Modeling Uncertainty of Directed Movement via Markov Chains

Directory of Open Access Journals (Sweden)

YIN Zhangcai

2015-10-01

Full Text Available Probabilistic time geography (PTG is suggested as an extension of (classical time geography, in order to present the uncertainty of an agent located at the accessible position by probability. This may provide a quantitative basis for most likely finding an agent at a location. In recent years, PTG based on normal distribution or Brown bridge has been proposed, its variance, however, is irrelevant with the agent's speed or divergent with the increase of the speed; so they are difficult to take into account application pertinence and stability. In this paper, a new method is proposed to model PTG based on Markov chain. Firstly, a bidirectional conditions Markov chain is modeled, the limit of which, when the moving speed is large enough, can be regarded as the Brown bridge, thus has the characteristics of digital stability. Then, the directed movement is mapped to Markov chains. The essential part is to build step length, the state space and transfer matrix of Markov chain according to the space and time position of directional movement, movement speed information, to make sure the Markov chain related to the movement speed. Finally, calculating continuously the probability distribution of the directed movement at any time by the Markov chains, it can be get the possibility of an agent located at the accessible position. Experimental results show that, the variance based on Markov chains not only is related to speed, but also is tending towards stability with increasing the agent's maximum speed.

Robust environmental closed-loop supply chain design under uncertainty

International Nuclear Information System (INIS)

MA, Ruimin; YAO, Lifei; JIN, Maozhu; REN, Peiyu; LV, Zhihan

2016-01-01

With the fast developments in product remanufacturing to improve economic and environmental performance, an environmental closed-loop supply (ECLSC) chain is important for enterprises' competitiveness. In this paper, a robust ECLSC network is investigated which includes multiple plants, collection centers, demand zones, and products, and consists of both forward and reverse supply chains. First, a robust multi-objective mixed integer nonlinear programming model is proposed to deal with ECLSC considering two conflicting objectives simultaneously, as well as the uncertain nature of the supply chain. Cost parameters of the supply chain and demand fluctuations are subject to uncertainty. The first objective function aims to minimize the economical cost and the second objective function is to minimize the environmental influence. Then, the proposed model is solved as a single-objective mixed integer programming model applying the LP-metrics method. Finally, numerical example has been presented to test the model. The results indicate that the proposed model is applicable in practice.

Life Cycle Energy and CO2 Emission Optimization for Biofuel Supply Chain Planning under Uncertainties

DEFF Research Database (Denmark)

Ren, Jingzheng; An, Da; Liang, Hanwei

2016-01-01

The purpose of this paper is to develop a model for the decision-makers/stakeholders to design biofuel supply chain under uncertainties. Life cycle energy and CO2 emission of biofuel supply chain are employed as the objective functions, multiple feedstocks, multiple transportation modes, multiple...... sites for building biofuel plants, multiple technologies for biofuel production, and multiple markets for biofuel distribution are considered, and the amount of feedstocks in agricultural system, transportation capacities, yields of crops, and market demands are considered as uncertainty variables...... in this study. A bi-objective interval mix integer programming model has been developed for biofuel supply chain design under uncertainties, and the bio-objective interval programming method has been developed to solve this model. An illustrative case of a multiple-feedstock-bioethanol system has been studied...

Supply chain design under uncertainty for advanced biofuel production based on bio-oil gasification

International Nuclear Information System (INIS)

Li, Qi; Hu, Guiping

2014-01-01

An advanced biofuels supply chain is proposed to reduce biomass transportation costs and take advantage of the economics of scale for a gasification facility. In this supply chain, biomass is converted to bio-oil at widely distributed small-scale fast pyrolysis plants, and after bio-oil gasification, the syngas is upgraded to transportation fuels at a centralized biorefinery. A two-stage stochastic programming is formulated to maximize biofuel producers' annual profit considering uncertainties in the supply chain for this pathway. The first stage makes the capital investment decisions including the locations and capacities of the decentralized fast pyrolysis plants as well as the centralized biorefinery, while the second stage determines the biomass and biofuels flows. A case study based on Iowa in the U.S. illustrates that it is economically feasible to meet desired demand using corn stover as the biomass feedstock. The results show that the locations of fast pyrolysis plants are sensitive to uncertainties while the capacity levels are insensitive. The stochastic model outperforms the deterministic model in the stochastic environment, especially when there is insufficient biomass. Also, farmers' participation can have a significant impact on the profitability and robustness of this supply chain. - Highlights: • Decentralized supply chain design for advanced biofuel production is considered. • A two-stage stochastic programming is formulated to consider uncertainties. • Farmers' participation has a significant impact on the biofuel supply chain design

The development of a green supply chain dual-objective facility by considering different levels of uncertainty

Science.gov (United States)

Khorasani, Sasan Torabzadeh; Almasifard, Maryam

2017-11-01

This paper presents a dual-objective facility programming model for a green supply chain network. The main objectives of the presented model are minimizing overall expenditure and negative environmental impacts of the supply chain. This study contributes to the existing literature by incorporating uncertainty in customer demand, suppliers, production, and casting capacity. An industrial case study is also analyzed to reveal the feasibility of the proposed model and its application. A fuzzy approach which is known as TH is used to solve the suggested dual-objective model. TH approach is integration of a max-min method (LH) and modified version of Werners' approach (MW). The outcome of this study reveals that the presented model can support green supply chain network in different levels of uncertainty. In presented model, cost and negative environmental impacts derived from the supply chain network will increase of higher levels of uncertainty.

How risk and uncertainty is used in Supply Chain Management: a literature study

DEFF Research Database (Denmark)

Bøge Sørensen, Lars

2004-01-01

Keywords Supply Chain Management, Risk Management, Supply Chain Risk ManagementAbstract To comply with Supply Chain Management dogma companies have cut their inventoriesto a minimum, lead times have been shortened, new suppliers have been chosen and the customerportfolio has been reduced. All...... of these activities impose a great deal of risk on the firms,jeopardizing the survival of entire supply chains. In this article the author intends to investigateand document the use and meaning of Risk and Uncertainty within journals publishing material onSupply Chain Management and Logistics. Subsequently...... suggestions for further research areproposed - the integration of Risk Management into the discipline of Supply Chain Design....

Uncertainty assessment in geodetic network adjustment by combining GUM and Monte-Carlo-simulations

Science.gov (United States)

Niemeier, Wolfgang; Tengen, Dieter

2017-06-01

In this article first ideas are presented to extend the classical concept of geodetic network adjustment by introducing a new method for uncertainty assessment as two-step analysis. In the first step the raw data and possible influencing factors are analyzed using uncertainty modeling according to GUM (Guidelines to the Expression of Uncertainty in Measurements). This approach is well established in metrology, but rarely adapted within Geodesy. The second step consists of Monte-Carlo-Simulations (MC-simulations) for the complete processing chain from raw input data and pre-processing to adjustment computations and quality assessment. To perform these simulations, possible realizations of raw data and the influencing factors are generated, using probability distributions for all variables and the established concept of pseudo-random number generators. Final result is a point cloud which represents the uncertainty of the estimated coordinates; a confidence region can be assigned to these point clouds, as well. This concept may replace the common concept of variance propagation and the quality assessment of adjustment parameters by using their covariance matrix. It allows a new way for uncertainty assessment in accordance with the GUM concept for uncertainty modelling and propagation. As practical example the local tie network in "Metsähovi Fundamental Station", Finland is used, where classical geodetic observations are combined with GNSS data.

