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An important aim of modern geostatistical modeling is to quantify uncertainty in geological systems. Geostatistical modeling
requires many input parameters. The input univariate distribution or histogram is perhaps the most important. A new method
for assessing uncertainty in the histogram, particularly uncertainty in the mean, is presented. This method, referred to as
the conditional finite-domain (CFD) approach, accounts for the size of the domain and the local conditioning data. It is a
stochastic approach based on a multivariate Gaussian distribution. The CFD approach is shown to be convergent, design independent,
and parameterization invariant. The performance of the CFD approach is illustrated in a case study focusing on the impact
of the number of data and the range of correlation on the limiting uncertainty in the parameters. The spatial bootstrap method
and CFD approach are compared. As the number of data increases, uncertainty in the sample mean decreases in both the spatial
bootstrap and the CFD. Contrary to spatial bootstrap, uncertainty in the sample mean in the CFD approach decreases as the
range of correlation increases. This is a direct result of the conditioning data being more correlated to unsampled locations
in the finite domain. The sensitivity of the limiting uncertainty relative to the variogram and the variable limits are also
discussed. 相似文献
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Metropolitan areas consist of complicated systems of interconnected infrastructures that are highly interdependent. Disruption of one infrastructure may induce disruption in other interconnected ones. The results from analysis of one infrastructure as an independent system are not realistic without considering the behavior of other interconnected infrastructures. Consequently, the study of the interdependencies among critical infrastructures is important for addressing the cascading effects of a failed infrastructure on the entire network to properly model its performance and help the disaster management team in decision making. In this study, the extended Petri net and Markov chain have been used to demonstrate the power and water infrastructure interdependency with a case study of one of the municipal districts of metropolitan Tehran, the capital of Iran. In this research, three cases have been assessed quantitatively: (1) the intra-dependency effects of different components in each network, (2) the interdependency effects between the considered critical infrastructures and (3) the behavior of the water network considering intra- and interdependency, when the power network fails. The analyses show that considering the mentioned interdependencies has a major influence on their performance simulations and assessment of their exact vulnerability. It is concluded that the failure probability of the water network that is dependent on the failed power network is 1.66 of the independent water network in the studied region. Eventually, the results of the research could be used in design, restoration and disaster management planning for safety assessment of critical infrastructures. 相似文献
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Masoud Mirmomeni Caro Lucas Babak Nadjar Araabi Behzad Moshiri Mohammad Reza Bidar 《Solar physics》2011,272(1):189-213
The time-varying Sun as the main source of space weather affects the Earth??s magnetosphere by emitting hot magnetized plasma in the form of solar wind into interplanetary space. Solar and geomagnetic activity indices and their chaotic characteristics vary abruptly during solar and geomagnetic storms. This variation depicts the difficulties in modeling and long-term prediction of solar and geomagnetic storms. On the other hand, the combination of neurofuzzy models and spectral analysis has been a subject of interest due to their many practical applications in modeling and predicting complex phenomena. However, these approaches should be trained by algorithms that need to be carried out by an offline data set, which influences their performance in online modeling and prediction of time-varying phenomena. This paper proposes an adaptive approach for multi-step ahead prediction of space weather indices by extending the regular singular spectrum analysis and locally linear neurofuzzy models to adaptive approaches. The combination of these recursive approaches fulfills requirements of long-term prediction of solar and geomagnetic activity indices. The results demonstrate the power of the proposed method in online prediction of space weather indices. 相似文献