural service information system are deployed, and the operational effects of its information system-related functions are demonstrated using prototype design. This paper constructs a comprehensive information system for science and technology agricultural services based on cloud technology that integrates management, innovation, service, production, and operation, which meets not only the needs of traditional science and technology agricultural service information systems, such as expert consultation and training demonstration, but also other needs spawned by the development of agricultural modernization.

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“…Archimedean copulas are widely useful in diverse areas, including hydrology, medical science, accident analysis, insurance, banking, weather, climate change, and more (Bhatti and Do, (2019)). In actuarial science, Hendriks and Landsman (2017) investigated the dependence structure of multivariate random variable with the probability measure F (X 1 , ..., X n ) = P (X 1 ≥ x, ..., X 1 ≥ x n ) = h n i=1 λ i x i , x i ≥ 0, and showed the intrinsic relation of this structure with survival Archimedean copula with a generator ϕ = h −1 .…”

Section: Introductionmentioning

confidence: 99%

Fitting Compound Archimedean Copulas to Data for Modeling Electricity Demand

Kelner¹,

Landsman²,

Makov³

2021

IJSP

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Modeling dependence between random variables is accomplished effectively by using copula functions. Practitioners often rely on the single parameter Archimedean family which contains a large number of functions, exhibiting a variety of dependence structures. In this work we propose the use of the multiple-parameter compound Archimedean family, which extends the original family and allows more elaborate dependence structures. In particular, we use a copula of this type to model the dependence structure between the minimum daily electricity demand and the maximum daily temperature. It is shown that the compound Archimedean copula enhances the flexibility of the dependence structure and provides a better fit to the data.

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Recent development in copula and its applications to the energy, forestry and environmental sciences

Cited by 77 publications

References 105 publications

Streamflow estimation at partially gaged sites using multiple-dependence conditions via vine copulas

Streamflow estimation at partially gaged sites using multiple-dependence conditions via vine copulas

High-Performance Computational Early Warning Analysis of Agricultural Economy Relying on Binary Fuzzy Cluster Analysis Algorithm

Fitting Compound Archimedean Copulas to Data for Modeling Electricity Demand

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