共查询到20条相似文献，搜索用时 136 毫秒
1.
Estimation of Hydraulic Conductivity and Its Uncertainty from Grain-Size Data Using GLUE and Artificial Neural Networks 总被引：4，自引：0，他引：4
Bart Rogiers Dirk Mallants Okke Batelaan Matej Gedeon Marijke Huysmans Alain Dassargues 《Mathematical Geosciences》2012,44(6):739-763
Various approaches exist to relate saturated hydraulic conductivity (K s) to grain-size data. Most methods use a single grain-size parameter and hence omit the information encompassed by the entire grain-size distribution. This study compares two data-driven modelling methods??multiple linear regression and artificial neural networks??that use the entire grain-size distribution data as input for K s prediction. Besides the predictive capacity of the methods, the uncertainty associated with the model predictions is also evaluated, since such information is important for stochastic groundwater flow and contaminant transport modelling. Artificial neural networks (ANNs) are combined with a generalised likelihood uncertainty estimation (GLUE) approach to predict K s from grain-size data. The resulting GLUE-ANN hydraulic conductivity predictions and associated uncertainty estimates are compared with those obtained from the multiple linear regression models by a leave-one-out cross-validation. The GLUE-ANN ensemble prediction proved to be slightly better than multiple linear regression. The prediction uncertainty, however, was reduced by half an order of magnitude on average, and decreased at most by an order of magnitude. This demonstrates that the proposed method outperforms classical data-driven modelling techniques. Moreover, a comparison with methods from the literature demonstrates the importance of site-specific calibration. The data set used for this purpose originates mainly from unconsolidated sandy sediments of the Neogene aquifer, northern Belgium. The proposed predictive models are developed for 173 grain-size K s-pairs. Finally, an application with the optimised models is presented for a borehole lacking K s data. 相似文献
2.
A key issue in assessment of rainfall-induced slope failure is a reliable evaluation of pore water pressure distribution and its variations during rainstorm, which in turn requires accurate estimation of soil hydraulic parameters. In this study, the uncertainties of soil hydraulic parameters and their effects on slope stability prediction are evaluated, within the Bayesian framework, using the field measured temporal pore-water pressure data. The probabilistic back analysis and parameter uncertainty estimation is conducted using the Markov Chain Monte Carlo simulation. A case study of a natural terrain site is presented to illustrate the proposed method. The 95% total uncertainty bounds for the calibration period are relatively narrow, indicating an overall good performance of the infiltration model for the calibration period. The posterior uncertainty bounds of slope safety factors are much narrower than the prior ones, implying that the reduction of uncertainty in soil hydraulic parameters significantly reduces the uncertainty of slope stability. 相似文献
3.
传统方法在计算含水层渗透系数的过程中通常面临着不均匀系数C难以确定、对研究区水文地质条件较为依赖等困难。通过分析和概括内分泌系统独特的信息处理功能,构建了人工内分泌网络模型。在详细介绍模型算法和计算步骤的基础上,将其应用到华北平原滹沱河冲洪积扇前缘,根据孔隙度及粒度分布特征对含水层渗透系数进行预测,并与传统的经验公式Beyer法和Slichter法进行对比。计算结果表明：Beyer法的预测精度最低,其相对误差主要集中于0.078~1.342;Slichter法的预测精度有所提高,但对于渗透系数极端值的预测仍存在较大误差,相对误差主要集中于0.046~0.643;人工内分泌网络模型具有较高的预测精度,其相对误差主要集中于0.006~0.420。与传统经验公式相比,人工内分泌网络模型具有较高的计算精度和良好的通用性,且能够直接计算出含水层的垂向渗透系数,无需后期的数据验证。 相似文献
4.
Abderraouf El Ouazzani Taibi Amro Elfeki Guoping Zhang 《Arabian Journal of Geosciences》2011,4(5-6):1005-1018
The research presented in this paper focuses on the application of a newly developed physically based watershed modeling approach, which is called representative elementary watershed approach. The study stressed the effects of uncertainty of input parameters on the watershed responses (i.e., simulated discharges). The approach was applied to the Zwalm catchment, which is an agriculture-dominated watershed with a drainage area of 114 km2 located in East Flanders, Belgium. Uncertainty analysis of the model parameters is limited to the saturated hydraulic conductivity because of its high influence on the watershed hydrologic behavior and availability of the data. The assessment of output uncertainty is performed using the Monte Carlo method. The ensemble statistical watershed responses and their uncertainties are calculated and compared with measurements. The results show that the measured discharges fall within the 95% confidence interval of the modeled discharge. This provides the uncertainty bounds of the discharges that account for the uncertainty in saturated hydraulic conductivity. The methodology can be extended to address other uncertain parameters as far as the probability density function of the parameter is defined. 相似文献
5.
