共查询到20条相似文献，搜索用时 31 毫秒
1.
Chuanrong Zhang Weidong Li 《Stochastic Environmental Research and Risk Assessment (SERRA)》2008,22(2):217-230
Simulating fields of categorical geospatial variables from samples is crucial for many purposes, such as spatial uncertainty
assessment of natural resources distributions. However, effectively simulating complex categorical variables (i.e., multinomial
classes) is difficult because of their nonlinearity and complex interclass relationships. The existing pure Markov chain approach
for simulating multinomial classes has an apparent deficiency—underestimation of small classes, which largely impacts the
usefulness of the approach. The Markov chain random field (MCRF) theory recently proposed supports theoretically sound multi-dimensional
Markov chain models. This paper conducts a comparative study between a MCRF model and the previous Markov chain model for
simulating multinomial classes to demonstrate that the MCRF model effectively solves the small-class underestimation problem.
Simulated results show that the MCRF model fairly produces all classes, generates simulated patterns imitative of the original,
and effectively reproduces input transiograms in realizations. Occurrence probability maps are estimated to visualize the
spatial uncertainty associated with each class and the optimal prediction map. It is concluded that the MCRF model provides
a practically efficient estimator for simulating multinomial classes from grid samples. 相似文献
2.
3.
Nak-Youl Ko Kang-Kun Lee 《Stochastic Environmental Research and Risk Assessment (SERRA)》2010,24(5):649-660
We examine the effect of uncertainty due to limited information on the remediation design of a contaminated aquifer using
the pump and treat method. The hydraulic conductivity and contaminant concentration distributions for a fictitious contaminated
aquifer are generated assuming a limited number of sampling locations. Stochastic optimization with multiple realizations
is used to account for aquifer uncertainty. The optimization process involves a genetic algorithm (GA). As the number of realizations
increases, a greater extraction rate and more wells are needed. There was a total cost increase, but the optimal remediation
designs became more reliable. Stochastic optimization analysis also determines the locations for extraction wells, the variation
in extraction rates as a function of the change of well locations, and the reliability of the optimal designs. The number
of realizations (stack number) that caused the design factors to converge could be determined. Effective stochastic optimization
may be achieved by reducing computational resources. An increase in the variability of the conductivity distribution requires
more extraction wells. Information about potential extraction wells can be used to prevent failure of the remediation task. 相似文献
4.
Multivariate grid-free geostatistical simulation with point or block scale secondary data 总被引：1，自引：1，他引：0
Yevgeniy Zagayevskiy Clayton V. Deutsch 《Stochastic Environmental Research and Risk Assessment (SERRA)》2016,30(6):1613-1633
A novel grid-free geostatistical simulation method (GFS) allows representing coregionalized variables as an analytical function of the coordinates of the simulation locations. Simulation on unstructured grids, regridding and refinement of available realizations of natural phenomena including, but not limited to, environmental systems are possible with GFS in a consistent manner. The unconditional realizations are generated by utilizing the linear model of coregionalization and Fourier series-based decomposition of the covariance function. The conditioning to data is performed by kriging. The data can be measured at scattered point-scale locations or sampled at a block scale. Secondary data are usually used in conjunction with primary data for the improved modeling. Satellite imaging is an example of exhaustively sampled secondary data. Improvements and recommendations are made to the implementation of GFS to properly assimilate secondary exhaustive data sets in a grid-free manner. Intrinsic cokriging (ICK) is utilized to reduce computational time and preserve the overall quality of the simulation. To further reduce the computational cost of ICK, a block matrix inversion is implemented in the calculation of the kriging weights. A projection approach to ICK is proposed to avoid artifacts in the realizations around the edges of the exhaustive data region when the data do not cover the entire modeling domain. The point-scale block value representation of the block-scale data is developed as an alternative to block cokriging to integrate block-scale data into realizations within the GFS framework. Several case studies support the proposed enhancements. 相似文献
5.