Stochastic production planning for a biofuel supply chain under demand and price uncertainties

International Nuclear Information System (INIS)

Awudu, Iddrisu; Zhang, Jun

2013-01-01

Highlights: ► The proposed stochastic model outperforms the deterministic model. ► The price of biofuel is modeled as Geometric Brownian Motion (GBM). ► The proposed model can be applied in any biofuel supply chain. -- Abstract: In this paper, we propose a stochastic production planning model for a biofuel supply chain under demand and price uncertainties. The supply chain consists of biomass suppliers, biofuel refinery plants and distribution centers. A stochastic linear programming model is proposed within a single-period planning framework to maximize the expected profit. Decisions such as the amount of raw materials purchased, the amount of raw materials consumed and the amount of products produced are considered. Demands of end products are uncertain with known probability distributions. The prices of end products follow Geometric Brownian Motion (GBM). Benders decomposition (BD) with Monte Carlo simulation technique is applied to solve the proposed model. To demonstrate the effectiveness of the proposed stochastic model and the decomposition algorithm, a representative supply chain for an ethanol plant in North Dakota is considered. To investigate the results of the proposed model, a simulation framework is developed to compare the performances of deterministic model and proposed stochastic model. The results from the simulation indicate the proposed model obtain higher expected profit than the deterministic model under different uncertainty settings. Sensitivity analyses are performed to gain management insight on how profit changes due to the uncertainties affect the model developed.

Sustainability Analysis and Buy-Back Coordination in a Fashion Supply Chain with Price Competition and Demand Uncertainty

Directory of Open Access Journals (Sweden)

Fan Wang

2016-12-01

Full Text Available Supply chain sustainability has become significantly important in the fashion industry, and more and more fashion brands have invested in developing sustainable supply chains. We note that dual channel system comprising a brand-owned direct channel and retail outsourcing channel is quite common in the fashion industry, and in the latter, buy-back contract is popular between brands and retailers. Therefore, we build a stylized dual channel model with price competition and demand uncertainty to characterize the main properties of a fashion supply chain. Our foci are the sustainability analysis and the channel coordination mechanism. We first design a buy-back contract with return cost to coordinate the channel. We then study supply chain sustainability and examine the effect of two key influencing factors, i.e., price competition and demand uncertainty. Interestingly, we find that a fiercer price competition will lead to a more sustainable supply chain. From the perspective of supply chain managers, we conclude that (1 if managers care about environmental sustainability, fierce price competition is not a suggested strategy; (2 if managers care about economic sustainability, fierce price competition is an advantageous strategy. We also find that high demand uncertainty results in a less sustainable supply chain, in both an environmental and economic sustainability sense.

Development of Shale Gas Supply Chain Network under Market Uncertainties

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Jorge Chebeir

2017-02-01

Full Text Available The increasing demand of energy has turned the shale gas and shale oil into one of the most promising sources of energy in the United States. In this article, a model is proposed to address the long-term planning problem of the shale gas supply chain under uncertain conditions. A two-stage stochastic programming model is proposed to describe and optimize the shale gas supply chain network. Inherent uncertainty in final products’ prices, such as natural gas and natural gas liquids (NGL, is treated through the utilization of a scenario-based method. A binomial option pricing model is utilized to approximate the stochastic process through the generation of scenario trees. The aim of the proposed model is to generate an appropriate and realistic supply chain network configuration as well as scheduling of different operations throughout the planning horizon of a shale gas development project.

Prediction uncertainty assessment of a systems biology model requires a sample of the full probability distribution of its parameters

Directory of Open Access Journals (Sweden)

Simon van Mourik

2014-06-01

Full Text Available Multi-parameter models in systems biology are typically ‘sloppy’: some parameters or combinations of parameters may be hard to estimate from data, whereas others are not. One might expect that parameter uncertainty automatically leads to uncertain predictions, but this is not the case. We illustrate this by showing that the prediction uncertainty of each of six sloppy models varies enormously among different predictions. Statistical approximations of parameter uncertainty may lead to dramatic errors in prediction uncertainty estimation. We argue that prediction uncertainty assessment must therefore be performed on a per-prediction basis using a full computational uncertainty analysis. In practice this is feasible by providing a model with a sample or ensemble representing the distribution of its parameters. Within a Bayesian framework, such a sample may be generated by a Markov Chain Monte Carlo (MCMC algorithm that infers the parameter distribution based on experimental data. Matlab code for generating the sample (with the Differential Evolution Markov Chain sampler and the subsequent uncertainty analysis using such a sample, is supplied as Supplemental Information.

Uncertainty quantification in flood risk assessment

Science.gov (United States)

Blöschl, Günter; Hall, Julia; Kiss, Andrea; Parajka, Juraj; Perdigão, Rui A. P.; Rogger, Magdalena; Salinas, José Luis; Viglione, Alberto

2017-04-01

Uncertainty is inherent to flood risk assessments because of the complexity of the human-water system, which is characterised by nonlinearities and interdependencies, because of limited knowledge about system properties and because of cognitive biases in human perception and decision-making. On top of the uncertainty associated with the assessment of the existing risk to extreme events, additional uncertainty arises because of temporal changes in the system due to climate change, modifications of the environment, population growth and the associated increase in assets. Novel risk assessment concepts are needed that take into account all these sources of uncertainty. They should be based on the understanding of how flood extremes are generated and how they change over time. They should also account for the dynamics of risk perception of decision makers and population in the floodplains. In this talk we discuss these novel risk assessment concepts through examples from Flood Frequency Hydrology, Socio-Hydrology and Predictions Under Change. We believe that uncertainty quantification in flood risk assessment should lead to a robust approach of integrated flood risk management aiming at enhancing resilience rather than searching for optimal defense strategies.

Entropic uncertainty for spin-1/2 XXX chains in the presence of inhomogeneous magnetic fields and its steering via weak measurement reversals

Science.gov (United States)

Wang, Dong; Ming, Fei; Huang, Ai-Jun; Sun, Wen-Yang; Ye, Liu

2017-09-01

The uncertainty principle configures a low bound to the measuring precision for a pair of non-commuting observables, and hence is considerably nontrivial to quantum precision measurement in the field of quantum information theory. In this letter, we consider the entropic uncertainty relation (EUR) in the context of quantum memory in a two-qubit isotropic Heisenberg spin chain. Specifically, we explore the dynamics of EUR in a practical scenario, where two associated nodes of a one-dimensional XXX-spin chain, under an inhomogeneous magnetic field, are connected to a thermal entanglement. We show that the temperature and magnetic field effect can lead to the inflation of the measuring uncertainty, stemming from the reduction of systematic quantum correlation. Notably, we reveal that, firstly, the uncertainty is not fully dependent on the observed quantum correlation of the system; secondly, the dynamical behaviors of the measuring uncertainty are relatively distinct with respect to ferromagnetism and antiferromagnetism chains. Meanwhile, we deduce that the measuring uncertainty is dramatically correlated with the mixedness of the system, implying that smaller mixedness tends to reduce the uncertainty. Furthermore, we propose an effective strategy to control the uncertainty of interest by means of quantum weak measurement reversal. Therefore, our work may shed light on the dynamics of the measuring uncertainty in the Heisenberg spin chain, and thus be important to quantum precision measurement in various solid-state systems.

