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1.
Probability theory as logic (or Bayesian probability theory) is a rational inferential methodology that provides a natural
and logically consistent framework for source reconstruction. This methodology fully utilizes the information provided by
a limited number of noisy concentration data obtained from a network of sensors and combines it in a consistent manner with
the available prior knowledge (mathematical representation of relevant physical laws), hence providing a rigorous basis for
the assimilation of this data into models of atmospheric dispersion for the purpose of contaminant source reconstruction.
This paper addresses the application of this framework to the reconstruction of contaminant source distributions consisting
of an unknown number of localized sources, using concentration measurements obtained from a sensor array. To this purpose,
Bayesian probability theory is used to formulate the full joint posterior probability density function for the parameters
of the unknown source distribution. A simulated annealing algorithm, applied in conjunction with a reversible-jump Markov
chain Monte Carlo technique, is used to draw random samples of source distribution models from the posterior probability density
function. The methodology is validated against a real (full-scale) atmospheric dispersion experiment involving a multiple
point source release. 相似文献
2.
Contaminant source identification in groundwater system is critical for remediation strategy implementation, including gathering further samples and analysis, as well as implementing and evaluating different remediation plans. Such problem is usually solved with the aid of groundwater modeling with lots of uncertainty, e.g. existing uncertainty in hydraulic conductivity, measurement variance and the model structure error. Monte Carlo simulation of flow model allows the input uncertainty onto the model predictions of concentration measurements at monitoring sites. Bayesian approach provides the advantage to update estimation. This paper presents an application of a dynamic framework coupling with a three dimensional groundwater modeling scheme in contamination source identification of groundwater. Markov Chain Monte Carlo (MCMC) is being applied to infer the possible location and magnitude of contamination source. Uncertainty existing in heterogonous hydraulic conductivity field is explicitly considered in evaluating the likelihood function. Unlike other inverse-problem approaches to provide single but maybe untrue solution, the MCMC algorithm provides probability distributions over estimated parameters. Results from this algorithm offer a probabilistic inference of the location and concentration of released contamination. The convergence analysis of MCMC reveals the effectiveness of the proposed algorithm. Further investigation to extend this study is also discussed. 相似文献
3.
The successful operation of buried infrastructure within urban environments is fundamental to the conservation of modern living standards. In this paper a novel multi-sensor image fusion framework has been proposed and investigated using dynamic Bayesian network for automatic detection of buried underworld infrastructure. Experimental multi-sensors images were acquired for a known buried plastic water pipe using Vibro-acoustic sensor based location methods and Ground Penetrating Radar imaging system. Computationally intelligent conventional image processing techniques were used to process three types of sensory images. Independently extracted depth and location information from different images regarding the target pipe were fused together using dynamic Bayesian network to predict the maximum probable location and depth of the pipe. The outcome from this study was very encouraging as it was able to detect the target pipe with high accuracy compared with the currently existing pipe survey map. The approach was also applied successfully to produce a best probable 3D buried asset map. 相似文献
4.
Efficient Bayesian sensor placement algorithm for structural identification: a general approach for multi‐type sensory systems 
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下载免费PDF全文 In this paper, a Bayesian sequential sensor placement algorithm, based on the robust information entropy, is proposed for multi‐type of sensors. The presented methodology has two salient features. It is a holistic approach such that the overall performance of various types of sensors at different locations is assessed. Therefore, it provides a rational and effective strategy to design the sensor configuration, which optimizes the use of various available resources. This sequential algorithm is very efficient due to its Bayesian nature, in which prior distribution can be incorporated. Therefore, it avoids the possible unidentifiability problem encountered in a sequential process, which starts with small number of sensors. The proposed algorithm is demonstrated using a shear building and a lattice tower with consideration of up to four types of sensors. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
5.
Jasper A. Vrugt Cajo J. F. ter Braak Hoshin V. Gupta Bruce A. Robinson 《Stochastic Environmental Research and Risk Assessment (SERRA)》2009,23(7):1061-1062
In recent years, a strong debate has emerged in the hydrologic literature regarding what constitutes an appropriate framework
for uncertainty estimation. Particularly, there is strong disagreement whether an uncertainty framework should have its roots
within a proper statistical (Bayesian) context, or whether such a framework should be based on a different philosophy and
implement informal measures and weaker inference to summarize parameter and predictive distributions. In this paper, we compare
a formal Bayesian approach using Markov Chain Monte Carlo (MCMC) with generalized likelihood uncertainty estimation (GLUE)
for assessing uncertainty in conceptual watershed modeling. Our formal Bayesian approach is implemented using the recently
developed differential evolution adaptive metropolis (DREAM) MCMC scheme with a likelihood function that explicitly considers
model structural, input and parameter uncertainty. Our results demonstrate that DREAM and GLUE can generate very similar estimates
of total streamflow uncertainty. This suggests that formal and informal Bayesian approaches have more common ground than the
hydrologic literature and ongoing debate might suggest. The main advantage of formal approaches is, however, that they attempt
to disentangle the effect of forcing, parameter and model structural error on total predictive uncertainty. This is key to
improving hydrologic theory and to better understand and predict the flow of water through catchments. 相似文献
6.
Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling? 总被引：1，自引：0，他引：1
Jasper A. Vrugt Cajo J. F. ter Braak Hoshin V. Gupta Bruce A. Robinson 《Stochastic Environmental Research and Risk Assessment (SERRA)》2009,23(7):1011-1026
In recent years, a strong debate has emerged in the hydrologic literature regarding what constitutes an appropriate framework for uncertainty estimation. Particularly, there is strong disagreement whether an uncertainty framework should have its roots within a proper statistical (Bayesian) context, or whether such a framework should be based on a different philosophy and implement informal measures and weaker inference to summarize parameter and predictive distributions. In this paper, we compare a formal Bayesian approach using Markov Chain Monte Carlo (MCMC) with generalized likelihood uncertainty estimation (GLUE) for assessing uncertainty in conceptual watershed modeling. Our formal Bayesian approach is implemented using the recently developed differential evolution adaptive metropolis (DREAM) MCMC scheme with a likelihood function that explicitly considers model structural, input and parameter uncertainty. Our results demonstrate that DREAM and GLUE can generate very similar estimates of total streamflow uncertainty. This suggests that formal and informal Bayesian approaches have more common ground than the hydrologic literature and ongoing debate might suggest. The main advantage of formal approaches is, however, that they attempt to disentangle the effect of forcing, parameter and model structural error on total predictive uncertainty. This is key to improving hydrologic theory and to better understand and predict the flow of water through catchments. 相似文献
7.
In this paper, we investigate the information content in “nanosensors” with limited functionality that might be injected into a reservoir or an aquifer to provide information on the spatial distribution of properties. The two types of sensors that we consider are sensors that can potentially measure pressure at various times during transport, and sensors can be located in space by perturbations in electrical, magnetic, or acoustic properties. The intent of the study is to determine the resolution of estimates of properties that can be obtained from various combinations of sensors, various frequencies of observations, and various specifications on sensor precision.Our goal is to investigate the resolution of model estimates for various types of measurements. For this, we compute linearized estimates of the sensitivity of the observations to the porosity and permeability assuming gaussian errors in the pressure and location observations. Because the flow is one-dimensional and incompressible, observations of location are sensitive to the porosity between the injection location and the sensor location, while the location of particles is sensitive to the effective permeability over the entire interval from injector to producer. When only the pressure is measured but the location of the sensor is unknown, as might be the situation for a threshold sensor, the pressure is sensitive to both permeability and porosity only in the region between the injector and sensor.In addition to the linearized sensitivity and resolution analyses, Markov chain Monte Carlo sampling is used to estimate the posterior pdf for model variables for realistic (non-Gaussian) likelihood models. For a Markov chain of length one million samples approximately 200-500 independent samples are generated for uncertainty and resolution assessment. Results from the MCMC analysis are not in conflict with the linearized analysis. 相似文献
8.
实验室声发射三维定位及标本波速场各向异性研究 总被引：1，自引：0，他引：1
根据慢度离差法的基本原理,给出由遗传算法确定AE空间位置、发生时刻及慢度离差5个参量的具体方法。结合实验条件,通过数值试验对定位误差等问题进行探讨,并对真实AE定位的误差分布给出统计上的圈定。数值试验结果表明,算法具有较高的精度和较好的收敛性及稳健性;探头数量及布设方式对定位结果的优劣有影响, 4个以上探头有记录时,即可得到理想的结果;大的定位误差主要来源于台阵外部少数“ AE”的结果。到时测量的随机误差小于最小测量时间单位的50%时,平均有97%的“AE”定位误差分布在3 mm 范围内,小于物理不可分辨精度(探头直径)。 相似文献
9.
