Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Monte Carlo sampling

In the last few decades hydrologists have made tremendous progress in using dynamic simulation models for the analysis and understanding of hydrologic systems. However, predictions with these models are often deterministic and as such they focus on the most probable forecast, without an explicit estimate of the associated uncertainty. This uncertainty arises from incomplete process representation, uncertainty in initial conditions, input, output and parameter error. The generalized likelihood uncertainty estimation (GLUE) framework was one of the first attempts to represent prediction uncertainty within the context of Monte Carlo (MC) analysis coupled with Bayesian estimation and propagation of uncertainty. Because of its flexibility, ease of implementation and its suitability for parallel implementation on distributed computer systems, the GLUE method has been used in a wide variety of applications. However, the MC based sampling strategy of the prior parameter space typically utilized in GLUE is not particularly efficient in finding behavioral simulations. This becomes especially problematic for high-dimensional parameter estimation problems, and in the case of complex simulation models that require significant computational time to run and produce the desired output. In this paper we improve the computational efficiency of GLUE by sampling the prior parameter space using an adaptive Markov Chain Monte Carlo scheme (the Shuffled Complex Evolution Metropolis (SCEM-UA) algorithm). Moreover, we propose an alternative strategy to determine the value of the cutoff threshold based on the appropriate coverage of the resulting uncertainty bounds. We demonstrate the superiority of this revised GLUE method with three different conceptual watershed models of increasing complexity, using both synthetic and real-world streamflow data from two catchments with different hydrologic regimes.     

裂隙岩体的多孔介质水力等效性研究

工程岩体中数量众多的裂隙面限制了离散裂隙网络模型在岩体渗流中的应用,迫使人们寻找能够用理论上成熟的等效连续介质模型替代,这就要求进行岩体多孔介质的水力等效性研究。在野外大量实测裂隙的基础上,进行裂隙密度、方位、大小、延续性、开度等几何参数的统计分析,以Enhanced Baecher模型建立离散裂隙网络随机模型,采用Monte-Carlo随机模拟方法进行三维裂隙网络随机模拟。在所生成的一定尺度的三维裂隙网络图基础上,给出计算研究域REV的方法,通过判断REV是否存在,确定能否用等效连续介质模型分析岩体渗流。     

降雨条件下考虑饱和渗透系数变异性的边坡可靠度分析

土体饱和渗透系数表现为天然的变异性,为此基于Green-Ampt模型建立了考虑饱和渗透系数变异性的降雨入渗物理模型,并藉此模型确定了坡体湿润锋深度和含水率分布。然后结合无限长非饱和土边坡稳定模型得到解析形式的反映边坡稳定性的极限状态函数。采用Monte Carlo法对饱和渗透系数进行随机抽样并最终建立降雨条件下考虑饱和渗透系数变异性的边坡概率分析框架。针对一假想边坡,探讨了饱和渗透系数的变异系数、降雨持时和降雨强度对边坡破坏概率以及破坏发生时间概率分布的影响,结果表明：在降雨初期,边坡的破坏概率随饱和渗透系数变异性的增强而逐渐增加,但随着降雨的持续,破坏概率开始随变异性的增强而显著降低;滑坡最可能发生时间的大小并不受饱和渗透系数变异性的影响,而是直接取决于降雨强度;滑坡最可能发生时间所对应的概率却随变异性的增强而逐渐减小。     

Modelling river water temperature using deterministic,empirical, and hybrid formulations in a Mediterranean stream

River water temperature is a common target of water quality models at the watershed scale, owing to its principal role in shaping biogeochemical processes and in stream ecology. Usually, models include physically‐based, deterministic formulations to calculate water temperatures from detailed meteorological information, which usually comes from meteorological stations located far from the river reaches. However, alternative empirical approaches have been proposed, that usually depend on air temperature as master variable. This study explored the performance of a semidistributed water quality application modelling river water temperature in a Mediterranean watershed, using three different approaches. First, a deterministic approach was used accounting for the different heat exchange components usually considered in water temperature models. Second, an empirical approximation was applied using the equilibrium temperature concept, assuming a linear relationship with air temperature. And third, a hybrid approach was constructed, in which the temperature equilibrium concept and the deterministic approach were combined. Results showed that the hybrid approach gave the best results, followed by the empirical approximation. The deterministic formulation gave the worst results. The hybrid approach not only fitted daily river water temperatures, but also adequately modelled the daily temperature range (maximum–minimum daily temperature). Other river water features directly dependent on water temperature, such as river intrusion depth in lentic systems (i.e. the depth at which the river inflow plunges to equilibrate density differences with lake water), were also correctly modelled even at hourly time steps. However, results for the different heat fluxes between river and atmosphere were very unrealistic. Although direct evidence of discrepancies between meteorological drivers measured at the meteorological stations and the actual river microclimate was not found, the use of models including empirical or hybrid formulations depending mainly on air temperature is recommended if only meteorological data from locations far from the river reaches are available. Copyright © 2008 John Wiley & Sons, Ltd.     