Life cycle cost optimization of biofuel supply chains under uncertainties based on interval linear programming.

Science.gov (United States)

Ren, Jingzheng; Dong, Liang; Sun, Lu; Goodsite, Michael Evan; Tan, Shiyu; Dong, Lichun

2015-01-01

The aim of this work was to develop a model for optimizing the life cycle cost of biofuel supply chain under uncertainties. Multiple agriculture zones, multiple transportation modes for the transport of grain and biofuel, multiple biofuel plants, and multiple market centers were considered in this model, and the price of the resources, the yield of grain and the market demands were regarded as interval numbers instead of constants. An interval linear programming was developed, and a method for solving interval linear programming was presented. An illustrative case was studied by the proposed model, and the results showed that the proposed model is feasible for designing biofuel supply chain under uncertainties. Copyright © 2015 Elsevier Ltd. All rights reserved.

An Automation System for Optimizing a Supply Chain Network Design under the Influence of Demand Uncertainty

OpenAIRE

Polany, Rany

2012-01-01

This research develops and applies an integrated hierarchical framework for modeling a multi-echelon supply chain network design, under the influence of demand uncertainty. The framework is a layered integration of two levels: macro, high-level scenario planning combined with micro, low-level Monte Carlo simulation of uncertainties in demand. To facilitate rapid simulation of the effects of demand uncertainty, the integrated framework was implemented as a dashboard automation system using Mic...

Uncertainty in ecological risk assessment: A statistician's view

International Nuclear Information System (INIS)

Smith, E.P.

1995-01-01

Uncertainty is a topic that has different meanings to researchers, modelers, managers and policy makers. The perspective of this presentation will be on the modeling view of uncertainty and its quantitative assessment. The goal is to provide some insight into how a statistician visualizes and addresses the issue of uncertainty in ecological risk assessment problems. In ecological risk assessment, uncertainty arises from many sources and is of different type depending on what is studies, where it is studied and how it is studied. Some major sources and their impact are described. A variety of quantitative approaches to modeling uncertainty are characterized and a general taxonomy given. Examples of risk assessments of lake acidification, power plant impact assessment and the setting of standards for chemicals will be used discuss approaches to quantitative assessment of uncertainty and some of the potential difficulties

A location-inventory model for distribution centers in a three-level supply chain under uncertainty

Directory of Open Access Journals (Sweden)

Ali Bozorgi-Amiri

2013-01-01

Full Text Available We study a location-inventory problem in a three level supply chain network under uncertainty, which leads to risk. The (r,Q inventory control policy is applied for this problem. Besides, uncertainty exists in different parameters such as procurement, transportation costs, supply, demand and the capacity of different facilities (due to disaster, man-made events and etc. We present a robust optimization model, which concurrently specifies: locations of distribution centers to be opened, inventory control parameters (r,Q, and allocation of supply chain components. The model is formulated as a multi-objective mixed-integer nonlinear programming in order to minimize the expected total cost of such a supply chain network comprising location, procurement, transportation, holding, ordering, and shortage costs. Moreover, we develop an effective solution approach on the basis of multi-objective particle swarm optimization for solving the proposed model. Eventually, computational results of different examples of the problem and sensitivity analysis are exhibited to show the model and algorithm's feasibility and efficiency.

Uncertainties in radioecological assessment models

International Nuclear Information System (INIS)

Hoffman, F.O.; Miller, C.W.; Ng, Y.C.

1983-01-01

Environmental radiological assessments rely heavily on the use of mathematical models. The predictions of these models are inherently uncertain because models are inexact representations of real systems. The major sources of this uncertainty are related to bias in model formulation and imprecision in parameter estimation. The magnitude of uncertainty is a function of the questions asked of the model and the specific radionuclides and exposure pathways of dominant importance. It is concluded that models developed as research tools should be distinguished from models developed for assessment applications. Furthermore, increased model complexity does not necessarily guarantee increased accuracy. To improve the realism of assessment modeling, stochastic procedures are recommended that translate uncertain parameter estimates into a distribution of predicted values. These procedures also permit the importance of model parameters to be ranked according to their relative contribution to the overall predicted uncertainty. Although confidence in model predictions can be improved through site-specific parameter estimation and increased model validation, health risk factors and internal dosimetry models will probably remain important contributors to the amount of uncertainty that is irreducible. 41 references, 4 figures, 4 tables

Risk assessments of lumpy skin diseases in Borena bull market chain and its implication for livelihoods and international trade.

Science.gov (United States)

Alemayehu, Gezahegn; Zewde, Girma; Admassu, Berhanu

2013-06-01

Risks of introduction of lumpy skin disease (LSD) through traded Borena bulls to market chain and its consequences were assessed. The assessment used the framework that has been recommended by the World Animal Health Organization (OIE) for risk analysis. Likelihoods for release and exposure were estimated by a qualitative scale ranging from negligible to very high, whereas the consequences which resulted from disease occurrences were assessed quantitatively. The likelihood of the introduction of LSD to the market chain through traded Borena bulls is found to be high (medium uncertainty), whereas the probability of exposure is very high (medium uncertainty). From the total of 11,189 bulls observed during outbreak investigation of LSD in six sites of feedlot operation in and around Adama, 681(6.1 %) and 204 (1.8 %) bulls were found to be affected and dead with LSD, respectively. The total economic loss due to LSD was estimated to be 667,785.6 USD. The risk estimates for LSD are greater than negligible; therefore, disease prevention and control strategy along the chain should be carefully considered by the Ethiopian veterinary services.

The role of uncertainty in supply chains under dynamic modeling

Directory of Open Access Journals (Sweden)

M. Fera

2017-01-01

Full Text Available The uncertainty in the supply chains (SCs for manufacturing and services firms is going to be, over the coming decades, more important for the companies that are called to compete in a new globalized economy. Risky situations for manufacturing are considered in trying to individuate the optimal positioning of the order penetration point (OPP. It aims at defining the best level of information of the client’s order going back through the several supply chain (SC phases, i.e. engineering, procurement, production and distribution. This work aims at defining a system dynamics model to assess competitiveness coming from the positioning of the order in different SC locations. A Taguchi analysis has been implemented to create a decision map for identifying possible strategic decisions under different scenarios and with alternatives for order location in the SC levels. Centralized and decentralized strategies for SC integration are discussed. In the model proposed, the location of OPP is influenced by the demand variation, production time, stock-outs and stock amount. Results of this research are as follows: (i customer-oriented strategies are preferable under high volatility of demand, (ii production-focused strategies are suggested when the probability of stock-outs is high, (iii no specific location is preferable if a centralized control architecture is implemented, (iv centralization requires cooperation among partners to achieve the SC optimum point, (v the producer must not prefer the OPP location at the Retailer level when the general strategy is focused on a decentralized approach.

