共查询到20条相似文献，搜索用时 31 毫秒
1.
Gómez-Hernández J. J. Wen X. -H. 《Stochastic Environmental Research and Risk Assessment (SERRA)》1994,8(1):19-55
A Monte Carlo approach is described for the quantification of uncertainty on travel time estimates. A real (non synthetic) and exhaustive data set of natural genesis is used for reference. Using an approach based on binary indicators, constraint interval data are easily accommodated in the modeling process. It is shown how the incorporation of imprecise data can reduce drastically the uncertainty in the estimates. It is also shown that unrealistic results are obtained when a deterministic modeling is carried out using a kriging estimate of the transmissivity field. Problems related with using sequential indicator simulation for the generation of fields incorporating constraint interval data are discussed. The final results consists of 95% probability intervals of arrival times at selected control planes reflecting the original uncertainty on the transmissivity maps. 相似文献
2.
Abhishek Singh Douglas D. Walker Barbara S. Minsker Albert J. Valocchi 《Stochastic Environmental Research and Risk Assessment (SERRA)》2010,24(6):881-898
The interactive multi-objective genetic algorithm (IMOGA) combines traditional optimization with an interactive framework that considers the subjective knowledge of hydro-geological experts in addition to quantitative calibration measures such as calibration errors and regularization to solve the groundwater inverse problem. The IMOGA is inherently a deterministic framework and identifies multiple large-scale parameter fields (typically head and transmissivity data are used to identify transmissivity fields). These large-scale parameter fields represent the optimal trade-offs between the different criteria (quantitative and qualitative) used in the IMOGA. This paper further extends the IMOGA to incorporate uncertainty both in the large-scale trends as well as the small-scale variability (which can not be resolved using the field data) in the parameter fields. The different parameter fields identified by the IMOGA represent the uncertainty in large-scale trends, and this uncertainty is modeled using a Bayesian approach where calibration error, regularization, and the expert’s subjective preference are combined to compute a likelihood metric for each parameter field. Small-scale (stochastic) variability is modeled using a geostatistical approach and added onto the large-scale trends identified by the IMOGA. This approach is applied to the Waste Isolation Pilot Plant (WIPP) case-study. Results, with and without expert interaction, are analyzed and the impact that expert judgment has on predictive uncertainty at the WIPP site is discussed. It is shown that for this case, expert interaction leads to more conservative solutions as the expert compensates for some of the lack of data and modeling approximations introduced in the formulation of the problem. 相似文献
3.
In this study, we examine the effects of conditioning spatially variable transmissivity fields using head and/or transmissivity measurements on well-capture zones. In order to address the challenge posed by conditioning a flow model with spatially varying parameters, an innovative inverse algorithm, the Representers method, is employed. The method explicitly considers this spatial variability.
A number of uniform measurement grids with different densities are used to condition transmissivity fields using the Representers method. Deterministic and stochastic analysis of well-capture zones are then examined. The deterministic study focuses on comparison between reference well-capture zones and their estimated mean conditioned on head data. It shows that model performance due to head conditioning on well-capture zone estimation is related to pumping rate. At moderate pumping rates transmissivity observations are more crucial to identify effects arising from small-scale variations in pore water velocity. However, with more aggressive pumping these effects are reduced, consequently model performance, through incorporating head observations, markedly improves. In the stochastic study, the effect of conditioning using head and/or transmissivity data on well-capture zone uncertainty is examined. The Representers method is coupled with the Monte Carlo method to propagate uncertainty in transmissivity fields to well-capture zones. For the scenario studied, the results showed that a combination of 48 head and transmissivity data could reduce the area of uncertainty (95% confidence interval) in well-capture zone location by over 50%, compared to a 40% reduction using either head or transmissivity data. This performance was comparable to that obtained through calibrating on three and a half times the number of head observations alone. 相似文献
4.