Integration of Tracer Test Data to Refine Geostatistical Hydraulic Conductivity Fields Using Sequential Self-Calibration Method 总被引：3，自引：0，他引：3
On the basis of local measurements of hydraulic conductivity,geostatistical methods have been found to be useful in heterogeneity characterization of a hydraulic conductivity field on a regional scale. However,the methods are not suited to directly integrate dynamic production data,such as,hydraulic head and solute concentration,into the study of conductivity distribution. These data,which record the flow and transport processes in the medium,are closely related to the spatial distribution of hydraulic conductivity. In this study,a three-dimensional gradient-based inverse method-the sequential self-calibration (SSC) method-is developed to calibrate a hydraulic conductivity field,initially generated by a geostatistical simulation method,conditioned on tracer test results. The SSC method can honor both local hydraulic conductivity measurements and tracer test data. The mismatch between the simulated hydraulic conductivity field and the reference true one,measured by its mean square error (MSE),is reduced through the SSC conditional study. In comparison with the unconditional results,the SSC conditional study creates the mean breakthrough curve much closer to the reference true curve,and significantly reduces the prediction uncertainty of the solute transport in the observed locations. Further,the reduction of uncertainty is spatially dependent,which indicates that good locations,geological structure,and boundary conditions will affect the efficiency of the SSC study results. 相似文献
6.
当土壤转换函数应用于土壤水力性质估计时,对于预测值的不确定性往往容易被忽视。为了有针对性地提出减少这种不确定性的方法和措施,提高土壤转换函数的实际应用能力,以两种现有的土壤转换函数(Vereecken和HYPRES模型)为例,将其应用于山东省平度市土壤饱和导水率的空间预测,并利用拉丁超立方抽样(LHS)方法对预测结果的不确定性进行了分析。结果表明,饱和导水率空间预测的不确定性主要来源于土壤基本性质的空间插值误差和土壤转换函数自身的预测误差。当Vereecken模型应用于饱和导水率空间预测时,预测结果的不确定性主要由土壤基本性质空间插值误差所决定,土壤转换函数预测误差的影响较小,而HYPRES模型则是受二者的双重影响。 相似文献
7.
裂隙介质渗透结构表现为高度的非均质性与各项异性。为了科学有效地预测某核工程场地裂隙地下水的流动规律,揭示裂隙岩体地下水的渗流特性,笔者等采用Pilot Point调参方法与null space Monte Carlo方法（NSMC）,开展了裂隙岩体渗透结构的不确定性分析研究,构建了符合实际水文地质条件的多个渗流数值模型集合。结果表明：该方法获得的各个实现地下水位模拟结果能够与实际观测数据较好吻合,可反映工程场地裂隙地下水动力特征与流动趋势;各个实现的参数化渗透结构在空间上存在一定的差异性,但整体变化趋势是保持一致的,渗透参数的不确定性表现为在实测数据分布区域相对较低,钻孔空白区域相对较高;该方法可以弥补单一、确定性模拟结果在表征裂隙介质渗透结构方面的局限性,有效地降低模型参数的不确定性与随机性。此方法对进一步提升裂隙岩体渗流模拟精度与预测能力,深化裂隙地下水迁移规律的认识具有重要的意义。 相似文献
8.