Review of uncertainty estimates associated with models for assessing the impact of breeder reactor radioactivity releases

International Nuclear Information System (INIS)

Miller, C.; Little, C.A.

1982-08-01

The purpose is to summarize estimates based on currently available data of the uncertainty associated with radiological assessment models. The models being examined herein are those recommended previously for use in breeder reactor assessments. Uncertainty estimates are presented for models of atmospheric and hydrologic transport, terrestrial and aquatic food-chain bioaccumulation, and internal and external dosimetry. Both long-term and short-term release conditions are discussed. The uncertainty estimates presented in this report indicate that, for many sites, generic models and representative parameter values may be used to calculate doses from annual average radionuclide releases when these calculated doses are on the order of one-tenth or less of a relevant dose limit. For short-term, accidental releases, especially those from breeder reactors located in sites dominated by complex terrain and/or coastal meteorology, the uncertainty in the dose calculations may be much larger than an order of magnitude. As a result, it may be necessary to incorporate site-specific information into the dose calculation under these circumstances to reduce this uncertainty. However, even using site-specific information, natural variability and the uncertainties in the dose conversion factor will likely result in an overall uncertainty of greater than an order of magnitude for predictions of dose or concentration in environmental media following shortterm releases

Uncertainty analysis in safety assessment

International Nuclear Information System (INIS)

Lemos, Francisco Luiz de; Sullivan, Terry

1997-01-01

Nuclear waste disposal is a very complex subject which requires the study of many different fields of science, like hydro geology, meteorology, geochemistry, etc. In addition, the waste disposal facilities are designed to last for a very long period of time. Both of these conditions make safety assessment projections filled with uncertainty. This paper addresses approaches for treatment of uncertainties in the safety assessment modeling due to the variability of data and some current approaches used to deal with this problem. (author)

A location-inventory model for distribution centers in a three-level supply chain under uncertainty

OpenAIRE

Ali Bozorgi-Amiri; M. Saeed Jabalameli; Sara Gharegozloo Hamedani

2013-01-01

We study a location-inventory problem in a three level supply chain network under uncertainty, which leads to risk. The (r,Q) inventory control policy is applied for this problem. Besides, uncertainty exists in different parameters such as procurement, transportation costs, supply, demand and the capacity of different facilities (due to disaster, man-made events and etc). We present a robust optimization model, which concurrently specifies: locations of distribution centers to be opened, inve...

Uncertainties in risk assessment and decision making

International Nuclear Information System (INIS)

Starzec, Peter; Purucker, Tom; Stewart, Robert

2008-02-01

The general concept for risk assessment in accordance with the Swedish model for contaminated soil implies that the toxicological reference value for a given receptor is first back-calculated to a corresponding concentration of a compound in soil and (if applicable) then modified with respect to e.g. background levels, acute toxicity, and factor of safety. This result in a guideline value that is subsequently compared to the observed concentration levels. Many sources of uncertainty exist when assessing whether the risk for a receptor is significant or not. In this study, the uncertainty aspects have been addressed from three standpoints: 1. Uncertainty in the comparison between the level of contamination (source) and a given risk criterion (e.g. a guideline value) and possible implications on subsequent decisions. This type of uncertainty is considered to be most important in situations where a contaminant is expected to be spatially heterogeneous without any tendency to form isolated clusters (hotspots) that can be easily delineated, i.e. where mean values are appropriate to compare to the risk criterion. 2. Uncertainty in spatial distribution of a contaminant. Spatial uncertainty should be accounted for when hotspots are to be delineated and the volume of soil contaminated with levels above a stated decision criterion has to be assessed (quantified). 3. Uncertainty in an ecological exposure model with regard to the moving pattern of a receptor in relation to spatial distribution of contaminant in question. The study points out that the choice of methodology to characterize the relation between contaminant concentration and a pre-defined risk criterion is governed by a conceptual perception of the contaminant's spatial distribution and also depends on the structure of collected data (observations). How uncertainty in transition from contaminant concentration into risk criterion can be quantified was demonstrated by applying hypothesis tests and the concept of

Uncertainty analysis of environmental models

International Nuclear Information System (INIS)

Monte, L.

1990-01-01

In the present paper an evaluation of the output uncertainty of an environmental model for assessing the transfer of 137 Cs and 131 I in the human food chain are carried out on the basis of a statistical analysis of data reported by the literature. The uncertainty analysis offers the oppotunity of obtaining some remarkable information about the uncertainty of models predicting the migration of non radioactive substances in the environment mainly in relation to the dry and wet deposition

Sources of uncertainty in hydrological climate impact assessment: a cross-scale study

Science.gov (United States)

Hattermann, F. F.; Vetter, T.; Breuer, L.; Su, Buda; Daggupati, P.; Donnelly, C.; Fekete, B.; Flörke, F.; Gosling, S. N.; Hoffmann, P.; Liersch, S.; Masaki, Y.; Motovilov, Y.; Müller, C.; Samaniego, L.; Stacke, T.; Wada, Y.; Yang, T.; Krysnaova, V.

2018-01-01

Climate change impacts on water availability and hydrological extremes are major concerns as regards the Sustainable Development Goals. Impacts on hydrology are normally investigated as part of a modelling chain, in which climate projections from multiple climate models are used as inputs to multiple impact models, under different greenhouse gas emissions scenarios, which result in different amounts of global temperature rise. While the goal is generally to investigate the relevance of changes in climate for the water cycle, water resources or hydrological extremes, it is often the case that variations in other components of the model chain obscure the effect of climate scenario variation. This is particularly important when assessing the impacts of relatively lower magnitudes of global warming, such as those associated with the aspirational goals of the Paris Agreement. In our study, we use ANOVA (analyses of variance) to allocate and quantify the main sources of uncertainty in the hydrological impact modelling chain. In turn we determine the statistical significance of different sources of uncertainty. We achieve this by using a set of five climate models and up to 13 hydrological models, for nine large scale river basins across the globe, under four emissions scenarios. The impact variable we consider in our analysis is daily river discharge. We analyze overall water availability and flow regime, including seasonality, high flows and low flows. Scaling effects are investigated by separately looking at discharge generated by global and regional hydrological models respectively. Finally, we compare our results with other recently published studies. We find that small differences in global temperature rise associated with some emissions scenarios have mostly significant impacts on river discharge—however, climate model related uncertainty is so large that it obscures the sensitivity of the hydrological system.

Coordinating a Supply Chain with a Loss-Averse Retailer under Yield and Demand Uncertainties

Directory of Open Access Journals (Sweden)

Weiwei Luo

2016-01-01

Full Text Available This paper investigates the channel coordination of a supply chain (SC consisting of a loss-averse retailer and a risk-neutral supplier under yield and demand uncertainties. Three existing contracts are analyzed. Our results demonstrate that the buyback (BB and quantity flexibility (QF contracts can not only coordinate the supply chain but also lead to Pareto improvement for each player, while the wholesale price (WP contract fails to coordinate the chain due to the effects of double marginalization and risk preference. For comparison, a chain with a risk-neutral retailer is also analyzed. Furthermore, numerical examples are provided to demonstrate the effectiveness of the coordination contracts, and the impacts of loss aversion and random yield on the decision-making behaviors and system performance are then discussed.