This paper develops a new method for decision-making under uncertainty. The method, Bayesian Programming (BP), addresses a class of two-stage decision problems with features that are common in environmental and water resources. BP is applicable to two-stage combinatorial problems characterized by uncertainty in unobservable parameters, only some of which is resolved upon observation of the outcome of the first-stage decision. The framework also naturally accommodates stochastic behavior, which has the effect of impeding uncertainty resolution. With the incorporation of systematic methods for decision search and Monte Carlo methods for Bayesian analysis, BP addresses limitations of other decision-analytic approaches for this class of problems, including conventional decision tree analysis and stochastic programming. The methodology is demonstrated with an illustrative problem of water quality pollution control. Its effectiveness for this problem is compared to alternative approaches, including a single-stage model in which expected costs are minimized and a deterministic model in which uncertain parameters are replaced by their mean values. A new term, the expected value of including uncertainty resolution, or EVIUR, is introduced and evaluated for the illustrative problem. It is a measure of the worth of incorporating the experimental value of decisions into an optimal decision-making framework. For the illustrative problem, the two-stage adaptive management framework extracted up to approximately 50% of the gains of perfect information. The strength and limitations of the method are discussed and conclusions are presented. 相似文献
10.
Estimation and prediction of the spatial occurrence of fish species using Bayesian latent Gaussian models 总被引：3，自引：3，他引：0
Facundo Muñoz M. Grazia Pennino David Conesa Antonio López-Quílez José M. Bellido 《Stochastic Environmental Research and Risk Assessment (SERRA)》2013,27(5):1171-1180
A methodological approach for modelling the occurrence patterns of species for the purpose of fisheries management is proposed here. The presence/absence of the species is modelled with a hierarchical Bayesian spatial model using the geographical and environmental characteristics of each fishing location. Maps of predicted probabilities of presence are generated using Bayesian kriging. Bayesian inference on the parameters and prediction of presence/absence in new locations (Bayesian kriging) are made by considering the model as a latent Gaussian model, which allows the use of the integrated nested Laplace approximation ( INLA ) software (which has been seen to be quite a bit faster than the well-known MCMC methods). In particular, the spatial effect has been implemented with the stochastic partial differential equation (SPDE) approach. The methodology is evaluated on Mediterranean horse mackerel (Trachurus mediterraneus) in the Western Mediterranean. The analysis shows that environmental and geographical factors can play an important role in directing local distribution and variability in the occurrence of species. Although this approach is used to recognize the habitat of mackerel, it could also be for other different species and life stages in order to improve knowledge of fish populations and communities. 相似文献
11.
Identification of uncertainty in low flow frequency analysis using Bayesian MCMC method 总被引：1，自引：0，他引：1
This study employs the Bayesian Markov Chain Monte Carlo (MCMC) method with the Metropolis–Hastings algorithm and maximum likelihood estimation (MLE) using a quadratic approximation of the likelihood function for the evaluation of uncertainties in low flow frequency analysis using a two‐parameter Weibull distribution. The two types of prior distributions, a non‐data‐based distribution and a data‐based distribution using regional information collected from neighbouring stations, are used to establish a posterior distribution. Eight case studies using the synthetic data with a sample size of 100, generated from two‐parameter Weibull distribution, are performed to compare with results of analysis using MLE and Bayesian MCMC. Also, Bayesian MCMC and MLE are applied to 36 years of gauged data to validate the efficiency of the developed scheme. These examples illustrate the advantages of Bayesian MCMC and the limitations of MLE based on a quadratic approximation. From the point of view of uncertainty analysis, Bayesian MCMC is more effective than MLE using a quadratic approximation when the sample size is small. In particular, Bayesian MCMC method is more attractive than MLE based on a quadratic approximation because the sample size of low flow at the site of interest is mostly not enough to perform the low flow frequency analysis. Copyright © 2007 John Wiley & Sons, Ltd. 相似文献
12.
Cheol-Eung Lee Sang Ug Kim Sangho Lee 《Stochastic Environmental Research and Risk Assessment (SERRA)》2014,28(3):639-654
Currently, an operational strategy for the maintenance of reservoirs is an important issue because of the reduction of reservoir storage from sedimentation. However, relatively few studies have addressed the reliability analysis including uncertainty on the decrease of the reservoir storage by the sedimentation. Therefore, it is necessary that the reduction of the reservoir storage by the sedimentation should be assessed by a probabilistic viewpoint because the natural uncertainty is embedded in the process of the sedimentation. The objective of this study is to advance the maintenance procedures, especially the assessment of future reservoir storage, using the time-dependent reliability analysis with the Bayesian approach. The stochastic gamma process is applied to estimate the reduction of the Soyang dam reservoir storage in South Korea. In estimating the parameters of the stochastic gamma process, the Bayesian Markov chain Monte Carlo (MCMC) scheme using the informative prior distribution through the empirical Bayes method is applied. The Metropolis–Hastings algorithm is constructed and its convergence is checked by the various diagnostics. The range of the expected life time of the Soyang dam reservoir by the Bayesian MCMC is estimated from 111 to 172 years at a 5 % significance level. Finally, it is suggested that improving the assessment strategy in this study can provide valuable information to the decision makers who are in charge of the maintenance of a reservoir or a dam. 相似文献
13.