Inverse modeling is a useful tool in ground water flow modeling studies. The most frequent difficulties encountered when using this technique are the lack of conditioning information (e.g., heads and transmissivities), the uncertainty in available data, and the nonuniqueness of the solution. These problems can be addressed and quantified through a stochastic Monte Carlo approach. The aim of this work was to compare the applicability of two stochastic inverse modeling approaches in a field-scale application. The multi-scaling (MS) approach uses a downscaling parameterization procedure that is not based on geostatistics. The pilot point (PP) approach uses geostatistical random fields as initial transmissivity values and an experimental variogram to condition the calibration. The studied area (375 km2) is part of a regional aquifer, northwest of Montreal in the St. Lawrence lowlands (southern Québec). It is located in limestone, dolomite, and sandstone formations, and is mostly a fractured porous medium. The MS approach generated small errors on heads, but the calibrated transmissivity fields did not reproduce the variogram of observed transmissivities. The PP approach generated larger errors on heads but better reproduced the spatial structure of observed transmissivities. The PP approach was also less sensitive to uncertainty in head measurements. If reliable heads are available but no transmissivities are measured, the MS approach provides useful results. If reliable transmissivities with a well inferred spatial structure are available, then the PP approach is a better alternative. This approach however must be used with caution if measured transmissivities are not reliable. 相似文献
5.
A method is presented for quantifying the uncertainty of the semivariogram of transmissivity and determining the required number of measurements. In this method, the estimated semivariogram and its 95% confidence limits are first determined from a finite number of measurements. The uncertainty of the estimated semivariogram is then quantified using the random field simulation technique. For a given value of the quantitative index of uncertainty, the required number of measured data can finally be obtained. Actual transmissivity data of an existing groundwater monitoring network are used in the application of the proposed method. The required numbers of measurements of transmissivity for four different values of the quantitative index of uncertainty are provided, from which reliable semivariograms of the transmissivity can be obtained. Copyright © 2004 John Wiley & Sons, Ltd. 相似文献
6.
It is well known that there is a degree of fuzzy uncertainty in land cover classification using remote sensing (RS) images. In this article, we propose a novel fuzzy uncertainty modeling algorithm for representing the features of land cover patterns, and present an adaptive interval type-2 fuzzy clustering method. The proposed fuzzy uncertainty modeling method is performed in two main phases. First, the segmentation units of the input multi-spectral RS image data are subjected to objectbased interval-valued symbolic modeling. As a result, features for each land cover type are represented in the form of an intervalvalued symbolic vector, which describes the intra-class uncertainty better than the source data and improves the separability between different classes. Second, interval type-2 fuzzy sets are generated for each cluster based on the distance metric of the interval-valued vectors. This step characterizes the inter-class high-order fuzzy uncertainty and improves the classification accuracy. To demonstrate the advantages and effectiveness of the proposed approach, extensive experiments are conducted on two multispectral RS image datasets from regions with complex land cover characteristics, and the results are compared with those given by well-known fuzzy and conventional clustering algorithms. 相似文献
7.
Se-Yeong Hamm Jae-Yeol Cheong Seong Jang Cha-Yeon Jung Bong-Sang Kim 《Journal of Hydrology》2005,310(1-4):111-121
Transmissivity is often estimated using specific capacity data when standard pumping test data are not available or the drawdown is stabilized early, as in this study. Previous researchers studied the relationship between transmissivity and specific capacity in the leaky aquifer system of volcanic rocks on Jeju Island, Korea, using the Cooper–Jacob equation. The current study utilizes the Moench leaky aquifer model. The linear relationship between transmissivity and specific capacity on a log–log scale for volcanic aquifers on Jeju Island is remarkably strong, with a correlation coefficient of 0.94. The width of the 90% prediction interval is about 0.89 log cycles, indicating a ±0.44 order of magnitude uncertainty when transmissivity is estimated using specific capacity. 相似文献
8.
