A three-level framework for multi-risk assessment

The effective management of the risks posed by natural and man-made hazards requires all relevant threats and their interactions to be considered. This paper proposes a three-level framework for multi-risk assessment that accounts for possible hazard and risk interactions. The first level is a flow chart that guides the user in deciding whether a multi-hazard and risk approach is required. The second level is a semi-quantitative approach to explore if a more detailed, quantitative assessment is needed. The third level is a detailed quantitative multi-risk analysis based on Bayesian networks. Examples that demonstrate the application of the method are presented.     

A Bayesian network approach for predicting seismic liquefaction based on interpretive structural modeling

The Bayesian network (BN) is a type of graphical network based on probabilistic inference that has been gradually applied to assessment of seismic liquefaction potential. However, how to construct a robust BN remains underexplored in this field. This paper aims to present an efficient hybrid approach combining domain knowledge and data to construct a BN that facilitates the integration of multiple factors and the quantification of uncertainties within a network model to assess seismic liquefaction. Initially, only using given domain knowledge, a naive network model can be constructed using interpretive structural modeling. Thereafter, some effective information about the naive model is provided to construct a robust model using structural learning of BN from historic data. Finally, the returning predictive results and the predictive results are compared to other methods including non-probabilistic and probabilistic models for seismic liquefaction using the metrics of the overall accuracy, the area under the curve of receiver operating characteristic, prediction, recall and F₁ score. The methodology proposed in this paper achieved better performance, and we discussed the power and value of the proposed approach at the end of this paper, which suggest that BN is a good alternative tool for seismic liquefaction prediction.     

Improvement of X-Band Polarization Radar Melting Layer Recognition by the Bayesian Method and ITS Impact on Hydrometeor Classification

Using melting layer(ML)and non-melting layer(NML)data observed with the X-band dual linear polarization Doppler weather radar(X-POL)in Shunyi,Beijing,the reflectivity(ZH),differential reflectivity(ZDR),and correlation coefficient(CC)in the ML and NML are obtained in several stable precipitation processes.The prior probability density distributions(PDDs)of the ZH,ZDR and CC are calculated first,and then the probabilities of ZH,ZDR and CC at each radar gate are determined(PBB in the ML and PNB in the NML)by the Bayesian method.When PBB>PNB the gate belongs to the ML,and when PBBPNB the gate belongs to the ML,and when PBB相似文献   

Relative sea‐level observations in western Scotland since the Last Glacial Maximum for testing models of glacial isostatic land movements and ice‐sheet reconstructions

Observations of relative sea‐level change and local deglaciation in western Scotland provide critical constraints for modelling glacio‐isostatic rebound in northern Britain over the last 18 000 years. The longest records come from Skye, Arisaig and Knapdale with a shorter, Holocene, record from Kintail. Biostratigraphic (diatom, pollen, dinoflagellate, foraminifera and thecamoebian), lithological and radiocarbon analyses provide age and elevation parameters for each sea‐level index point. All four sites reveal relative sea‐level change that is highly non‐monotonic in time as the local vertical component of glacio‐isostatic rebound and eustasy (or global meltwater influx) dominate at different periods. Copyright © 2006 John Wiley & Sons, Ltd.     

活动星系核统一模式的统计检验

本文对视超光速源样本的有关数据进行统计分析，检验活动星系核统一模式。给出各类视超光速源的Ｒ（核光度与扩展区光度之比）分布和喷流与视线的平均夹角　。分析源样本的Ｒ、洛伦兹因子ｒ、喷流的投影尺度ｄ和线性尺度ｌ间的关系，发现Ｒ和ｄ之间存在反相关、ｒ和ｌ之间存在相关的特性，这些结果有利于说明活动星系核统一模式的取向效应。     

A hybrid Bayesian back‐propagation neural network approach to multivariate modelling

There is growing interest in the use of back‐propagation neural networks to model non‐linear multivariate problems in geotehnical engineering. To overcome the shortcomings of the conventional back‐propagation neural network, such as overfitting, where the neural network learns the spurious details and noise in the training examples, a hybrid back‐propagation algorithm has been developed. The method utilizes the genetic algorithms search technique and the Bayesian neural network methodology. The genetic algorithms enhance the stochastic search to locate the global minima for the neural network model. The Bayesian inference procedures essentially provide better generalization and a statistical approach to deal with data uncertainty in comparison with the conventional back‐propagation. The uncertainty of data can be indicated using error bars. Two examples are presented to demonstrate the convergence and generalization capabilities of this hybrid algorithm. Copyright © 2003 John Wiley & Sons, Ltd.     