This work evaluated the spatial variability and distribution of heterogeneous hydraulic conductivity (K) in the Choushui River alluvial fan in Taiwan, using ordinary kriging (OK) and mean and individual sequential Gaussian simulations (SGS). A baseline flow model constructed by upscaling parameters was inversely calibrated to determine the pumping and recharge rates. Simulated heads using different K realizations were then compared with historically measured heads. A global/local simulated error between simulated and measured heads was analysed to assess the different spatial variabilities of various estimated K distributions. The results of a MODFLOW simulation indicate that the OK realization had the smallest sum of absolute mean simulation errors (SAMSE) and the SGS realizations preserved the spatial variability of the measured K fields. Moreover, the SAMSE increases as the spatial variability of the K field increases. The OK realization yields small local simulation errors in the measured K field of moderate magnitude, whereas the SGS realizations have small local simulation errors in the measured K fields, with high and low values. The OK realization of K can be applied to perform a deterministic inverse calibration. The mean SGS method is suggested for constructing a K field when the application focuses on extreme values of estimated parameters and small calibration errors, such as in a simulation of contaminant transport in heterogeneous aquifers. The individual SGS realization is useful in stochastically assessing the spatial uncertainty of highly heterogeneous aquifers. Copyright © 2004 John Wiley & Sons, Ltd. 相似文献
6.
Stochastic weather generators are widely used in hydrological, environmental, and agricultural applications to simulate weather time series. However, such stochastic models produce random outputs hence the question on how representative the generated data are if obtained from only one simulation run (realization) as is common practice. In this study, the impact of different numbers of realizations (1, 25, 50, and 100) on the suitability of generated weather data was investigated. Specifically, 50 years of daily precipitation, and maximum and minimum temperatures were generated for three weather stations in the Western Lake Erie Basin (WLEB), using three widely used weather generators, CLIGEN, LARSWG and WeaGETS. Generated results were compared with 50 years of observed data. For all three generators, the analyses showed that one realization of data for 50 years of daily precipitation, and maximum and minimum temperatures may not be representative enough to capture essential statistical characteristics of the climate. Results from the three generators captured the essential statistical characteristics of the climate when the number of realizations was increased from 1 to 25, 50 or 100. Performance did not improve substantially when realizations were increased above 25. Results suggest the need for more than a single realization when generating weather data and subsequently utilizing in other models, to obtain suitable representations of climate. 相似文献
7.
Impact of the simulation algorithm, magnitude of ergodic fluctuations and number of realizations on the spaces of uncertainty of flow properties 总被引：1，自引：1，他引：0
P. Goovaerts 《Stochastic Environmental Research and Risk Assessment (SERRA)》1999,13(3):161-182
Geostatistical simulation algorithms are routinely used to generate conditional realizations of the spatial distribution
of petrophysical properties, which are then fed into complex transfer functions, e.g. a flow simulator, to yield a distribution
of responses, such as the time to recover a given proportion of the oil. This latter distribution, often referred to as the
space of uncertainty, cannot be defined analytically because of the complexity (non-linearity) of transfer functions, but
it can be characterized algorithmically through the generation of many realizations. This paper compares the space of uncertainty
generated by four of the most commonly used algorithms: sequential Gaussian simulation, sequential indicator simulation, p-field simulation and simulated annealing. Conditional to 80 sample permeability values randomly drawn from an exhaustive
40×40 image, 100 realizations of the spatial distribution of permeability values are generated using each algorithm and fed
into a pressure solver and a flow simulator. Principal component analysis is used to display the sets of realizations into
the joint space of uncertainty of the response variables (effective permeability, times to reach 5% and 95% water cuts and
to recover 10% and 50% of the oil). The attenuation of ergodic fluctuations through a rank-preserving transform of permeability
values reduces substantially the extent of the space of uncertainty for sequential indicator simulation and p-field simulation, while improving the prediction of the response variable by the mean of the output distribution. Differences
between simulation algorithms are the most pronounced for long-term responses (95% water cut and 50% oil recovery), with sequential
Gaussian simulation yielding the most accurate prediction. In this example, utilizing more than 20 realizations generally
increases only slightly the size of the space of uncertainty. 相似文献
8.
Alyssa M. Dausman John Doherty Christian D. Langevin Michael C. Sukop 《Ground water》2010,48(5):729-740
The present study demonstrates a methodology for optimization of environmental data acquisition. Based on the premise that the worth of data increases in proportion to its ability to reduce the uncertainty of key model predictions, the methodology can be used to compare the worth of different data types, gathered at different locations within study areas of arbitrary complexity. The method is applied to a hypothetical nonlinear, variable density numerical model of salt and heat transport. The relative utilities of temperature and concentration measurements at different locations within the model domain are assessed in terms of their ability to reduce the uncertainty associated with predictions of movement of the salt water interface in response to a decrease in fresh water recharge. In order to test the sensitivity of the method to nonlinear model behavior, analyses were repeated for multiple realizations of system properties. Rankings of observation worth were similar for all realizations, indicating robust performance of the methodology when employed in conjunction with a highly nonlinear model. The analysis showed that while concentration and temperature measurements can both aid in the prediction of interface movement, concentration measurements, especially when taken in proximity to the interface at locations where the interface is expected to move, are of greater worth than temperature measurements. Nevertheless, it was also demonstrated that pairs of temperature measurements, taken in strategic locations with respect to the interface, can also lead to more precise predictions of interface movement. 相似文献
9.