MODFLOW 2000 head uncertainty,a first-order second moment method 总被引：1，自引：0，他引：1
A computationally efficient method to estimate the variance and covariance in piezometric head results computed through MODFLOW 2000 using a first-order second moment (FOSM) approach is presented. This methodology employs a first-order Taylor series expansion to combine model sensitivity with uncertainty in geologic data. MODFLOW 2000 is used to calculate both the ground water head and the sensitivity of head to changes in input data. From a limited number of samples, geologic data are extrapolated and their associated uncertainties are computed through a conditional probability calculation. Combining the spatially related sensitivity and input uncertainty produces the variance-covariance matrix, the diagonal of which is used to yield the standard deviation in MODFLOW 2000 head. The variance in piezometric head can be used for calibrating the model, estimating confidence intervals, directing exploration, and evaluating the reliability of a design. A case study illustrates the approach, where aquifer transmissivity is the spatially related uncertain geologic input data. The FOSM methodology is shown to be applicable for calculating output uncertainty for (1) spatially related input and output data, and (2) multiple input parameters (transmissivity and recharge). 相似文献
9.
José E. Capilla J. Rodrigo J. J. Gómez-Hernández 《Stochastic Environmental Research and Risk Assessment (SERRA)》1998,12(3):171-190
A common approach for the performance assessment of radionuclide migration from a nuclear waste repository is by means of
Monte-Carlo techniques. Multiple realizations of the parameters controlling radionuclide transport are generated and each
one of these realizations is used in a numerical model to provide a transport prediction. The statistical analysis of all
transport predictions is then used in performance assessment. In order to reduce the uncertainty on the predictions is necessary
to incorporate as much information as possible in the generation of the parameter fields. In this regard, this paper focuses
in the impact that conditioning the transmissivity fields to geophysical data and/or piezometric head data has on convective
transport predictions in a two-dimensional heterogeneous formation. The Walker Lake data based is used to produce a heterogeneous
log-transmissivity field with distinct non-Gaussian characteristics and a secondary variable that represents some geophysical
attribute. In addition, the piezometric head field resulting from the steady-state solution of the groundwater flow equation
is computed. These three reference fields are sampled to mimic a sampling campaign. Then, a series of Monte-Carlo exercises
using different combinations of sampled data shows the relative worth of secondary data with respect to piezometric head data
for transport predictions. The analysis shows that secondary data allows to reproduce the main spatial patterns of the reference
transmissivity field and improves the mass transport predictions with respect to the case in which only transmissivity data
is used. However, a few piezometric head measurements could be equally effective for the characterization of transport predictions. 相似文献
10.
José E. Capilla J. Rodrigo J. J. Gómez-Hernández 《Stochastic Hydrology and Hydraulics》1998,12(3):171-190
A common approach for the performance assessment of radionuclide migration from a nuclear waste repository is by means of
Monte-Carlo techniques. Multiple realizations of the parameters controlling radionuclide transport are generated and each
one of these realizations is used in a numerical model to provide a transport prediction. The statistical analysis of all
transport predictions is then used in performance assessment. In order to reduce the uncertainty on the predictions is necessary
to incorporate as much information as possible in the generation of the parameter fields. In this regard, this paper focuses
in the impact that conditioning the transmissivity fields to geophysical data and/or piezometric head data has on convective
transport predictions in a two-dimensional heterogeneous formation. The Walker Lake data based is used to produce a heterogeneous
log-transmissivity field with distinct non-Gaussian characteristics and a secondary variable that represents some geophysical
attribute. In addition, the piezometric head field resulting from the steady-state solution of the groundwater flow equation
is computed. These three reference fields are sampled to mimic a sampling campaign. Then, a series of Monte-Carlo exercises
using different combinations of sampled data shows the relative worth of secondary data with respect to piezometric head data
for transport predictions. The analysis shows that secondary data allows to reproduce the main spatial patterns of the reference
transmissivity field and improves the mass transport predictions with respect to the case in which only transmissivity data
is used. However, a few piezometric head measurements could be equally effective for the characterization of transport predictions. 相似文献
11.
