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桩-土动力相互作用分析模型的对比分析 总被引：3，自引：0，他引：3
本文对常用的刚结模型、铰结模型、接触模型、简化模型和弹簧模型五种桩-土动力相互作用分析模型进行了对比研究,分析发现：不同的分析模型有不同的适用范围,在桩-土动力相互作用分析时应根据所关心的问题选用不同的分析模型。本文给出了选择模型的建议,一般优先采用接触模型,次优为铰结模型。 相似文献
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The effect of forcing and landscape distribution on performance and consistency of model structures 
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下载免费PDF全文 It is often challenging to determine the appropriate level of spatial model forcing and model distribution in conceptual rainfall‐runoff modelling. This paper compares the value of incorporating both spatially distributed forcing data and spatially distributed model conceptualisations based on landscape heterogeneity, applied to the Ourthe catchment in Belgium. Distributed forcing data were used to create a spatial distribution of model states. Eight different configurations were tested: a lumped and distributed model structure, each with four levels of model state distribution. The results show that in the study catchment the distributed model structure can in general better reproduce the dynamics of the hydrograph, and furthermore, that the differences in performance and consistency between calibration and validation are smallest for the distributed model structure with distributed model states. For the Ourthe catchment, it can be concluded that the positive effect of incorporating a distributed model structure is larger than that of incorporating distributed model states. Distribution of model structure increases both model performance and consistency. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
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A coupled surface resistance model to estimate crop evapotranspiration in arid region of northwest China 总被引：1，自引：0，他引：1 下载免费PDF全文
下载免费PDF全文 The Penman–Monteith (PM) model has been widely used to estimate crop evapotranspiration (ET), but it performs poorly with sparse vegetation. By combining the Jarvis canopy resistance model and the soil resistance model, we have developed a coupled surface resistance model to address this issue. Maize field and vineyard ET, measured by the eddy covariance method during 2007 and 2008, were used to test the estimations produced by the PM model combined with our coupled surface resistance model and Jarvis model, respectively. Results indicate that PM model combined with the coupled surface resistance model produces higher determination coefficient and lower root mean square error when compared with the PM–Jarvis method, either for maize field or for the sparse vineyard, on half‐hourly or daily time scales. Our study confirms that the coupled surface resistance model produces higher accuracy than the Jarvis model and provides a method to calculate resistance parameters for using the PM model to simulate the ET of sparse vegetation. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献
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《水文科学杂志》2013,58(5):872-885
Abstract The “optimal” model complexity is defined as the minimum watershed model structure required for realistic representation of runoff processes. This paper examines the effects of model complexity at different time scales, daily and hourly. Two watershed models with different levels of complexity were constructed and their capability to simulate runoff from a watershed was evaluated. Both models were tested on the same watershed using identical meteorological input, thereby assuring that any difference between model outputs is due only to their model structure. It is demonstrated that, at a daily time scale, a simple model gives good results. For the mountain situation, in which snowmelt is a dominant influence, the nonlinearity of the runoff processes is moderate, and therefore a simple model works well. The model produced good results over a period of 28 years of continuous simulation. However, this simpler model was inadequate when tested on an hourly time scale due to greater nonlinear effects, especially when modelling high-intensity rainfall events. Therefore, the hourly simulation benefited from the more complex model structure. These model results show that optimal watershed model complexity depends on temporal resolution, namely the simulation period and the computational time step. It was shown that certain process representations and model parameters that appeared unimportant during the long-term simulation had significant effects on the short-term extreme event model simulation. 相似文献
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A key point in the application of multi‐model Bayesian averaging techniques to assess the predictive uncertainty in groundwater modelling applications is the definition of prior model probabilities, which reflect the prior perception about the plausibility of alternative models. In this work the influence of prior knowledge and prior model probabilities on posterior model probabilities, multi‐model predictions, and conceptual model uncertainty estimations is analysed. The sensitivity to prior model probabilities is assessed using an extensive numerical analysis in which the prior probability space of a set of plausible conceptualizations is discretized to obtain a large ensemble of possible combinations of prior model probabilities. Additionally, the value of prior knowledge about alternative models in reducing conceptual model uncertainty is assessed by considering three example knowledge states, expressed as quantitative relations among the alternative models. A constrained maximum entropy approach is used to find the set of prior model probabilities that correspond to the different prior knowledge states. For illustrative purposes, a three‐dimensional hypothetical setup approximated by seven alternative conceptual models is employed. Results show that posterior model