Associations between outdoor air quality and birth weight: a geostatistical sequential simulation approach in Coastal Alentejo,Portugal 总被引：3，自引：2，他引：1
Manuel Castro Ribeiro Pedro Pinho Esteve Llop Cristina Branquinho Amílcar Soares Maria João Pereira 《Stochastic Environmental Research and Risk Assessment (SERRA)》2014,28(3):527-540
Several environmental health studies suggest birth weight is associated with outdoor air pollution during gestation. In these studies, exposure assignments are usually based on measurements collected at air quality monitoring stations that do not coincide with health data locations. So, estimated exposures can be misleading if they do not take into account the uncertainty of exposure estimates. In this article we conducted a semi-ecological study to analyze associations between air quality during gestation and birth weight. Air quality during gestation was measured using a biomonitor, as an alternative to traditional air quality monitoring stations data, in order to increase spatial resolution of exposure measurements. To our knowledge this is the first time that the association between air quality and birth weight is studied using biomonitors. To address exposure uncertainty at health locations, we applied geostatistical simulation on biomonitoring data that provided multiple equally probable realizations of biomonitoring data, with reproduction of observed histogram and spatial covariance while matching for conditioning data. Each simulation represented a measure of exposure at each location. The set of simulations provided a measure of exposure uncertainty at each location. To incorporate uncertainty in our analysis we used generalized linear models, fitted simulation outputs and health data on birth weights and assessed statistical significance of exposure parameter using non-parametric bootstrap techniques. We found a positive association between air quality and birth weight. However, this association was not statistically significant. We also found a modest but significant association between air quality and birth weight among babies exposed to gestational tobacco smoke. 相似文献
10.
A statistical framework of data fusion for spatial prediction of categorical variables 总被引：1，自引：0，他引：1
Guofeng Cao Eun-hye Yoo Shaowen Wang 《Stochastic Environmental Research and Risk Assessment (SERRA)》2014,28(7):1785-1799
With rapid advances of geospatial technologies, the amount of spatial data has been increasing exponentially over the past few decades. Usually collected by diverse source providers, the available spatial data tend to be fragmented by a large variety of data heterogeneities, which highlights the need of sound methods capable of efficiently fusing the diverse and incompatible spatial information. Within the context of spatial prediction of categorical variables, this paper describes a statistical framework for integrating and drawing inferences from a collection of spatially correlated variables while accounting for data heterogeneities and complex spatial dependencies. In this framework, we discuss the spatial prediction of categorical variables in the paradigm of latent random fields, and represent each spatial variable via spatial covariance functions, which define two-point similarities or dependencies of spatially correlated variables. The representation of spatial covariance functions derived from different spatial variables is independent of heterogeneous characteristics and can be combined in a straightforward fashion. Therefore it provides a unified and flexible representation of heterogeneous spatial variables in spatial analysis while accounting for complex spatial dependencies. We show that in the spatial prediction of categorical variables, the sought-after class occurrence probability at a target location can be formulated as a multinomial logistic function of spatial covariances of spatial variables between the target and sampled locations. Group least absolute shrinkage and selection operator is adopted for parameter estimation, which prevents the model from over-fitting, and simultaneously selects an optimal subset of important information (variables). Synthetic and real case studies are provided to illustrate the introduced concepts, and showcase the advantages of the proposed statistical framework. 相似文献
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12.
Estimating threshold-exceeding probability maps of environmental variables with Markov chain random fields 总被引：2，自引：2，他引：0
Weidong Li Chuanrong Zhang Dipak K. Dey Shanqin Wang 《Stochastic Environmental Research and Risk Assessment (SERRA)》2010,24(8):1113-1126
Estimating and mapping spatial uncertainty of environmental variables is crucial for environmental evaluation and decision
making. For a continuous spatial variable, estimation of spatial uncertainty may be conducted in the form of estimating the
probability of (not) exceeding a threshold value. In this paper, we introduced a Markov chain geostatistical approach for
estimating threshold-exceeding probabilities. The differences of this approach compared to the conventional indicator approach
lie with its nonlinear estimators—Markov chain random field models and its incorporation of interclass dependencies through
transiograms. We estimated threshold-exceeding probability maps of clay layer thickness through simulation (i.e., using a
number of realizations simulated by Markov chain sequential simulation) and interpolation (i.e., direct conditional probability
estimation using only the indicator values of sample data), respectively. To evaluate the approach, we also estimated those
probability maps using sequential indicator simulation and indicator kriging interpolation. Our results show that (i) the
Markov chain approach provides an effective alternative for spatial uncertainty assessment of environmental spatial variables
and the probability maps from this approach are more reasonable than those from conventional indicator geostatistics, and
(ii) the probability maps estimated through sequential simulation are more realistic than those through interpolation because
the latter display some uneven transitions caused by spatial structures of the sample data. 相似文献
13.