A Bayesian approach to characterize the predictive uncertainty in the delineation of time-related well capture zones in heterogeneous formations is presented and compared with the classical or non-Bayesian approach. The transmissivity field is modelled as a random space function and conditioned on distributed measurements of the transmissivity. In conventional geostatistical methods the mean value of the log transmissivity and the functional form of the covariance and its parameters are estimated from the available measurements, and then entered into the prediction equations as if they are the true values. However, this classical approach accounts only for the uncertainty that stems from the lack of ability to exactly predict the transmissivity at unmeasured locations. In reality, the number of measurements used to infer the statistical properties of the transmissvity field is often limited, which introduces error in the estimation of the structural parameters. The method presented accounts for the uncertainty that originates from the imperfect knowledge of the parameters by treating them as random variables. In particular, we use Bayesian methods of inference so as to make proper allowance for the uncertainty associated with estimating the unknown values of the parameters. The classical and Bayesian approach to stochastic capture zone delineation are detailed and applied to a hypothetical flow field. Two different sampling densities on a regular grid are considered to evaluate the effect of data density in both methods. Results indicate that the predictions of the Bayesian approach are more conservative. 相似文献
12.
The value of subsidence data in ground water model calibration 总被引：2，自引：0，他引：2
The accurate estimation of aquifer parameters such as transmissivity and specific storage is often an important objective during a ground water modeling investigation or aquifer resource evaluation. Parameter estimation is often accomplished with changes in hydraulic head data as the key and most abundant type of observation. The availability and accessibility of global positioning system and interferometric synthetic aperture radar data in heavily pumped alluvial basins can provide important subsidence observations that can greatly aid parameter estimation. The aim of this investigation is to evaluate the value of spatial and temporal subsidence data for automatically estimating parameters with and without observation error using UCODE-2005 and MODFLOW-2000. A synthetic conceptual model (24 separate cases) containing seven transmissivity zones and three zones each for elastic and inelastic skeletal specific storage was used to simulate subsidence and drawdown in an aquifer with variably thick interbeds with delayed drainage. Five pumping wells of variable rates were used to stress the system for up to 15 years. Calibration results indicate that (1) the inverse of the square of the observation values is a reasonable way to weight the observations, (2) spatially abundant subsidence data typically produce superior parameter estimates under constant pumping even with observation error, (3) only a small number of subsidence observations are required to achieve accurate parameter estimates, and (4) for seasonal pumping, accurate parameter estimates for elastic skeletal specific storage values are largely dependent on the quantity of temporal observational data and less on the quantity of available spatial data. 相似文献
13.
byK. L. Ricciardi 《Ground water》2009,47(5):675-685
Optimal cost pump-and-treat ground water remediation designs for containment of a contaminated aquifer are often developed using deterministic ground water models to predict ground water flow. Uncertainty in hydraulic conductivity fields used in these models results in remediation designs that are unreliable. The degree to which uncertainty contributes to the reliability of remediation designs as measured by the characterization of the uncertainty is shown to differ depending upon the geologic environments of the models. This conclusion is drawn from the optimal design costs for multiple deterministic models generated to represent the uncertainty of four distinct models with different geologic environments. A multi scenario approach that includes uncertainty into the remediation design called the deterministic method for optimization subject to uncertainty (DMOU) is applied to these distinct models. It is found that the DMOU is a method for determining a remediation design subject to uncertainty that requires minimal postprocessing efforts. Preprocessing, however, is required for the application of the DMOU to unique problems. In the ground water remediation design problems, the orientation of geologic facies with respect to the orientation of flow patterns, pumping well locations, and constraint locations are shown to affect the preprocessing, the solutions to the DMOU problems, and the computational efficiency of the DMOU approach. The results of the DMOU are compared to the results of a statistical analysis of the effects of the uncertainty on remediation designs. This comparison validates the efficacy of the DMOU and illustrates the computational advantages to using the DMOU over statistical measures. 相似文献
14.
