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Characterization of geological uncertainties from limited boreholes using copula-based coupled Markov chains for underground construction

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Underground Space 2024年第3期16卷 94-105页

作者： Fan Wang Heng Li Gang Li Zheng-Jun You Elton J.Chen National Center of Technology Innovation for Digital Construction Huazhong University of Science and TechnologyWuhan 430074China Department of Civil and Hydraulic Engineering Huazhong University of Science and TechnologyWuhan 430074China Department of Building and Real Estate The Hong Kong Polytechnic UniversityKowloon 999077Hong KongChina Shanghai Tunnel Engineering Co. Ltd.Shanghai 200032China Wuhan Metro Group Co.Ltd. Wuhan 430070China

This paper proposes an efficient method for quantifying the stratigraphic uncertainties and modeling the geological formations based on *** Markov chains are used to describe the soil transitions along different directions,and the transition probability matrices(TPMs)of the Markov chains are analytically expressed by *** copula expression is efficient since it can represent a large TPM by a few unknown *** to the analytical expression of the TPMs,the likelihood function of the Markov chain model is given in an explicit *** estimation of the TPMs is then re-casted as a multi-objective constrained optimization problem that aims to maximize the likelihoods of two independent Markov chains subject to a set of parameter *** the method which determines the TPMs by counting the number of transitions between soil types,the proposed method is more statistically ***,a random path sampling method is presented to avoid the directional effect problem in *** soil type at a location is inferred from its nearest known neighbors along the cardinal directions.A general form of the conditional probability,based on Pickard’s theorem and Bayes rule,is presented for the soil type *** proposed stratigraphic characterization and simulation method is applied to real borehole data collected from a construction site in Wuhan,*** is illustrated that the proposed method is accurate in prediction and does not show an inclination during simulation.

关键词： Geological stratigraphy Markov chain Transition probability matrix Statistical method copula

Multi-objective interval prediction of wind power based on conditional copula function

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Journal of Modern Power Systems and Clean Energy 2019年第4期7卷 802-812页

作者： Gang ZHANG Zhixuan LI Kaoshe ZHANG Lei ZHANG Xia HUA Yongqing WANG Institute of Water Resources and Hydro-Electric Engineering Xi’an University of TechnologyXi’an 710048China State Grid Shaanxi Baoji Electric Power Company Baoji 721000China State Grid Gansu Electric Power Company Gansu Electric Power Research InstituteLanzhou 730050China State Grid Shaanxi Electric Power Company Shaanxi Electric Power Research InstituteXi’an 710054China

Interval prediction of wind power,which features the upper and lower limits of wind power at a given confidence level,plays a significant role in accurate prediction and stability of the power grid integrated with wind ***,the conventional methods of interval prediction are commonly based on a hypothetic probability distribution function,which neglects the correlations among various variables,leading to the decrease of prediction ***,we improve the multi-objective interval prediction based on the conditional copula function,through which we can fully utilize the correlations among variables to improve prediction accuracy without an assumed probability distribution *** use the multi-objective optimization method of nondominated sorting genetic algorithm-II(NSGA-II)to obtain the optimal solution *** particular best solution is weighted by the prediction interval average width(PIAW)and prediction interval coverage probability(PICP)to pick the optimized solution in practical ***,we apply the proposed method to three wind power plants in different cities in China as examples forvalidation and obtain higher prediction accuracy compared with other methods,i.e.,relevance vector machine(RVM),artificial neural network(ANN),and particle swarm optimization kernel extreme learning machine(PSO-KELM).These results demonstrate the superiority and practicability of this method in interval prediction of wind power.

关键词： Wind power prediction Interval prediction Conditional copula function Empirical distribution function Multi-objective optimization algorithm

DEPENDENCE BETWEEN STOCK RETURNS AND INVESTOR SENTIMENT IN CHINESE MARKETS:A copula APPROACH

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Journal of Systems Science & Complexity 2012年第3期25卷 529-548页

作者： Xunfa LU Kin Keung LAI Liang LIANG Department of Management Sciences City University of Hong KongHong KongChina School of Business University of Science and Technology of China

Using data of newly opened stock trading accounts in China as a proxy of investor sentiment index, the authors employ the time-varying copula-GARCH model with Hansen＇s skewed Student-t innovations to investigate the dynamic dependence between investor sentiment and stock returns. The empirical findings show that shifts in investor sentiment are asymptotically positively correlated to stock returns in extreme value situations in both A shares market and B shares market in China, that is to say, stock prices will increase （decrease） more when investors become more bullish （bearish）. Also, results show that the dependence between investor sentiment and stock returns is time-varying, which means that the traditional Pearson＇s correlation based on normal distribution is not enough to describe the relationship between stock market behavior and investor behavior.

关键词： Behavioral finance copula GARCH investor sentiment newly opened stock trading accounts.