In earth and environmental sciences applications, uncertainty analysis regarding the outputs of models whose parameters are spatially varying (or spatially distributed) is often performed in a Monte Carlo framework. In this context, alternative realizations of the spatial distribution of model inputs, typically conditioned to reproduce attribute values at locations where measurements are obtained, are generated via geostatistical simulation using simple random (SR) sampling. The environmental model under consideration is then evaluated using each of these realizations as a plausible input, in order to construct a distribution of plausible model outputs for uncertainty analysis purposes. In hydrogeological investigations, for example, conditional simulations of saturated hydraulic conductivity are used as input to physically-based simulators of flow and transport to evaluate the associated uncertainty in the spatial distribution of solute concentration. Realistic uncertainty analysis via SR sampling, however, requires a large number of simulated attribute realizations for the model inputs in order to yield a representative distribution of model outputs; this often hinders the application of uncertainty analysis due to the computational expense of evaluating complex environmental models. Stratified sampling methods, including variants of Latin hypercube sampling, constitute more efficient sampling aternatives, often resulting in a more representative distribution of model outputs (e.g., solute concentration) with fewer model input realizations (e.g., hydraulic conductivity), thus reducing the computational cost of uncertainty analysis. The application of stratified and Latin hypercube sampling in a geostatistical simulation context, however, is not widespread, and, apart from a few exceptions, has been limited to the unconditional simulation case. This paper proposes methodological modifications for adopting existing methods for stratified sampling (including Latin hypercube sampling), employed to date in an unconditional geostatistical simulation context, for the purpose of efficient conditional simulation of Gaussian random fields. The proposed conditional simulation methods are compared to traditional geostatistical simulation, based on SR sampling, in the context of a hydrogeological flow and transport model via a synthetic case study. The results indicate that stratified sampling methods (including Latin hypercube sampling) are more efficient than SR, overall reproducing to a similar extent statistics of the conductivity (and subsequently concentration) fields, yet with smaller sampling variability. These findings suggest that the proposed efficient conditional sampling methods could contribute to the wider application of uncertainty analysis in spatially distributed environmental models using geostatistical simulation. 相似文献
9.
探讨了将蒙特卡罗(Monte Carlo)方法应用于多尺度非均质含水层中溶质运移模拟的方法。所研究的含水层由两种具有不同渗透系数统计特征的多孔介质所组成,每一种多孔介质是非均质的,且其渗透系数场符合平稳假设,而整个模拟区的渗透系数是非平稳的。Monte Carlo方法要求参数是平稳的,因此,分别对两种多孔介质产生若干随机渗透系数场后,用两种方法进行组合,并进行溶质运移的模拟计算。通过对计算结果的分析,综合考虑计算精度、计算时间等因素,得出了处理多尺度非均质多孔介质中溶质运移问题的较好方法。 相似文献
10.
为预测非饱和冻土的导热性能,基于土体微观结构,提出了非饱和冻土特征结构识别算法和多元素生成算法,并将该算法与传统有限单元法组合,建立非饱和冻土导热系数蒙特卡洛预测模型。通过土体SEM电镜图像,采用逆向四参数增长识别法识别土体中各组分含量、大小以及各方向分布概率;改进传统的四参数随机增长法,提出了考虑土、水、冰和气的多元素生成算法;基于生成的非饱和冻土模型,通过蒙特卡洛方法获得非饱和冻土导热系数,并与规范中冻土导热系数进行对比,验证了蒙特卡洛法预测模型的合理性（平均误差<4%）;通过多因素分析研究孔隙率、颗粒大小、土体导热性、饱和度以及结冰率对非饱和冻土导热性影响,各因素与导热系数的相关系数依次为：-0.352、-0.098、0.641、0.520和0.060,影响大小为：土颗粒导热性>饱和度>孔隙率>土颗粒大小>结冰率。各影响因素对非饱和冻土导热系数影响可以归纳为对热通量形成“热链”密度、宽度、连通性、热流承载力以及对“热桥”通量的影响。 相似文献
11.
Ernieza Suhana Mokhtar Biswajeet Pradhan Abd Halim Ghazali Helmi Zulhaidi Mohd Shafri 《Arabian Journal of Geosciences》2018,11(21):682
Water discharge is the main parameter in hydraulic modeling for flood hazard assessment. However, the unavailability of data on discharge and observed river morphologies resulted in erroneous calculations and irregularities in flood inundation mapping. The objectives of this study are (i) to investigate uncertainties of hydraulic parameters (width, cross-sectional depth, and channel slope) used in discharge equation and (ii) to examine the influence of estimate discharge on water extent and flood depth with different boundary conditions on interferometric synthetic aperture radar (IFSAR) and modified IFSAR DEMs. Sensitivity analysis was conducted with the Monte Carlo simulation method to generate random data combinations. Bjerklie’s equation was used to calculate discharge based on the three variables, and Manning’s n was substituted into the Hydrologic Engineering Center River Analysis System (HEC-RAS) model. TerraSAR-X was used to distinguish existing flood water bodies and normal water extent. The uncertainty of the combined variables was assessed with the likelihood measures such as F-statistic, mean absolute error, root mean square error, and Nash–Sutcliffe efficiency which compares observed and predicted inundated area as well as flood water depth simulated using the HEC-RAS model. 相似文献
12.