probabilities, leading moments of the predictive distributions and estimations of conceptual model uncertainty are very sensitive to prior model probabilities, indicating the relevance of selecting proper prior probabilities. Additionally, including proper prior knowledge improves the predictive performance of the multi‐model approach, expressed by reductions of the multi‐model prediction variances by up to 60% compared with a non‐informative case. However, the ratio between‐model to total variance does not substantially decrease. This suggests that the contribution of conceptual model uncertainty to the total variance cannot be further reduced based only on prior knowledge about the plausibility of alternative models. These results advocate including proper prior knowledge about alternative conceptualizations in combination with extra conditioning data to further reduce conceptual model uncertainty in groundwater modelling predictions. Copyright © 2009 John Wiley & Sons, Ltd. 相似文献
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A complementary relationship evaporation model has been proposed and verified based on evaluations of the advection–aridity model and the Granger's complementary relationship model (Granger model) in dimensionless forms. Normalized by Penman potential evaporation, the Granger model and the advection–aridity model have been transformed into similar dimensionless forms. Evaporation ratio (ratio of actual evaporation to Penman potential evaporation) has been expressed as a function of dimensionless variable based on radiation and atmospheric conditions. Similar dimensionless variables for the different functions have been used in the two models. By referring to the dimensionless variable from the advection–aridity model and the function from the Granger model, a new model to estimate actual evaporation was proposed. The performance of the new model has been validated by the observed data from four sites under different land covers. The new model is an enhanced Granger model with better evaporation prediction over the aforementioned different land covers. It also offers more stable optimized parameters in a grassland site than the Granger model. The new model somewhat approximates the advection–aridity model under neither too wet nor too dry conditions, but without its system bias. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
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Considering complexity in groundwater modeling can aid in selecting an optimal model, and can avoid over parameterization, model uncertainty, and misleading conclusions. This study was designed to determine the uncertainty arising from model complexity, and to identify how complexity affects model uncertainty. The Ajabshir aquifer, located in East Azerbaijan, Iran, was used for comprehensive hydrogeological studies and modeling. Six unique conceptual models with four different degrees of complexity measured by the number of calibrated model parameters (6, 10, 10, 13, 13 and 15 parameters) were compared and characterized with alternative geological interpretations, recharge estimates and boundary conditions. The models were developed with Model Muse and calibrated using UCODE with the same set of observed data of hydraulic head. Different methods were used to calculate model probability and model weight to explore model complexity, including Bayesian model averaging, model selection criteria, and multicriteria decision-making (MCDM). With the model selection criteria of AIC, AICc and BIC, the simplest model received the highest model probability. The model selection criterion, KIC, and the MCDM method, in addition to considering the quality of model fit between observed and simulated data and the number of calibrated parameters, also consider uncertainty in parameter estimates with a Fisher information matrix. KIC and MCDM selected a model with moderate complexity (10 parameters) and the best parameter estimation (model 3) as the best models, over another model with the same degree of complexity (model 2). The results of these comparisons show that in choosing between models, priority should be given to quality of the data and parameter estimation rather than degree of complexity. 相似文献
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根据电磁波的传播特性，把视电阻率随周期变化的曲线转化为电阻率随深度变化的曲线，并以此作为初始反演的地电模型．通过初始地电模型得到的视电阻率曲线与真实模型的视电阻率曲线的对比，对初始地电模型的电阻率值进行校正．校正后的反演模型的视电阻率曲线与真实模型的视电阻曲线的拟合程度有所提高．然后如此反复进行多次校正，获得与真实模型更为接近的反演模型，反演的拟合误差一般小于ｌ％．模型试验和实际例子表明，该方法的拟合程度优于Ｂｏｓｔｉｃｋ法． 相似文献
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合理选择本构模型是土动力学问题数值模拟中的一项重要工作。利用PLAXIS 2D软件的土工实验模拟功能分别对4种常用的岩土本构模型——线弹性模型、摩尔库伦模型、土体硬化模型和小应变土体硬化模型在往复荷载下的理论滞回曲线进行了对比分析,并在此基础上研究了选择不同本构模型对自由场地震反应分析结果的影响以及不同本构模型中各参数的变化对场地动力计算结果的敏感性分析。研究结果为土动力学问题数值模拟中如何选择本构模型和合理判断数值分析结果提供了参考依据。 相似文献
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Comparison of Seismic Dispersion and Attenuation Models 总被引：2，自引：0，他引：2
The frequency-dependent attenuation of seismic waves causes decreased resolution of seismic images with depth, and the difference in transmission losses induces amplitude variations with offset. Transmission losses may occur due to friction or fluid movement, or may result from scattering in thin-layer. Whatever the physical mechanism, they can often be conveniently described using an empirical formulation wherein the elastic moduli and propagation velocity are complex functions of frequency.We have compiled and compared algebraically and numerically eight different models involving complex velocity: the Kolsky-Futterman model, the power-law model, Kjartansson's model, Müller's model, Azimi's second and third model, the Cole-Cole model, and the standard linear-solid model.For two different parameter sets, the attenuation and phase velocity are computed in the seismic frequency band, and the plane-wave propagation of a Ricker wavelet for the other models is compared with that for the Kolsky-Futterman model. The first parameter set consists of parameters for each of the models calculated from expressions given in the appendix. These expressions make the different models behave similarly to the KF model. The second parameter set consists of model parameters that are numerically adapted to the KF model.By selecting proper parameters, all models, except the standard linear-solid model, show behavior similar to that of the Kolsky-Futterman model. The SLS model behaves differently from the other models as the frequency goes to zero or infinity. Broadband measurement data is needed to select a specific model for a given seismic experiment. 相似文献