A Reliability Allocation Method of CNC Lathes Based on copula Failure Correlation Model

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Chinese Journal of Mechanical Engineering 2018年第6期31卷 128-136页

作者： Hao Wang Yi-Min Zhang Zhou Yang School of Mechanical Engineering and Automation Northeastern University Shenyang 110819 China Equipment Reliability Institute Shenyang University of Chemical Technology Shenyang 110142 China

The current research of reliability allocation of CNC lathes always treat CNC lathes as independent series systems. However, CNC lathes are complex systems in the actual situation. Failure correlation is rarely considered when reliabil?ity allocation is conducted. In this paper, drawbacks of reliability model based on failure independence assumption are illustrated, after which, reliability model of CNC lathes considering failure correlation of subsystems is established based on copula theory, which is an improvement of traditional reliability model of series systems. As the failure time of CNC lathes often obeys Weibull or exponential distribution, Gumbel copula is selected to build correlation model. After that, a reliability allocation method considering failure correlation is analyzed based on the model established before. Reliability goal is set first and then failure rates are allocated to subsystems according to the allocation vector through solving the correlation model. Reliability allocation is conducted for t = 1. A real case of a CNC lathe and a numerical case are presented together to illustrate the advantages of the reliability model established consider?ing failure correlation and the corresponding allocation method. It shows that the model accords to facts and real working condition more, and failure rates allocated to all the subsystems are increased to some extent. This research proposes a reliability allocation method which takes failure correlation among subsystems of CNC lathes into consid?eration, and costs for design and manufacture could be decreased.

关键词： CNC lathe copula Failure correlation Reliability model Reliability allocation

Design Tide Hydrograph with A Given Risk Threshold by A copula-Based Multivariate Method

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China Ocean Engineering 2017年第4期31卷 504-509页

作者： YANG Xing WANG Wei Hydraulic Research Institute of Jiangsu Nanjing 210017 China Jiangsu Water Conservancy Project Construction Bureau Nanjing 210029 China

This paper describes the development of a T-year design tide hydrograph （DTH）. A core innovation is that the proposed technique uses the design risk threshold and copula-based conditional risk probability to analyze the optimal combination of high waters and low waters of the DTH. A brief description of the method is presented. The in situ semi-diurnal tide data at the coast of Jiangsu Province in China are analyzed. Marginal distributions for high waters and low waters of tides are examined. Furthermore, the joint distributions, condition risk probabilities and risk thresholds of high waters and low waters are presented. Results of the DTH from the proposed method are compared with those from the traditional same-multiple enlarging design approach. It is demonstrated that the proposed method is preferable.

关键词： semi-diurnal tide design tide hydrograph marginal distribution joint distribution risk threshold copula

Using copula functions to predict climatic change impacts on floods in river source regions

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Advances in Climate Change Research 2024年第3期15卷 406-418页

作者： Ting-Xing CHEN Hai-Shen LYU Robert HORTON Yong-Hua ZHU Ren-Sheng CHEN Ming-Yue SUN Ming-Wen LIU Yu LIN The National Key Laboratory of Water Disaster Prevention College of Hydrology and Water ResourcesHohai UniversityNanjing 2098China College of Hydrology and Water Resources Hohai UniversityNanjing 210098China Departmentof Agronomy lowa State UniversityAmes IA5oollUSA Qilian Alpine Eology and Hydrology Research Station Northwest Instiuteof Eco-Environmentand ReourcesChinese Academy of SciencesLanzhou0000 China

Flood frequency in river source regions is significantly affected by rainfall and snowmelt as part of climatic changes.A traditional univariate flood frequency analysis cannot reflect the complexity of floods,and when used in isolation,it can only underestimate flood *** effective flood prevention and mitigation,it is essential to consider the combined effects of precipitation and *** functions can effectively quantify the joint distribution relationship between floods and their associated variables without restrictions on their distribution *** study uses copula functions to consider a multivariate probability distribution model of flood peak flow(Q)with cumulative snowmelt(CSm)and cumulative precipitation(CPr)for the Hutubi River basin located in northern Xinjiang,*** joint frequencies of rainfall and snowmelt floods are predicted using copula models based on the Coupled Model Intercomparison Project Phase 6 *** results show that Q has a significant positive correlation with 24-d CSm(r=0.559,p=0.002)and 23-d CPr(r=0.965,p<0.05).Flood frequency will increase in the future,and mid-(2050e2074)and long-term(2075e2099)floods will be more severe than those in the near-term(2025e2049).The probability of flood occurrence is higher under the SSP2-4.5 and SSP1-2.6 scenarios than under *** during the historical period(1990e2014)led to extreme floods,and increasing future precipitation trends are found to be *** increases with rising temperatures and occurs earlier than estimated,leading to an earlier flood period in the basin and more frequent snowmelt *** Q under the joint return period is larger than that during the same univariate return *** difference indicates that neglecting the interaction between precipitation and snowmelt for floods leads to an underestimation of the flood risk(with underestimations ranging from 0.3%to 22%).The underestimations decrease with an increase