Accurate modeling of hydraulic properties such as transmissivity and interbed specific storages is significant for reliable predictions of land subsidence modeling. Calibration of land subsidence model is a challenge because of the strong non-linearity of groundwater flow equation especially when it accounting for the interbed drainage process. Pumping well drawdown and land subsidence data are very important signals for identification of aquifer hydraulic properties. In this work, it is proposed that the ensemble Kalman filter is used to calibrate the transmissivity and interbed elastic and inelastic specific storages using both drawdown and subsidence data for the first time. A synthetic example demonstrated that the characterization of transmissivity and specific storages is improved, and the uncertainties of predictions of both drawdown and subsidence are reduced, when additional dynamic observation data are used for inverse modeling. Issues such as how to account for interferometric synthetic aperture radar data, which may be encountered using the EnKF for real case studies, are discussed. 相似文献
15.
by M. Cardiff W. Barrash P.K. Kitanidis B. Malama A. Revil S. Straface and E. Rizzo 《Ground water》2009,47(2):259-270
The importance of estimating spatially variable aquifer parameters such as transmissivity is widely recognized for studies in resource evaluation and contaminant transport. A useful approach for mapping such parameters is inverse modeling of data from series of pumping tests, that is, via hydraulic tomography. This inversion of field hydraulic tomographic data requires development of numerical forward models that can accurately represent test conditions while maintaining computational efficiency. One issue this presents is specification of boundary and initial conditions, whose location, type, and value may be poorly constrained. To circumvent this issue when modeling unconfined steady-state pumping tests, we present a strategy that analyzes field data using a potential difference method and that uses dipole pumping tests as the aquifer stimulation. By using our potential difference approach, which is similar to modeling drawdown in confined settings, we remove the need for specifying poorly known boundary condition values and natural source/sink terms within the problem domain. Dipole pumping tests are complementary to this strategy in that they can be more realistically modeled than single-well tests due to their conservative nature, quick achievement of steady state, and the insensitivity of near-field response to far-field boundary conditions. After developing the mathematical theory, our approach is first validated through a synthetic example. We then apply our method to the inversion of data from a field campaign at the Boise Hydrogeophysical Research Site. Results from inversion of nine pumping tests show expected geologic features, and uncertainty bounds indicate that hydraulic conductivity is well constrained within the central site area. 相似文献
16.
Ioannis Nalbantis Ino Papageorgaki Panayiotis Sioras Charalambos Ioannidis 《水文研究》2017,31(9):1760-1775
The impact of uncertainty in ground elevation on the extent of areas that are inundated due to flooding is investigated. Land surface is represented through a Digital Surface Model (DSM). The effect of uncertainty in DSM is compared to that of the uncertainty due to rainfall. The Monte Carlo method is used to quantify the uncertainty. A typical photogrammetric procedure and conventional maps are used to obtain a reference DSM, later altered to provide DSMs of lower accuracy. Also, data from the Shuttle Radar Topography Mission are used. Floods are simulated in two stages. In the first stage, flood hydrographs for typical return periods are synthesized using generated storm hyetographs, the Soil Conservation Service–Curve Number method for effective rainfall, and the Soil Conservation Service synthetic unit hydrograph. In the second stage, hydrographs are routed via a one‐dimensional hydraulic model. Uncertainty in DSM is considered only in the second stage. Data from two real‐world basins in Greece are used. To characterize the inundated area, we employ the 90% quantile of the inundation extent and inundation topwidth for peak water level at specific river cross‐sections. For topwidths, apart from point estimates, also interval estimates are acquired using the bootstrap method. The effect of DSM uncertainty is compared to that for rainfall. Low uncertainty in DSM is found to widen the inundated area; whereas, the opposite occurred with high uncertainty. SRTM data proved unsuitable for our test basins and modelling context. 相似文献
17.