Amir H. Hosseini Clayton V. Deutsch Kevin W. Biggar Carl A. Mendoza 《Stochastic Environmental Research and Risk Assessment (SERRA)》2010,24(5):735-749
The spatial distribution of residual light non-aqueous phase liquid (LNAPL) is an important factor in reactive solute transport
modeling studies. There is great uncertainty associated with both the areal limits of LNAPL source zones and smaller scale
variability within the areal limits. A statistical approach is proposed to construct a probabilistic model for the spatial
distribution of residual NAPL and it is applied to a site characterized by ultra-violet-induced-cone-penetration testing (CPT–UVIF).
The uncertainty in areal limits is explicitly addressed by a novel distance function (DF) approach. In modeling the small-scale
variability within the areal limits, the CPT–UVIF data are used as primary source of information, while soil texture and distance
to water table are treated as secondary data. Two widely used geostatistical techniques are applied for the data integration,
namely sequential indicator simulation with locally varying means (SIS–LVM) and Bayesian updating (BU). A close match between
the calibrated uncertainty band (UB) and the target probabilities shows the performance of the proposed DF technique in characterization
of uncertainty in the areal limits. A cross-validation study also shows that the integration of the secondary data sources
substantially improves the prediction of contaminated and uncontaminated locations and that the SIS–LVM algorithm gives a
more accurate prediction of residual NAPL contamination. The proposed DF approach is useful in modeling the areal limits of
the non-stationary continuous or categorical random variables, and in providing a prior probability map for source zone sizes
to be used in Monte Carlo simulations of contaminant transport or Monte Carlo type inverse modeling studies. 相似文献
14.
湖泊模型为数值天气预报模型提供热量通量、水汽通量和动量通量等下边界条件,但是不同时间尺度上湖泊水热通量变化的控制因子不同,因此有必要对湖泊模型进行多时间尺度上的离线评估.本文利用2012-2016年太湖中尺度通量网避风港站的气象资料和辐射数据驱动CLM4-LISSS模型(Community Land Model version 4-Lake,Ice,Snow and Sediment Simulator),并与涡度相关观测(Eddy Covariance,EC)结果进行对比,以年平均潜热通量模拟结果最佳为目标调整了模式中的消光系数、粗糙度长度方案,研究了该模型从半小时到年尺度上对湖表温度和水热通量的模拟性能.结果表明:模型对湖表温度的模拟在各时间尺度上均比较理想,但是模拟的日较差较小;从半小时到年尺度上潜热通量的变化趋势都能被很好地模拟出来,但在季节尺度上,潜热通量的模拟出现了秋冬季偏高、春夏季偏低的情况,季节变化模拟不准确.湖表温度和潜热通量模拟偏差的原因可能是消光系数的参数化方案.相比之下,感热通量尽管年际变化趋势的模拟值与观测值一致,但是从半小时到年尺度均被高估.特别地,冷锋过境期间,模型能较好地模拟出潜热通量和感热通量的变化趋势,但对于高风速条件下的感热通量模拟效果不佳.本文的研究结果能为湖泊模式的应用与发展提供有用信息. 相似文献
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A multivariate spatial sampling design that uses spatial vine copulas is presented that aims to simultaneously reduce the prediction uncertainty of multiple variables by selecting additional sampling locations based on the multivariate relationship between variables, the spatial configuration of existing locations and the values of the observations at those locations. Novel aspects of the methodology include the development of optimal designs that use spatial vine copulas to estimate prediction uncertainty and, additionally, use transformation methods for dimension reduction to model multivariate spatial dependence. Spatial vine copulas capture non-linear spatial dependence within variables, whilst a chained transformation that uses non-linear principal component analysis captures the non-linear multivariate dependence between variables. The proposed design methodology is applied to two environmental case studies. Performance of the proposed methodology is evaluated through partial redesigns of the original spatial designs. The first application is a soil contamination example that demonstrates the ability of the proposed methodology to address spatial non-linearity in the data. The second application is a forest biomass study that highlights the strength of the methodology in incorporating non-linear multivariate dependence into the design. 相似文献
17.