Uncertainty analysis in safety assessment

Energy Technology Data Exchange (ETDEWEB)

Lemos, Francisco Luiz de [Centro de Desenvolvimento da Tecnologia Nuclear (CDTN), Belo Horizonte, MG (Brazil); Sullivan, Terry [Brookhaven National Lab., Upton, NY (United States)

1997-12-31

Nuclear waste disposal is a very complex subject which requires the study of many different fields of science, like hydro geology, meteorology, geochemistry, etc. In addition, the waste disposal facilities are designed to last for a very long period of time. Both of these conditions make safety assessment projections filled with uncertainty. This paper addresses approaches for treatment of uncertainties in the safety assessment modeling due to the variability of data and some current approaches used to deal with this problem. (author) 13 refs.; e-mail: lemos at bnl.gov; sulliva1 at bnl.gov

Model uncertainty in safety assessment

International Nuclear Information System (INIS)

Pulkkinen, U.; Huovinen, T.

1996-01-01

The uncertainty analyses are an essential part of any risk assessment. Usually the uncertainties of reliability model parameter values are described by probability distributions and the uncertainty is propagated through the whole risk model. In addition to the parameter uncertainties, the assumptions behind the risk models may be based on insufficient experimental observations and the models themselves may not be exact descriptions of the phenomena under analysis. The description and quantification of this type of uncertainty, model uncertainty, is the topic of this report. The model uncertainty is characterized and some approaches to model and quantify it are discussed. The emphasis is on so called mixture models, which have been applied in PSAs. Some of the possible disadvantages of the mixture model are addressed. In addition to quantitative analyses, also qualitative analysis is discussed shortly. To illustrate the models, two simple case studies on failure intensity and human error modeling are described. In both examples, the analysis is based on simple mixture models, which are observed to apply in PSA analyses. (orig.) (36 refs., 6 figs., 2 tabs.)

Model uncertainty in safety assessment

Energy Technology Data Exchange (ETDEWEB)

Pulkkinen, U; Huovinen, T [VTT Automation, Espoo (Finland). Industrial Automation

1996-01-01

The uncertainty analyses are an essential part of any risk assessment. Usually the uncertainties of reliability model parameter values are described by probability distributions and the uncertainty is propagated through the whole risk model. In addition to the parameter uncertainties, the assumptions behind the risk models may be based on insufficient experimental observations and the models themselves may not be exact descriptions of the phenomena under analysis. The description and quantification of this type of uncertainty, model uncertainty, is the topic of this report. The model uncertainty is characterized and some approaches to model and quantify it are discussed. The emphasis is on so called mixture models, which have been applied in PSAs. Some of the possible disadvantages of the mixture model are addressed. In addition to quantitative analyses, also qualitative analysis is discussed shortly. To illustrate the models, two simple case studies on failure intensity and human error modeling are described. In both examples, the analysis is based on simple mixture models, which are observed to apply in PSA analyses. (orig.) (36 refs., 6 figs., 2 tabs.).

Estimating U.S. Methane Emissions from the Natural Gas Supply Chain. Approaches, Uncertainties, Current Estimates, and Future Studies

Energy Technology Data Exchange (ETDEWEB)

Heath, Garvin [Joint Inst. for Strategic Energy Analysis, Golden, CO (United States); Warner, Ethan [Joint Inst. for Strategic Energy Analysis, Golden, CO (United States); Steinberg, Daniel [Joint Inst. for Strategic Energy Analysis, Golden, CO (United States); Brandt, Adam [Stanford Univ., CA (United States)

2015-08-01

A growing number of studies have raised questions regarding uncertainties in our understanding of methane (CH₄) emissions from fugitives and venting along the natural gas (NG) supply chain. In particular, a number of measurement studies have suggested that actual levels of CH₄ emissions may be higher than estimated by EPA" tm s U.S. GHG Emission Inventory. We reviewed the literature to identify the growing number of studies that have raised questions regarding uncertainties in our understanding of methane (CH₄) emissions from fugitives and venting along the natural gas (NG) supply chain.

Treatment of uncertainty in low-level waste performance assessment

International Nuclear Information System (INIS)

Kozak, M.W.; Olague, N.E.; Gallegos, D.P.; Rao, R.R.

1991-01-01

Uncertainties arise from a number of different sources in low-level waste performance assessment. In this paper the types of uncertainty are reviewed, and existing methods for quantifying and reducing each type of uncertainty are discussed. These approaches are examined in the context of the current low-level radioactive waste regulatory performance objectives, which are deterministic. The types of uncertainty discussed in this paper are model uncertainty, uncertainty about future conditions, and parameter uncertainty. The advantages and disadvantages of available methods for addressing uncertainty in low-level waste performance assessment are presented. 25 refs

Risk Assessment Uncertainties in Cybersecurity Investments

Directory of Open Access Journals (Sweden)

Andrew Fielder

2018-06-01

Full Text Available When undertaking cybersecurity risk assessments, it is important to be able to assign numeric values to metrics to compute the final expected loss that represents the risk that an organization is exposed to due to cyber threats. Even if risk assessment is motivated by real-world observations and data, there is always a high chance of assigning inaccurate values due to different uncertainties involved (e.g., evolving threat landscape, human errors and the natural difficulty of quantifying risk. Existing models empower organizations to compute optimal cybersecurity strategies given their financial constraints, i.e., available cybersecurity budget. Further, a general game-theoretic model with uncertain payoffs (probability-distribution-valued payoffs shows that such uncertainty can be incorporated in the game-theoretic model by allowing payoffs to be random. This paper extends previous work in the field to tackle uncertainties in risk assessment that affect cybersecurity investments. The findings from simulated examples indicate that although uncertainties in cybersecurity risk assessment lead, on average, to different cybersecurity strategies, they do not play a significant role in the final expected loss of the organization when utilising a game-theoretic model and methodology to derive these strategies. The model determines robust defending strategies even when knowledge regarding risk assessment values is not accurate. As a result, it is possible to show that the cybersecurity investments’ tool is capable of providing effective decision support.