Watershed water quality models are increasingly used in management. However, simulations by such complex models often involve significant uncertainty, especially those for non-conventional pollutants which are often poorly monitored. This study first proposed an integrated framework for watershed water quality modeling. Within this framework, Probabilistic Collocation Method (PCM) was then applied to a WARMF model of diazinon pollution to assess the modeling uncertainty. Based on PCM, a global sensitivity analysis method named PCM-VD (VD stands for variance decomposition) was also developed, which quantifies variance contribution of all uncertain parameters. The study results validated the applicability of PCM and PCM-VD to the WARMF model. The PCM-based approach is much more efficient, regarding computational time, than conventional Monte Carlo methods. It has also been demonstrated that analysis using the PCM-based approach could provide insights into data collection, model structure improvement and management practices. It was concluded that the PCM-based approach could play an important role in watershed water quality modeling, as an alternative to conventional Monte Carlo methods to account for parametric uncertainty and uncertainty propagation. 相似文献
18.
A parameter-estimation technique based on existing hydrological, geophysical, and geological data was developed to approximate transmissivity values for use in a ground-water flow model of the Animas Valley, southwest New Mexico. Complete Bouguer gravity anomaly maps together with seismic-refraction profiles, geologic maps, geologic, geophysical, and drillers' logs, water levels, and pumping-test data provide insight into the transmissivity of bolson deposits throughout the basin. The transmissivity distribution was primarily based on reported pumping and specific-capacity tests in conjunction with complete Bouguer gravity anomaly maps and well log data. Reported transmissivity values were characterized by gravity values and well log data. In grid blocks lacking pumping and specific-capacity tests, transmissivity values were assigned based on the relationship of gravity values and well log data within the grid block to gravity values and well log data within other grid blocks for which transmissivity values are available. A two-dimensional, finite-difference, ground-water flow computer code was used to evaluate the effectiveness of the parameter-estimation technique. Although the trial-and-error method of calibration was employed, the actual computer time necessary for model calibration was minimal. The conceptually straightforward approach for parameter estimation utilizing existing hydrological, geophysical, and geological data provides realistic parameter estimates. 相似文献
19.
A combinatorial optimization scheme for parameter structure identification in ground water modeling 总被引：5，自引：0，他引：5
This research develops a methodology for parameter structure identification in ground water modeling. For a given set of observations, parameter structure identification seeks to identify the parameter dimension, its corresponding parameter pattern and values. Voronoi tessellation is used to parameterize the unknown distributed parameter into a number of zones. Accordingly, the parameter structure identification problem is equivalent to finding the number and locations as well as the values of the basis points associated with the Voronoi tessellation. A genetic algorithm (GA) is allied with a grid search method and a quasi-Newton algorithm to solve the inverse problem. GA is first used to search for the near-optimal parameter pattern and values. Next, a grid search method and a quasi-Newton algorithm iteratively improve the GA's estimates. Sensitivities of state variables to parameters are calculated by the sensitivity-equation method. MODFLOW and MT3DMS are employed to solve the coupled flow and transport model as well as the derived sensitivity equations. The optimal parameter dimension is determined using criteria based on parameter uncertainty and parameter structure discrimination. Numerical experiments are conducted to demonstrate the proposed methodology, in which the true transmissivity field is characterized by either a continuous distribution or a distribution that can be characterized by zones. We conclude that the optimized transmissivity zones capture the trend and distribution of the true transmissivity field. 相似文献
20.
Traditionally the Cooper–Jacob equation is used to determine the transmissivity and the storage coefficient for an aquifer using pump test results. This model, however, is a simplified version of the actual subsurface and does not allow for analysis of the uncertainty that comes from a lack of knowledge about the heterogeneity of the environment under investigation. In this paper, a modified fuzzy least-squares regression (MFLSR) method is developed that uses imprecise pump test data to obtain fuzzy intercept and slope values which are then used in the Cooper–Jacob method. Fuzzy membership functions for the transmissivity and the storage coefficient are then calculated using the extension principle. The supports of the fuzzy membership functions incorporate the transmissivity and storage coefficient values that would be obtained using ordinary least-squares regression and the Cooper–Jacob method. The MFLSR coupled with the Cooper–Jacob method allows the analyst to ascertain the uncertainty that is inherent in the estimated parameters obtained using the simplified Cooper–Jacob method and data that are uncertain due to lack of knowledge regarding the heterogeneity of the aquifer. 相似文献