Fausto Cupola Maria Giovanna Tanda Andrea Zanini 《Stochastic Environmental Research and Risk Assessment (SERRA)》2015,29(1):169-182
Inverse methods can be used to recover the pollutant source location from concentration data. In this paper, the relative effectiveness of two proposed methods, simultaneous release function and source location identification (SRSI) and backward probability model based on adjoint state method (BPM-ASM) are evaluated using real data collected by using experimental equipment. The device is a sandbox that reproduces an unconfined aquifer in which all the variables are controlled. A numerical model was calibrated using experimental observations. The SRSI is a stochastic procedure which finds the source location and the release history by means of a Bayesian geostatistical approach (GA). The BPM-ASM provides the backward probability location of the pollutant detected at a monitoring point by means of a reverse transport simulation. The results show that both methods perform well. While the simultaneous release function and SRSI method requires a preliminary delineation of a probable source area and some weak hypotheses about the statistical structure of the unknown release function, the backward probability model requires some hypothesis about the contaminant release time. A case study was performed using two observation points only, and despite the scarcity of data, both methodologies were able to accurately reconstruct the true source location. The GA has the advantage to recover the release history function too, whilst the backward probability model works well with fewer data. If there are many observations, both methodologies may be computationally heavy. A transfer function approach has been adopted for the numerical definition of the sensitivity matrix in the SRSI method. The reliability of the experimental equipment was tested in previous laboratory works, conducted under several different conditions. 相似文献
14.
Victor P. Yakushev Vladimir V. Karelin Vladimir M. Bure Elena M. Parilina 《Stochastic Environmental Research and Risk Assessment (SERRA)》2015,29(6):1671-1677
Problem of soil acidity regularization is modeled as stochastic adaptive control problem with a linear difference equation of the dynamics of a field pH level. Stochastic component in the equation represents an individual time variability of soil acidity of an elementary section. We use Bayesian approach to determine a posteriori probability density function of the unknown parameters of the stochastic transition process. The Kullback–Leibler information divergence is used as a measure of difference between true distribution and its estimation. Algorithm for the construction of an adaptive stabilizing control in such a linear control system is proposed in the paper. Numerical realization of the algorithm is represented for a problem of a field soil acidity control. 相似文献
15.
Evaggelos Ntotsios Costas Papadimitriou Panagiotis Panetsos Grigorios Karaiskos Kyriakos Perros Philip C. Perdikaris 《Bulletin of Earthquake Engineering》2009,7(2):469-483
A bridge health monitoring system is presented based on vibration measurements collected from a network of acceleration sensors.
Sophisticated structural identification methods, combining information from the sensor network with the theoretical information
built into a finite element model for simulating bridge behavior, are incorporated into the system in order to monitor structural
condition, track structural changes and identify the location, type and extent of damage. This work starts with a brief overview
of the modal and model identification algorithms and software incorporated into the monitoring system and then presents details
on a Bayesian inference framework for the identification of the location and the severity of damage using measured modal characteristics.
The methodology for damage detection combines the information contained in a set of measurement modal data with the information
provided by a family of competitive, parameterized, finite element model classes simulating plausible damage scenarios in
the structure. The effectiveness of the damage detection algorithm is demonstrated and validated using simulated modal data
from an instrumented R/C bridge of the Egnatia Odos motorway, as well as using experimental vibration data from a laboratory
small-scaled bridge section. 相似文献
16.
Increasing water demands, higher standards of living, depletion of resources of acceptable quality and excessive water pollution due to agricultural and industrial expansions have caused intense social and political predicaments, and conflicting issues among water consumers. The available techniques commonly used in reservoir optimization/operation do not consider interaction, behavior and preferences of water users, reservoir operator and their associated modeling procedures, within the stochastic modeling framework. In this paper, game theory is used to present the associated conflicts among different consumers due to limited water. Considering the game theory fundamentals, the Stochastic Dynamic Nash Game with perfect information (PSDNG) model is developed, which assumes that the decision maker has sufficient (perfect) information regarding the associated randomness of reservoir operation parameters. The simulated annealing approach (SA) is applied as a part of the proposed stochastic framework, which makes the PSDNG solution conceivable. As a case study, the proposed model is applied to the Zayandeh-Rud river basin in Iran with conflicting demands. The results are compared with alternative reservoir operation models, i.e., Bayesian stochastic dynamic programming (BSDP), sequential genetic algorithm (SGA) and classical dynamic programming regression (DPR). Results show that the proposed model has the ability to generate reservoir operating policies, considering interactions of water users, reservoir operator and their preferences. 相似文献
17.