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This paper provides a procedure for the evaluation of model performance for rainfall–runoff event summary variables, such as total discharge or peak runoff. The procedure is based on the analysis of model errors, defined as the differences between observed values and values predicted by a simulation model. Model errors can (i) indicate whether and where the model can be improved, (ii) be used to measure the performance of a model, and (iii) be used to compare model simulations. In this paper, both statistical and graphical methods are used to characterize model errors. We explore model recalibration by relating model errors to the model predictions, and to external, independent variables. The R‐5 catchment data sets that we used in this study include summary variables for 72 rainfall–runoff events. The simulations used in this study were previously conducted with the quasi‐physically based rainfall–runoff model QPBRRM for 11 different characterizations of the R‐5 catchment, each with increasing information or a refined spatial discretization of the overland flow planes. This paper is about proposing model diagnostics and not about procedures for using diagnostics for model modification. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
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Ming-Chang Wu Gwo-Fong Lin Hsuan-Yu Lin 《Stochastic Environmental Research and Risk Assessment (SERRA)》2013,27(7):1661-1671
The model performance is usually influenced by the quality of the data used in model construction. If the model performance is less affected by data quality, the resulting estimates will be more reliable. In this paper, the variation in model performance due to different data quality is explored in a field-scale application. Hence, two models, the proposed support vector machine (SVM) based model and the Stephen and Stewart (SS) model, are employed for daily estimation of evaporation at an experiment station. Five scenarios corresponding to different data qualities are designed to evaluate the effect of data quality on model performance. Additionally, the most effective meteorological variables influencing evaporation are obtained by a systematic input determination process. These most effective meteorological variables are used as inputs to the SVM-based model. The results show that the model performance decreases as the data quality decreases (i.e. the percentage of missing data increases). However, the estimation accuracy of SVM-based models is still better than that of the SS model. Moreover, the variation of model performance of the SVM-based model is smaller than that of the SS model. That is, the negative impact of different data quality is effectively decreased by using the SVM-based model instead of the SS model. 相似文献
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建立符合油气储层近地表复杂介质强衰减性质的数学-物理模型是油气勘探和开发的重要课题.本文针对D'Alembert黏弹性介质模型存在的量纲不统一和不能充分刻画孔隙介质结构的不足,通过引入孔隙度和渗透率等参数修改耗散项,改进了原D'Alembert模型,获得了能精细刻画具有强吸收衰减特征的近地表复杂介质模型,即改进的近地表黏弹性模型.基于这种改进的模型,推导了波频散和衰减的表达式,并研究了孔隙度、流体黏度等物理参数对波频散和衰减的影响,获得了相应的规律性认识.为了验证新模型预测近地表介质中波衰减的有效性,本文将新模型应用于胜利油田YX工区的近地表实测数据,同时与弹性Biot模型和BISQ模型以及黏弹性BISQ模型进行了比较.结果表明,与其它三个模型比较,改进的近地表黏弹性模型能够很好地刻画近地表介质的强衰减性,而且新模型所涉及到的物理参数明显少于其它几种模型,有利于新模型在油气储层近地表复杂介质地震勘探的实际应用. 相似文献
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The identifiability of parameters in a water quality model of the Biebrza River, Poland 总被引：1，自引：0，他引：1
The identifiability of model parameters of a steady state water quality model of the Biebrza River and the resulting variation in model results was examined by applying the Monte Carlo method which combines calibration, identifiability analysis, uncertainty analysis, and sensitivity analysis. The water quality model simulates the steady state concentration profiles of chloride, phosphate, ammonium, and nitrate as a function of distance along a river. The water quality model with the best combination of parameter values simulates the observed concentrations very well. However, the range of possible modelled concentrations obtained for other more or less equally eligible combinations of parameter values is rather wide. This range in model outcomes reflects possible errors in the model parameters. Discrepancies between the range in model outcomes and the validation data set are only caused by errors in model structure, or (measurement) errors in boundary conditions or input variables. In this sense the validation procedure is a test of model capability, where the effects of calibration errors are filtered out. It is concluded that, despite some slight deviations between model outcome and observations, the model is successful in simulating the spatial pattern of nutrient concentrations in the Biebrza River. 相似文献
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合理的随机模型是确定高精度卫星轨道的前提条件,目前广泛应用于地面观测数据的随机模型主要有高度角模型和载噪比模型,本文通过对GRACE卫星实测数据的分析表明上述随机模型均不能很好地描述GRACE卫星星载GPS观测值的噪声特点,为此,文中提出了扩展的高度角模型和扩展的载噪比随机模型.利用自主研发的精密定轨软件,分别采用高度角模型、扩展的高度角模型、载噪比模型、扩展的载噪比模型对GRACE卫星进行了轨道确定.数值结果表明:(1)高度角模型的运动学轨道径向精度为3.4 cm,扩展的高度角模型的为3.3 cm;(2)载噪比模型的运动学轨道径向精度为4.9 cm,扩展的载噪比模型的则为3.4 cm,精度提高了1.5 cm.经比较分析,文中提出的扩展的高度角模型和载噪比模型能更好地描述GRACE卫星观测值噪声特点,并能取得更高的卫星定轨精度. 相似文献