地下水流数值模拟过程中,水文地质参数的不确定性对模拟结果影响很大。以内蒙古鄂尔多斯市某水源地为例,利用拉丁超立方抽样(LHS)方法获得了含水层渗透参数的随机组合,进行地下水流随机模拟。通过对观测资料与计算水位的绝对值平均(MAE)误差和误差均方根(RMSE)的统计分析,获得了模型较为稳定的随机模拟次数是243次。利用该随机模型对水源地设计开采量进行水位预测,并给出允许降深的风险性分布图。结果表明,预测水位和标准差分布符合实际情况,水位降深大于35 m的风险性最大达到75%。 相似文献
13.
模型不确定性研究是水文科学的重要课题。以尼泊尔Bagmati流域为案例,采用了马尔科夫链蒙托卡罗(Markov Chain Monte Carlo)、蒙托卡罗(Monte Carlo)和拉丁超立方体(Latin Hypercube)等三种方法,分析了水箱模型输出成果的不确定性,并将三种方法所获得参数不确定性进行了比较。另外,运用Meta-Gaussian模型计算了总体不确定性,在基于所采用的似然函数基础上,对由参数导致模型输出的不确定性和模型输出的总体不确定性进行了比较。结果显示,模型的不确定性比参数不确定性更为重要,同时也表明,尽管蒙托卡罗和拉丁超立方体两种模拟方法产生几乎相同的结果,但两者都与马尔科夫链蒙托卡罗方法有很大的不同。 相似文献
14.
Steven B. Meyerhoff Reed M. Maxwell Wendy D. Graham John L. Williams III 《Hydrogeology Journal》2014,22(6):1329-1343
Subsurface heterogeneity is one of the largest sources of uncertainty associated with saturated hydraulic conductivity. Recent work has demonstrated that uncertainty in hydraulic conductivity can impart significant uncertainty in runoff generation processes and surface-water flow. Here, the role of site characterization in reducing hydrograph prediction bias and uncertainty is demonstrated. A fully integrated hydrologic model is used to conduct two sets of stochastic, transient simulation experiments comprising different overland flow mechanisms: Dunne and Hortonian. Conditioning hydraulic conductivity fields using values drawn from a simulated synthetic control case are shown to reduce both mean bias and variance in an ensemble of conditional hydrograph predictions when compared with the control case. The ensemble simulations show a greater reduction in uncertainty in the hydrographs for Hortonian flow. The conditional simulations predict surface ponding and surface pressure distributions with reduced mean error and reduced root mean square error compared with unconditional simulations. Uncertainty reduction in Hortonian and Dunne flow cases demonstrates different temporal signals, with more substantial reduction achieved for Hortonian flow. 相似文献
15.
一种基于贝叶斯理论的区域斜坡稳定性评价模型 总被引：1，自引：0，他引：1
本文结合滑坡物理模型和统计模型的优点,针对小流域滑坡稳定性分析,建立了一种基于贝叶斯理论的区域斜坡稳定性评价模型。该模型主要采用灾害自身信息来修正原始模型中的参数,解决了区域稳定性评价中参数难以确定的问题。该方法首先设定模型的初始参数分布,然后利用采样点雨前和雨后稳定性不同的信息建立验证方程,再根据马尔科夫链蒙特卡罗模拟和贝叶斯方法确定最终的参数分布,进而得出区域稳定性分布。应用该模型对福建省蔡源小流域的6 13滑坡群发性事件进行分析。结果显示,蔡源小流域地区的无量纲黏聚系数C为0.028,有效摩擦角为16.7,土壤的导水系数T和降雨量q的比值为529.026m,可能不稳定地区和不稳定地区达到76.0%。该模型利用历史灾害数据自动模拟出合适的参数,对区域滑坡稳定性评价具有重要意义。 相似文献
16.