Continuous remediation monitoring using sensors is potentially a more effective and inexpensive alternative to current methods of sample collection and analysis. Gaseous components of a system are the most mobile and easiest to monitor. Continuous monitoring of soil gases such as oxygen, carbon dioxide, and contaminant vapors can provide important quantitative information regarding the progress of bioremediation efforts and the area of influence of air sparging or soil venting. Laboratory and field tests of a commercially available oxygen sensor show that the subsurface oxygen sensor provides rapid and accurate data on vapor phase oxygen concentrations. The sensor is well suited for monitoring gas flow and oxygen consumption in the vadose zone during air sparging and bioventing. The sensor performs well in permeable, unsaturated soil environments and recovers completely after being submerged during temporary saturated conditions. Calibrations of the in situ oxygen sensors were found to be stable after one year of continuous subsurface operation. However, application of the sensor in saturated soil conditions is limited. The three major advantages of this sensor for in situ monitoring arc as follows: (1) it allows data acquisition at any specified time interval; (2) it provides potentially more accurate data by minimizing disturbance of subsurface conditions; and (3) it minimizes the cost of field and laboratory procedures involved in sample retrieval and analysis. 相似文献
18.
Sun AY 《Ground water》2008,46(4):638-641
Model-based contaminant source identification plays an important role in effective site remediation. In this article, a contaminant source identification toolbox (CONSID) is introduced as a framework for solving contaminant source identification problems. It is known that the presence of various types of model uncertainties can severely undermine the performance of many existing source estimators. The current version of CONSID consists of two robust estimators for recovering source release histories under model uncertainty; one was developed in the deterministic framework and the other in the stochastic framework. To use the robust estimators provided in CONSID, the user is required to have only modest prior knowledge about the model uncertainty and be able to estimate the bound of model deviations resulting from the uncertainty. The toolbox is designed so that other source estimators can be added easily. A step-by-step guidance for using CONSID is described and an example is provided. 相似文献
19.
Haidong Yang Dongguo Shao Biyu Liu Jianhua Huang Xianbao Ye 《Stochastic Environmental Research and Risk Assessment (SERRA)》2016,30(2):507-522
Sudden water pollution accidents in surface waters occur with increasing frequency. These accidents significantly threaten people’s health and lives. To prevent the diffusion of pollutants, identifying these pollution sources is necessary. The identification problem of pollution source, especially for multi-point source, is one of the difficulties in the inverse problem area. This study examines this issue. A new method is designed by combining differential evolution algorithm (DEA) and Metropolis–Hastings–Markov Chain Monte Carlo (MH–MCMC) based on Bayesian inference to identify multi-point sudden water pollution sources. The effectiveness and accuracy of this proposed method is verified through outdoor experiments and comparison between DEA and MH–MCMC. The average absolute error of the sources’ position and intensity, the relative error and the average standard deviations obtained using the proposed method are less than those of DEA and MH–MCMC. Moreover, the relative error and the sampling relative error under four different standard deviations of measurement error (σ = 0.01, 0.05, 0.1, 0.15) are less than 2 and 0.11 %, respectively. The proposed method (i.e., DEMH–MCMC) is effective even when the standard deviation of the measurement error increases to 0.15. Therefore, the proposed method can identify sources of multi-point sudden water pollution accidents efficiently and accurately. 相似文献
20.
Benjamin Renard Michel Lang Philippe Bois 《Stochastic Environmental Research and Risk Assessment (SERRA)》2006,21(2):97-112
Statistical analysis of extremes currently assumes that data arise from a stationary process, although such an hypothesis is not easily assessable and should therefore be considered as an uncertainty. The aim of this paper is to describe a Bayesian framework for this purpose, considering several probabilistic models (stationary, step-change and linear trend models) and four extreme values distributions (exponential, generalized Pareto, Gumbel and GEV). Prior distributions are specified by using regional prior knowledge about quantiles. Posterior distributions are used to estimate parameters, quantify the probability of models and derive a realistic frequency analysis, which takes into account estimation, distribution and stationarity uncertainties. MCMC methods are needed for this purpose, and are described in the article. Finally, an application to a POT discharge series is presented, with an analysis of both occurrence process and peak distribution. 相似文献