地磁匹配导航算法研究

提出一种MonteCarlo方法和均方差算法（MSD）相结合的匹配算法，并将本算法用于地磁匹配导航中。该方法根据惯性导航系统指示的位置从地磁图上提取参考地磁数据，将地磁测量数据进行若干次随机干扰后和参考地磁数据使用MSD算法进行匹配，得到若干个匹配位置，取其均值作为最终匹配位置。仿真结果表明本算法可行。     

基于岩体精细化描述的围岩分类及力学参数概率分布特征分析

针对目前水电站地下厂房工程中不同围岩分类方法存在评价结果不一致、围岩力学参数存在室内试验值与实际情况不吻合的现象,现推荐采用岩体精细化描述体系对围岩岩体结构进行定量化评价。将常用围岩分类方法(RMR、Q、RMi、GSI、BQ、HC)评价指标予以归纳分组,并通过各组内不同指标对比分析获得围岩分类方法中的基础评价指标。以大岗山水电站主厂房某区段为分析对象,采取现场岩体精细化地质素描与后期数据挖掘、拟合相结合方法,并依据评价指标间的关联关系,获得了基础、非基础评价指标的分布概型及对应参数,实现对该段围岩岩体精细化描述认知;基于精细化描述结果,应用Monte Carlo法生成符合各评价指标分布概型的大量随机数,而后参照各分类方法评价思路与评分流程,得到评价指标在各分类方法对应的大量随机评分值,通过归纳统计获得不同围岩分类方法评价结果的分布概型;基于各围岩分类方法评价结果与力学参数值之间的关联关系实现对力学参数概率特征分析。该分析方法与思路可为类似工程围岩质量及力学参数的精确确定提供一定借鉴,并可为实现围岩支护极限状态设计提供必要的原始参数支持。     

Reliability analysis of piles in spatially varying soils considering multiple failure modes

In most limit state design codes, the serviceability limit checks for drilled shafts still use deterministic approaches. Moreover, different limit states are usually considered separately. This paper develops a probabilistic framework to assess the serviceability performance with the consideration of soil spatial variability in reliability analysis. Specifically, the performance of a drilled shaft is defined in terms of the vertical settlement, lateral deflection, and angular distortion at the top of the shaft, corresponding to three limit states in the reliability analysis. Failure is defined as the event that the displacements exceed the corresponding tolerable displacements. The spatial variability of soil properties is considered using random field modeling. To illustrate the proposed framework, this study assesses the reliability of each limit state and the system reliability of a numerical example of a drilled shaft. The results show the system reliability should be considered for the serviceability performance. The importance measures of the random variables indicate that the external loads, the performance criteria, the model errors of load transfer curves and soil strength parameter are the most important factors in reliability analysis. Moreover, it is shown that the correlation length and coefficient of variation of soil strength can exert significant impacts on the calculated failure probability.     

Primary proton spectrum around the knee deduced from the emulsion-chamber data obtained at Mts. Fuji and Kanbala

We have done extensive Monte Carlo simulations using the new simulation codes of CORSIKA and COSMOS to compare with the gamma-family data obtained at Mts. Fuji (3750 m above sea level) and Kanbala (5500 m above sea level). Then, we estimated the primary proton and helium spectra around the knee energy region using a multiple-layered feed-forward neural network as a classifier of primary particle kind. The selection efficiency of proton-induced family events is estimated to be 82%. The flux value of protons at 2×10¹⁵ eV is (5.5±1.5)×10⁻¹⁴ (m⁻² s⁻¹ sr⁻¹ GeV⁻¹). The result suggests heavy-enriched primary composition around the knee region.     

Shallow landslides in deeply weathered slates of the Sierra de Collcerola (Catalonian Coastal Range, Spain)

Stability conditions in an area located NW of Barcelona (Spain) are discussed. Here, several mass movements were observed, mainly affecting weathered Paleozoic slates. Many of these failures involved slopes cut along recent infrastructures: debris flows, wedge and plane failures, generally surficial, occurred more frequently. After a detailed geological and geomorphologic survey, geomechanic characterization was carried out, according to RMR and SMR classifications. This rating gave a prediction of slope behaviour, in fairly good agreement with the real observed one.

Stability numerical analysis was carried out for the main cut slopes, based upon the Limit Equilibrium Method. First of all, the deterministic factor of safety was computed using the mean values of parameters. After that, a simulation technique based upon the Monte Carlo Method was applied in order to obtain factor of safety distributions. The probability of failure was estimated as P(F<1).

Finally, results from deterministic and probabilistic approaches were compared. The effectiveness of different possible remedial measures was highlighted by means of a sensitivity analysis, which showed that the more important parameters in the study area are the geometrical ones (height, slope and failure plane angles). The final technical solutions adopted are briefly outlined.     

An Improved Perturbation Mechanism for Simulated Annealing Simulation

Simulated annealing (SA) is being increasingly used for the generation of stochastic models of spatial phenomena because of its flexibility to integrate data of diverse types and scales. The major shortcoming of SA is the extensive CPU requirements. We present a perturbation mechanism that significantly improves the CPU speed. Two conventional perturbation mechanisms are to (1) randomly select two locations and swap their attribute values, or (2) visit a randomly selected location and draw a new value from the global histogram. The proposed perturbation mechanism is a modification of option 2: each candidate value is drawn from a local conditional distribution built with a template of kriging weights rather than from the global distribution. This results in accepting more perturbations and in perturbations that improve the variogram reproduction for short scale lags. We document the new method, the increased convergence speed, and the improved variogram reproduction. Implementation details of the method such as the size of the local neighborhood are considered.