Raffaella De Matteis Annalisa Romeo Giuseppe Pasquale Giovanni Iannaccone Aldo Zollo 《Studia Geophysica et Geodaetica》2010,54(3):367-387
A new dataset of first P-wave arrival times is used to derive the 3D tomographic model of the Campania-Lucania region in the
southern Apennines (Italy). We address the issue related to the non-uniqueness of the tomographic inversion solution through
massive numerical experimentation based on the global exploration of the model parameter space starting from a large variety
of physically plausible initial models. The average of all the realizations is adopted as the best-fit solution and the uncertainty
of the model parameters is studied using a statistical approach based on a Monte Carlo-type analysis. How the uncertainty
in the initial model, earthquake locations, and data influences the inversion result is studied by considering separately
the individual effects. Checkerboard tests are performed to estimate the resolving power of the dataset. Re-located seismicity
in a reliable new 3D tomographic model allows us to correlate the earthquake distribution with the main seismogenic structures
present in the area. 相似文献
18.
ABSTRACTThis study investigates the impact of hydraulic conductivity uncertainty on the sustainable management of the aquifer of Lake Karla, Greece, using the stochastic optimization approach. The lack of surface water resources in combination with the sharp increase in irrigation needs in the basin over the last 30 years have led to an unprecedented degradation of the aquifer. In addition, the lack of data regarding hydraulic conductivity in a heterogeneous aquifer leads to hydrogeologic uncertainty. This uncertainty has to be taken into consideration when developing the optimization procedure in order to achieve the aquifer’s sustainable management. Multiple Monte Carlo realizations of this spatially-distributed parameter are generated and groundwater flow is simulated for each one of them. The main goal of the sustainable management of the ‘depleted’ aquifer of Lake Karla is two-fold: to determine the optimum volume of renewable groundwater that can be extracted, while, at the same time, restoring its water table to a historic high level. A stochastic optimization problem is therefore formulated, based on the application of the optimization method for each of the aquifer’s multiple stochastic realizations in a future period. In order to carry out this stochastic optimization procedure, a modelling system consisting of a series of interlinked models was developed. The results show that the proposed stochastic optimization framework can be a very useful tool for estimating the impact of hydraulic conductivity uncertainty on the management strategies of a depleted aquifer restoration. They also prove that the optimization process is affected more by hydraulic conductivity uncertainty than the simulation process.
Editor Z.W. Kundzewicz; Guest editor S. Weijs 相似文献
19.
ABSTRACT Groundwater-level time series often have a substantial number of missing values which should be taken into consideration before using them for further analysis, particularly for numerical groundwater flow modelling applications. This study aims to comprehensively compare two data-driven models, singular spectrum analysis (SSA) and multichannel spectrum analysis (MSSA), to reconstruct groundwater-level time series and impute the missing values for 25 piezometric stations in Ardabil Plain, northwest Iran. The reconstructed groundwater-level time series are assessed against the complete observed groundwater time series, while the imputed values are appraised against the artificially created gap values. The results show that both SSA and MSSA demonstrate a solid competency in imputation and reconstruction of groundwater-level data. However, depending on the spatial correlation between the piezometers, and the most suitable probability distribution function (pdf) fitted to the time series of each piezometer, the performance may vary from piezometer to piezometer. 相似文献
20.
Kyung-Ho Kim Kang-Kun Lee 《Stochastic Environmental Research and Risk Assessment (SERRA)》2007,21(6):785-794
A new methodology is proposed to optimize monitoring networks for identification of the extent of contaminant plumes. The
optimal locations for monitoring wells are determined as the points where maximal decreases are expected in the quantified
uncertainty about contaminant existence after well installation. In this study, hydraulic conductivity is considered to be
the factor that causes uncertainty. The successive random addition (SRA) method is used to generate random fields of hydraulic
conductivity. The expected value of information criterion for the existence of a contaminant plume is evaluated based on how
much the uncertainty of plume distribution reduces with increases in the size of the monitoring network. The minimum array
of monitoring wells that yields the maximum information is selected as the optimal monitoring network. In order to quantify
the uncertainty of the plume distribution, the probability map of contaminant existence is made for all generated contaminant
plume realizations on the domain field. The uncertainty is defined as the sum of the areas where the probability of contaminant
existence or nonexistence is uncertain. Results of numerical experiments for determination of optimal monitoring networks
in heterogeneous conductivity fields are presented. 相似文献