Stochastic optimization of a multi-feedstock lignocellulosic-based bioethanol supply chain under multiple uncertainties

International Nuclear Information System (INIS)

Osmani, Atif; Zhang, Jun

2013-01-01

An integrated multi-feedstock (i.e. switchgrass and crop residue) lignocellulosic-based bioethanol supply chain is studied under jointly occurring uncertainties in switchgrass yield, crop residue purchase price, bioethanol demand and sales price. A two-stage stochastic mathematical model is proposed to maximize expected profit by optimizing the strategic and tactical decisions. A case study based on ND (North Dakota) state in the U.S. demonstrates that in a stochastic environment it is cost effective to meet 100% of ND's annual gasoline demand from bioethanol by using switchgrass as a primary and crop residue as a secondary biomass feedstock. Although results show that the financial performance is degraded as variability of the uncertain parameters increases, the proposed stochastic model increasingly outperforms the deterministic model under uncertainties. The locations of biorefineries (i.e. first-stage integer variables) are insensitive to the uncertainties. Sensitivity analysis shows that “mean” value of stochastic parameters has a significant impact on the expected profit and optimal values of first-stage continuous variables. Increase in level of mean ethanol demand and mean sale price results in higher bioethanol production. When mean switchgrass yield is at low level and mean crop residue price is at high level, all the available marginal land is used for switchgrass cultivation. - Highlights: • Two-stage stochastic MILP model for maximizing profit of a multi-feedstock lignocellulosic-based bioethanol supply chain. • Multiple uncertainties in switchgrass yield, crop residue purchase price, bioethanol demand, and bioethanol sale price. • Proposed stochastic model outperforms the traditional deterministic model under uncertainties. • Stochastic parameters significantly affect marginal land allocation for switchgrass cultivation and bioethanol production. • Location of biorefineries is found to be insensitive to the stochastic environment

Dealing with uncertainties in environmental burden of disease assessment

Directory of Open Access Journals (Sweden)

van der Sluijs Jeroen P

2009-04-01

Full Text Available Abstract Disability Adjusted Life Years (DALYs combine the number of people affected by disease or mortality in a population and the duration and severity of their condition into one number. The environmental burden of disease is the number of DALYs that can be attributed to environmental factors. Environmental burden of disease estimates enable policy makers to evaluate, compare and prioritize dissimilar environmental health problems or interventions. These estimates often have various uncertainties and assumptions which are not always made explicit. Besides statistical uncertainty in input data and parameters – which is commonly addressed – a variety of other types of uncertainties may substantially influence the results of the assessment. We have reviewed how different types of uncertainties affect environmental burden of disease assessments, and we give suggestions as to how researchers could address these uncertainties. We propose the use of an uncertainty typology to identify and characterize uncertainties. Finally, we argue that uncertainties need to be identified, assessed, reported and interpreted in order for assessment results to adequately support decision making.

Statistically based uncertainty assessments in nuclear risk analysis

International Nuclear Information System (INIS)

Spencer, F.W.; Diegert, K.V.; Easterling, R.G.

1987-01-01

Over the last decade, the problems of estimation and uncertainty assessment in probabilistics risk assessment (PRAs) have been addressed in a variety of NRC and industry-sponsored projects. These problems have received attention because of a recognition that major uncertainties in risk estimation exist, which can be reduced by collecting more and better data and other information, and because of a recognition that better methods for assessing these uncertainties are needed. In particular, a clear understanding of the nature and magnitude of various sources of uncertainty is needed to facilitate descision-making on possible plant changes and research options. Recent PRAs have employed methods of probability propagation, sometimes involving the use of Bayes Theorem, and intended to formalize the use of ''engineering judgment'' or ''expert opinion.'' All sources, or feelings, of uncertainty are expressed probabilistically, so that uncertainty analysis becomes simply a matter of probability propagation. Alternatives to forcing a probabilistic framework at all stages of a PRA are a major concern in this paper, however

Critical loads - assessment of uncertainty

Energy Technology Data Exchange (ETDEWEB)

Barkman, A.

1998-10-01

The effects of data uncertainty in applications of the critical loads concept were investigated on different spatial resolutions in Sweden and northern Czech Republic. Critical loads of acidity (CL) were calculated for Sweden using the biogeochemical model PROFILE. Three methods with different structural complexity were used to estimate the adverse effects of S0{sub 2} concentrations in northern Czech Republic. Data uncertainties in the calculated critical loads/levels and exceedances (EX) were assessed using Monte Carlo simulations. Uncertainties within cumulative distribution functions (CDF) were aggregated by accounting for the overlap between site specific confidence intervals. Aggregation of data uncertainties within CDFs resulted in lower CL and higher EX best estimates in comparison with percentiles represented by individual sites. Data uncertainties were consequently found to advocate larger deposition reductions to achieve non-exceedance based on low critical loads estimates on 150 x 150 km resolution. Input data were found to impair the level of differentiation between geographical units at all investigated resolutions. Aggregation of data uncertainty within CDFs involved more constrained confidence intervals for a given percentile. Differentiation as well as identification of grid cells on 150 x 150 km resolution subjected to EX was generally improved. Calculation of the probability of EX was shown to preserve the possibility to differentiate between geographical units. Re-aggregation of the 95%-ile EX on 50 x 50 km resolution generally increased the confidence interval for each percentile. Significant relationships were found between forest decline and the three methods addressing risks induced by S0{sub 2} concentrations. Modifying S0{sub 2} concentrations by accounting for the length of the vegetation period was found to constitute the most useful trade-off between structural complexity, data availability and effects of data uncertainty. Data

Assessing scenario and parametric uncertainties in risk analysis: a model uncertainty audit

International Nuclear Information System (INIS)

Tarantola, S.; Saltelli, A.; Draper, D.

1999-01-01

In the present study a process of model audit is addressed on a computational model used for predicting maximum radiological doses to humans in the field of nuclear waste disposal. Global uncertainty and sensitivity analyses are employed to assess output uncertainty and to quantify the contribution of parametric and scenario uncertainties to the model output. These tools are of fundamental importance for risk analysis and decision making purposes

Optimal design and planning of glycerol-based biorefinery supply chains under uncertainty

DEFF Research Database (Denmark)

Loureiro da Costa Lira Gargalo, Carina; Carvalho, Ana; Gernaey, Krist V.

2017-01-01

-echelon mixed integer linear programming problem is proposed based upon a previous model, GlyThink. In the new formulation, market uncertainties are taken into account at the strategic planning level. The robustness of the supply chain structures is analyzed based on statistical data provided...... by the implementation of the Monte Carlo method, where a deterministic optimization problem is solved for each scenario. Furthermore, the solution of the stochastic multi-objective optimization model, points to the Pareto set of trade-off solutions obtained when maximizing the NPV and minimizing environmental......The optimal design and planning of glycerol-based biorefinery supply chains is critical for the development and implementation of this concept in a sustainable manner. To achieve this, a decision-making framework is proposed in this work, to holistically optimize the design and planning...

Hybrid Electromagnetism-Like Algorithm for Dynamic Supply Chain Network Design under Traffic Congestion and Uncertainty

Directory of Open Access Journals (Sweden)

Javid Jouzdani

2016-01-01

Full Text Available With the constantly increasing pressure of the competitive environment, supply chain (SC decision makers are forced to consider several aspects of business climate. More specifically, they should take into account the endogenous features (e.g., available means of transportation, and the variety of products and exogenous criteria (e.g., the environmental uncertainty, and transportation system conditions. In this paper, a mixed integer nonlinear programming (MINLP model for dynamic design of a supply chain network is proposed. In this model, multiple products and multiple transportation modes, the time value of money, traffic congestion, and both supply-side and demand-side uncertainties are considered. Due to the complexity of such models, conventional solution methods are not applicable; therefore, two hybrid Electromagnetism-Like Algorithms (EMA are designed and discussed for tackling the problem. The numerical results show the applicability of the proposed model and the capabilities of the solution approaches to the MINLP problem.