关键词： Climate change Flood frequency copula CMIP6 River source regions

The chaotic behavior among the oil prices, expectation of investors and stock returns: TAR-TR-GARCH copula and TAR-TR-TGARCH copula

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Petroleum Science 2019年第1期16卷 217-228页

作者： Melike Bildirici Faculty of Economics and Administrative Sciences Y?ld?z Technical University Davutpasa Campus

This paper has two aims. The first one is to investigate the existence of chaotic structures in the oil prices, expectations of investors and stock returns by combining the Lyapunov exponent and Kolmogorov entropy, and the second one is to analyze the dependence behavior of oil prices, expectations of investors and stock returns from January 02, 1990, to June06, 2017. Lyapunov exponents and Kolmogorov entropy determined that the oil price and the stock return series exhibited chaotic behavior. TAR-TR-GARCH and TAR-TR-TGARCH copula methods were applied to study the co-movement among the selected variables. The results showed significant evidence of nonlinear tail dependence between the volatility of the oil prices, the expectations of investors and the stock returns. Further, upper and lower tail dependence and comovement between the analyzed series could not be rejected. Moreover, the TAR-TR-GARCH and TAR-TR-TGARCH copula methods revealed that the volatility of oil price had crucial effects on the stock returns and on the expectations of investors in the long run.

关键词： Oil price Expectations of investors - Stock returns Chaos Lyapunov exponent Kolmogorov entropy TAR-TR-GARCH and TAR-TR-TGARCH copula methods

Joint probability analysis of water and sediment and predicting sediment load based on copula function

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International Journal of Sediment Research 2022年第5期37卷 639-652页

作者： Haoyu Jin Xiaohong Chen Ruida Zhong Yingjie Pan Tongtiegang Zhao Zhiyong Liu Xinjun Tu School of Civil Engineering Sun Yat-sen University Guangdong Engineering Technology Research Center of Water Security Regulation and Control for Southern China Sun Yat-sen University Key Laboratory of Water Cycle and Water Security in Southern China of Guangdong High Education Institute Sun Yat-sen University Dongjiang Water Source Project Management Division of Shenzhen

The Jinsha River comprises the upper reaches of the Yangtze River, which is the river section with the highest sediment content. Monitoring of sediment transport in the Jinsha River is done to the guarantee for the normal operation of the Three Gorges Reservoir. In the current study, a copula function was used to do a joint probability analysis of the water and sediment in the Jinsha River Basin(JRB), further a sediment load prediction model based on the copula function also was constructed. The results show that the average annual flow from 2001 to 2018 at the outlet of the Jinsha River(Yibin station) is about60.43 billion m3, and the average annual sediment load is about 58.82 million t. The linear correlation coefficient between annual flow and annual sediment load is 0.28. The best marginal distribution for annual flow and sediment load is Pearson Type Three(PE3) and Generalized Normal(GNO), respectively,and the best fit for the combined distribution of the two variables is the Frank copula function. The synchronous probability of water and sediment occurrence is 0.459, and the asynchronous probability is0.541. Based on the copula prediction model, the sediment load can be effectively simulated, and the correlation coefficient between the simulated sequence and the measured sequence reached 0.93. The current study provides important significance for the analysis of water and sediment in the JRB, which is beneficial to the management of Three Gorges Reservoir sediment discharge in the upstream and downstream.

关键词： Marginal distribution Joint distribution copula Flow Sediment load Jinsha River Basin (JRB)

Dependence Tree Structure Estimation via copula

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International Journal of Automation and computing 2012年第2期9卷 113-121页

作者： Jian Ma Zeng-Qi Sun Sheng Chen Hong-Hai Liu Department of Computer Science Tsinghua University Beijing 100084 PRC Electronics and Computer Science Faculty of Physical and Applied Sciences University of Southampton Southampton SO17 1B J UK Faculty of Engineering King Abdulaziz University Jeddah 21589 Saudi Arabia Intelligent Systems and Robotics Research Group School of Creative Technologies University of Portsmouth Portsmouth PO1 2D J UK

We propose an approach for dependence tree structure learning via copula. A nonparametric algorithm for copula estimation is presented. Then a Chow-Liu like method based on dependence measure via copula is proposed to estimate maximum spanning bivariate copula associated with bivariate dependence relations. The main advantage of the approach is that learning with empirical copula focuses on dependence relations among random variables, without the need to know the properties of individual variables as well as without the requirement to specify parametric family of entire underlying distribution for individual variables. Experiments on two real-application data sets show the effectiveness of the proposed method.

关键词： copula empirical copula dependence tree structure learning probability distribution.