Bayesian Discovery Process Modeling of the Lower and Middle Jurassic Play of the Halten Terrace,Offshore Norway,as Compared with the Previous Modeling

A Bayesian version of the discovery process model was applied to the pre-rift Lower and Middle Jurassic play of the Halten Terrace, Mid-Norway. The Bayesian approach estimates the lognormal parameters, the discoverability parameter, and the distribution of sizes of the undiscovered fields as well as the play potential, conditioned on a discovery sequence averaging for all possible prior choices weighted by their likelihood. This approach avoids the problem of having to make arbitrary choices for the parameters. The estimates of parameters and play potential based upon the present methodology compares well with previous estimates, if the play is divided into two sub-plays representing the overpressured and normally pressured zones. These sub-plays have been estimated independently and aggregated in order to get the total undiscovered resource potential. This study estimates that the expected remaining play potential is 100 × 10⁶Sm³ o.e., about 9% of the total resources in the play. There is however a 90% chance that the remaining potential ranges from 13 to 282× 10⁶ Sm³ o.e. and a 5% possibility of exceeding this value.     

Foraminifera from the Irish Sea glacigenic deposits at Aberdaron,western Lleyn,North Wales: Palaeoenvironmental implications

In a sequence of glacigenic sediments at Aberdaron, Foraminifera were obtained from samples located specifically in order to differentiate between opposing models of depositional environment. All the diamict samples yielded remarkably uniform assemblages, with similar numbers of benthic specimens and benthic species per unit weight of sediment, similar planktic : benthic ratios, and similar ratios of clearly allochthonous to possibly autochthonous elements. This is precisely as predicted by the terrestrial model of sedimentation, where all of the sediments are interpreted as being derived from the melting of glacier ice rich in marine debris entrained during passage along the Irish Sea Basin. The results lend no support to a glacial marine model, since no faunal responses to increasingly distal sedimentary environments are observed. However, the fauna is dominated by the Foraminifera Elphidium excavatum (Terquem) forma clavata Cushman, which is commonly assumed to indicate glacial marine conditions. The modern distribution of similar assemblages suggests that it is just as likely to represent the cold, reduced salinity conditions that would have prevailed in the northern Irish Sea Basin for much of the Quaternary.     

Probabilistic Precipitation Forecasting Based on Ensemble Output Using Generalized Additive Models and Bayesian Model Averaging

A probabilistic precipitation forecasting model using generalized additive models (GAMs) and Bayesian model averaging (BMA) was proposed in this paper.GAMs were used to fit the spatial-temporal precipi...     

长江流域夏季降水的贝叶斯多模式集合概率预测

A Bayesian probabilistic prediction scheme of the Yangtze River Valley (YRV) summer rainfall is proposed to combine forecast information from multi-model ensemble dataset provided by ENSEMBLES project.Due to the low forecast skill of rainfall in dynamic models,the time series of regressed YRV summer rainfall are selected as ensemble members in the new scheme,instead of commonly-used YRV summer rainfall simulated by models.Each time series of regressed YRV summer rainfall is derived from a simple linear regression.The predictor in each simple linear regression is the skillfully simulated circulation or surface temperature factor which is highly linear with the observed YRV summer rainfall in the training set.The high correlation between the ensemble mean of these regressed YRV summer rainfall and observation benefit extracting more sample information from the ensemble system.The results show that the cross-validated skill of the new scheme over the period of 1960 to 2002 is much higher than equally-weighted ensemble,multiple linear regression,and Bayesian ensemble with simulated YRV summer rainfall as ensemble members.In addition,the new scheme is also more skillful than reference forecasts (random forecast at a 0.01 significance level for ensemble mean and climatology forecast for probability density function).