Assessment of SFR Wire Wrap Simulation Uncertainties

Energy Technology Data Exchange (ETDEWEB)

Delchini, Marc-Olivier G. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Reactor and Nuclear Systems Division; Popov, Emilian L. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Reactor and Nuclear Systems Division; Pointer, William David [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Reactor and Nuclear Systems Division; Swiler, Laura P. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2016-09-30

Predictive modeling and simulation of nuclear reactor performance and fuel are challenging due to the large number of coupled physical phenomena that must be addressed. Models that will be used for design or operational decisions must be analyzed for uncertainty to ascertain impacts to safety or performance. Rigorous, structured uncertainty analyses are performed by characterizing the model’s input uncertainties and then propagating the uncertainties through the model to estimate output uncertainty. This project is part of the ongoing effort to assess modeling uncertainty in Nek5000 simulations of flow configurations relevant to the advanced reactor applications of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. Three geometries are under investigation in these preliminary assessments: a 3-D pipe, a 3-D 7-pin bundle, and a single pin from the Thermal-Hydraulic Out-of-Reactor Safety (THORS) facility. Initial efforts have focused on gaining an understanding of Nek5000 modeling options and integrating Nek5000 with Dakota. These tasks are being accomplished by demonstrating the use of Dakota to assess parametric uncertainties in a simple pipe flow problem. This problem is used to optimize performance of the uncertainty quantification strategy and to estimate computational requirements for assessments of complex geometries. A sensitivity analysis to three turbulent models was conducted for a turbulent flow in a single wire wrapped pin (THOR) geometry. Section 2 briefly describes the software tools used in this study and provides appropriate references. Section 3 presents the coupling interface between Dakota and a computational fluid dynamic (CFD) code (Nek5000 or STARCCM+), with details on the workflow, the scripts used for setting up the run, and the scripts used for post-processing the output files. In Section 4, the meshing methods used to generate the THORS and 7-pin bundle meshes are explained. Sections 5, 6 and 7 present numerical results

Contracting for Competitive Supply Chains under Network Externalities and Demand Uncertainty

Directory of Open Access Journals (Sweden)

Xiaojing Liu

2016-01-01

Full Text Available Based on network externalities and demand uncertainty environment, supply chain competition model is built; we identify the valid mechanism for the alternative range of profit-sharing contracts and also analyze the effect of product substitutability coefficient and network externalities on the alliance and profit-sharing contract. The results show that the vertical alliance contributes profit improvement to both the manufacturer and the retailer when the impact of network externalities on the product substitutability is not strong. However, vertical alliance will be out of operation when the effect of network externalities on the product substitutability is strong.

Where do uncertainties reside within environmental risk assessments? Expert opinion on uncertainty distributions for pesticide risks to surface water organisms.

Science.gov (United States)

Skinner, Daniel J C; Rocks, Sophie A; Pollard, Simon J T

2016-12-01

A reliable characterisation of uncertainties can aid uncertainty identification during environmental risk assessments (ERAs). However, typologies can be implemented inconsistently, causing uncertainties to go unidentified. We present an approach based on nine structured elicitations, in which subject-matter experts, for pesticide risks to surface water organisms, validate and assess three dimensions of uncertainty: its level (the severity of uncertainty, ranging from determinism to ignorance); nature (whether the uncertainty is epistemic or aleatory); and location (the data source or area in which the uncertainty arises). Risk characterisation contains the highest median levels of uncertainty, associated with estimating, aggregating and evaluating the magnitude of risks. Regarding the locations in which uncertainty is manifest, data uncertainty is dominant in problem formulation, exposure assessment and effects assessment. The comprehensive description of uncertainty described will enable risk analysts to prioritise the required phases, groups of tasks, or individual tasks within a risk analysis according to the highest levels of uncertainty, the potential for uncertainty to be reduced or quantified, or the types of location-based uncertainty, thus aiding uncertainty prioritisation during environmental risk assessments. In turn, it is expected to inform investment in uncertainty reduction or targeted risk management action. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Uncertainty Assessment in Urban Storm Water Drainage Modelling

DEFF Research Database (Denmark)

Thorndahl, Søren

The object of this paper is to make an overall description of the author's PhD study, concerning uncertainties in numerical urban storm water drainage models. Initially an uncertainty localization and assessment of model inputs and parameters as well as uncertainties caused by different model...

Adapting transport modes to supply chains classified by the uncertainty supply chain model: A case study at Manaus Industrial Pole

Directory of Open Access Journals (Sweden)

Fabiana Lucena Oliveira

2017-01-01

Full Text Available This paper discusses transport modes supporting Uncertainty Supply Chain Model (USCM in the case of Manaus Industrial Pole (PIM, an industrial cluster in the Brazilian Amazon that hosts six hundred factories with diverse logistics and supply chain managerial strategies. USCM (Lee, 2002; Fisher, 1997develops a dot matrix classification of the supply chains considering several attributes (e.g., agility, cost, security, responsiveness and argues that emergent economies industrial clusters, in the effort to keep attractiveness for technological frontier firms, need to adapt supply chain strategies according to USCM attributes. The paper takes a further step, discussing which transport modes are suitable to each supply chain classified at the USCM in PIM´s case. The research´s methods covered the use of PIM´s statistical official database (secondary data, interviews with the main logistical services providers of PIM and phone survey with a sample of firms (primary data. Findings confirm the theoretical argument that different supply chains will demand different transport modes running at the same time in the same industrial cluster (Oliveira, 2009. In the case of PIM, this implies investments on port and airport infrastructure and a strategic focus on air transport mode, due to (1 short life cycle of products, (2 distance from suppliers, (3 quick response to demand and (4 the fact that even PIM´s standard products use, in average, forty per cent of air transport at inbound logistics.

Coping with uncertainty in environmental impact assessments: Open techniques

Energy Technology Data Exchange (ETDEWEB)

Cardenas, Ibsen C., E-mail: c.cardenas@utwente.nl [IceBridge Research Institutea, Universiteit Twente, P.O. Box 217, 7500 AE Enschede (Netherlands); Halman, Johannes I.M., E-mail: J.I.M.Halman@utwente.nl [Universiteit Twente, P.O. Box 217, 7500 AE Enschede (Netherlands)

2016-09-15

Uncertainty is virtually unavoidable in environmental impact assessments (EIAs). From the literature related to treating and managing uncertainty, we have identified specific techniques for coping with uncertainty in EIAs. Here, we have focused on basic steps in the decision-making process that take place within an EIA setting. More specifically, we have identified uncertainties involved in each decision-making step and discussed the extent to which these can be treated and managed in the context of an activity or project that may have environmental impacts. To further demonstrate the relevance of the techniques identified, we have examined the extent to which the EIA guidelines currently used in Colombia consider and provide guidance on managing the uncertainty involved in these assessments. Some points that should be considered in order to provide greater robustness in impact assessments in Colombia have been identified. These include the management of stakeholder values, the systematic generation of project options, and their associated impacts as well as the associated management actions, and the evaluation of uncertainties and assumptions. We believe that the relevant and specific techniques reported here can be a reference for future evaluations of other EIA guidelines in different countries. - Highlights: • uncertainty is unavoidable in environmental impact assessments, EIAs; • we have identified some open techniques to EIAs for treating and managing uncertainty in these assessments; • points for improvement that should be considered in order to provide greater robustness in EIAs in Colombia have been identified; • the paper provides substantiated a reference for possible examinations of EIAs guidelines in other countries.