Stochastic analysis of the effect of heterogeneity and fractures on radionuclide transport in a low-permeability clay layer 总被引：1，自引：0，他引：1
Deep low-permeability clay layers are considered as safe environments for disposal of high-level radioactive waste. In Belgium,
the Boom Clay is a candidate host rock for deep geological disposal. In this study, we analyze the effects of fractures and
spatially variable hydraulic conductivity on radionuclide migration through the clay. Fracture geometry and properties are
simulated with Monte Carlo simulation. The heterogeneity of hydraulic conductivity is simulated by direct sequential co-simulation
using measurements of hydraulic conductivity and four types of secondary variables. The hydraulic conductivity and fracture
simulations are used as input for a transport model. Radionuclide fluxes computed with this heterogeneous model are compared
with fluxes obtained with a homogeneous model. The output fluxes of the heterogeneous model differ at most 8% from the homogeneous
model. The main safety function of the Boom Clay is thus not affected by the fractures and the spatial variability of hydraulic
conductivity. 相似文献
17.
Slope stability assessment is a geotechnical problem characterized by many sources of uncertainty. Some of them, e.g., are connected to the variability of soil parameters involved in the analysis. Beginning from a correct geotechnical characterization of the examined site, only a complete approach to uncertainty matter can lead to a significant result. The purpose of this paper is to demonstrate how to model data uncertainty in order to perform slope stability analysis with a good degree of significance.
Once the input data have been determined, a probabilistic stability assessment (first-order second moment and Monte Carlo analysis) is performed to obtain the variation of failure probability vs. correlation coefficient between soil parameters. A first result is the demonstration of the stability of first-order second moment (FOSM) (both with normal and lognormal distribution assumption) and Monte Carlo (MC) solutions, coming from a correct uncertainty modelling. The paper presents a simple algorithm (Fuzzy First Order Second Moment, FFOSM), which uses a fuzzy-based analysis applied to data processing. 相似文献
18.
We investigate the uncertainty in bedrock depth and soil hydraulic parameters on the stability of a variably-saturated slope in Rio de Janeiro, Brazil. We couple Monte Carlo simulation of a three-dimensional flow model with numerical limit analysis to calculate confidence intervals of the safety factor using a 22-day rainfall record. We evaluate the marginal and joint impact of bedrock depth and soil hydraulic uncertainty. The mean safety factor and its 95% confidence interval evolve rapidly in response to the storm events. Explicit recognition of uncertainty in the hydraulic properties and depth to bedrock increases significantly the probability of failure. 相似文献
19.
掌握岩体的渗透性是精细化描述一个地区水文地质特征的重要工作。渗透系数是表征岩体渗透性的重要指标,研究渗透系数估算模型对于实际工程应用具有重要意义。在现有的渗透系数估算模型中,单因子模型忽略了其他因素对该地区渗透系数的综合影响,复合因子模型存在参数选取不够灵活、部分参数较难获取等问题。基于公开数据,分类整理和对比分析了影响裂隙岩体渗透性的正、负相关参数,提出一种拟合效果好、参数选取灵活的渗透系数估算模型——PNC（Positive and Negative Correlation）模型。研究结果表明:在研究区一,PNC模型的拟合效果（可决系数R2=0.964和R2=0.801）优于HC模型的拟合效果（R2=0.905和R2=0.563）;在研究区二,PNC模型的拟合效果（R2=0.959）优于RMP模型的拟合效果（R2=0.927）;在研究区三,PNC模型的拟合效果（R2=0.94～0.99）优于ZRF模型的拟合效果（R2=0.92～0.99）。利用纳什效率系数（Nash-Sutcliffe Coefficient,NSE）进行模型误差分析,7组数据中有5组数据的误差系数在0.95以上。这说明PNC模型具有便利性和可靠性,可以为实际工程估算和验证渗透系数提供一定的参考。 相似文献
20.
三维剖面地质界线是构建三维地质结构模型的重要基础数据,其不确定性会影响三维模型的几何形态和属性分布。以单一分布为假设前提的统计学不确定性分析方法掩盖了其他概率分布特征对模型的影响。突破单一误差分布条件的假设前提,本文使用Monte Carlo方法模拟了不同概率分布情况下地质剖面数据中地质界线的抽样采集,以及地质界线空间分布的不确定性;依托地质界线空间位置与地质属性的耦合关系,提出了用地质属性概率分布实现地质界线空间不确定性的定量可视化,并结合实际地质剖面探讨了多种概率分布条件下地质界线的空间不确定性。实例研究表明,基于Monte Carlo模拟的不确定性分析方法可以突破单一误差分布假设条件,结合地质属性概率可充分揭示出建模数据的内在不确定性与模型外在要素形态之间的耦合关系。 相似文献