Coping with uncertainty in environmental impact assessments: Open techniques

International Nuclear Information System (INIS)

Cardenas, Ibsen C.; Halman, Johannes I.M.

2016-01-01

Uncertainty is virtually unavoidable in environmental impact assessments (EIAs). From the literature related to treating and managing uncertainty, we have identified specific techniques for coping with uncertainty in EIAs. Here, we have focused on basic steps in the decision-making process that take place within an EIA setting. More specifically, we have identified uncertainties involved in each decision-making step and discussed the extent to which these can be treated and managed in the context of an activity or project that may have environmental impacts. To further demonstrate the relevance of the techniques identified, we have examined the extent to which the EIA guidelines currently used in Colombia consider and provide guidance on managing the uncertainty involved in these assessments. Some points that should be considered in order to provide greater robustness in impact assessments in Colombia have been identified. These include the management of stakeholder values, the systematic generation of project options, and their associated impacts as well as the associated management actions, and the evaluation of uncertainties and assumptions. We believe that the relevant and specific techniques reported here can be a reference for future evaluations of other EIA guidelines in different countries. - Highlights: • uncertainty is unavoidable in environmental impact assessments, EIAs; • we have identified some open techniques to EIAs for treating and managing uncertainty in these assessments; • points for improvement that should be considered in order to provide greater robustness in EIAs in Colombia have been identified; • the paper provides substantiated a reference for possible examinations of EIAs guidelines in other countries.

Risk assessment under deep uncertainty: A methodological comparison

International Nuclear Information System (INIS)

Shortridge, Julie; Aven, Terje; Guikema, Seth

2017-01-01

Probabilistic Risk Assessment (PRA) has proven to be an invaluable tool for evaluating risks in complex engineered systems. However, there is increasing concern that PRA may not be adequate in situations with little underlying knowledge to support probabilistic representation of uncertainties. As analysts and policy makers turn their attention to deeply uncertain hazards such as climate change, a number of alternatives to traditional PRA have been proposed. This paper systematically compares three diverse approaches for risk analysis under deep uncertainty (qualitative uncertainty factors, probability bounds, and robust decision making) in terms of their representation of uncertain quantities, analytical output, and implications for risk management. A simple example problem is used to highlight differences in the way that each method relates to the traditional risk assessment process and fundamental issues associated with risk assessment and description. We find that the implications for decision making are not necessarily consistent between approaches, and that differences in the representation of uncertain quantities and analytical output suggest contexts in which each method may be most appropriate. Finally, each methodology demonstrates how risk assessment can inform decision making in deeply uncertain contexts, informing more effective responses to risk problems characterized by deep uncertainty. - Highlights: • We compare three diverse approaches to risk assessment under deep uncertainty. • A simple example problem highlights differences in analytical process and results. • Results demonstrate how methodological choices can impact risk assessment results.

Uncertainty analysis in an accidental situation. Radionuclide transfer in environment and assessment of human exposure by food

International Nuclear Information System (INIS)

Sy, Mouhamadou Moustapha

2016-01-01

Major nuclear accidents of Chernobyl (April, 1986) and Fukushima (March, 2011) have led to a huge environmental contamination with important amounts of radionuclides released in the atmosphere. Risk assessment, in case of nuclear emergency, is confronted to uncertainties on the transfer of radioactive substances in terrestrial ecosystems and to human population through the food chain, which could affect the reliability of decisions. The extent of the repercussions of Chernobyl and Fukushima accidents highlighted the difficulty of managing the consequences of such disasters and specifically to accommodate the different sources of uncertainty within decision-making processes. The objective of this research project is to develop a methodology to take into account uncertainties within environmental and food risk assessment models in order to improve decision support tools used for accidental situations. In this regard, different hierarchical Bayesian models aiming at capturing, within a unique modelling framework, uncertainty and variability about radioecological parameters of great important for accidental situation (dry and wet interception fractions and weathering loss parameter) were developed. Models parameters were estimated by Bayesian inference applied on databases obtained by an extended literature review. The impact on the risk assessment models of uncertainty and variability about these radioecological parameters was then assessed by stochastic simulations and sensitivity analyses applied on two case-studies: a hypothetical accident simulating a standardized deposition of radionuclides and the accident of Fukushima nuclear power plant. The works developed in this project contribute to enhance knowledge on key processes governing environmental transfer of radionuclides and to improve the parameterization of the radioecological risk assessment models with respect to the research lines outlined by the scientific community in radioecology. (author)

Global reverse supply chain design for solid waste recycling under uncertainties and carbon emission constraint.

Science.gov (United States)

Xu, Zhitao; Elomri, Adel; Pokharel, Shaligram; Zhang, Qin; Ming, X G; Liu, Wenjie

2017-06-01

The emergence of concerns over environmental protection, resource conservation as well as the development of logistics operations and manufacturing technology has led several countries to implement formal collection and recycling systems of solid waste. Such recycling system has the benefits of reducing environmental pollution, boosting the economy by creating new jobs, and generating income from trading the recyclable materials. This leads to the formation of a global reverse supply chain (GRSC) of solid waste. In this paper, we investigate the design of such a GRSC with a special emphasis on three aspects; (1) uncertainty of waste collection levels, (2) associated carbon emissions, and (3) challenges posed by the supply chain's global aspect, particularly the maritime transportation costs and currency exchange rates. To the best of our knowledge, this paper is the first attempt to integrate the three above-mentioned important aspects in the design of a GRSC. We have used mixed integer-linear programming method along with robust optimization to develop the model which is validated using a sample case study of e-waste management. Our results show that using a robust model by taking the complex interactions characterizing global reverse supply chain networks into account, we can create a better GRSC. The effect of uncertainties and carbon constraints on decisions to reduce costs and emissions are also shown. Copyright © 2017 Elsevier Ltd. All rights reserved.

Uncertainties in life cycle assessment of waste management systems

DEFF Research Database (Denmark)

Clavreul, Julie; Christensen, Thomas Højlund

2011-01-01

Life cycle assessment has been used to assess environmental performances of waste management systems in many studies. The uncertainties inherent to its results are often pointed out but not always quantified, which should be the case to ensure a good decisionmaking process. This paper proposes...... a method to assess all parameter uncertainties and quantify the overall uncertainty of the assessment. The method is exemplified in a case study, where the goal is to determine if anaerobic digestion of organic waste is more beneficial than incineration in Denmark, considering only the impact on global...... warming. The sensitivity analysis pointed out ten parameters particularly highly influencing the result of the study. In the uncertainty analysis, the distributions of these ten parameters were used in a Monte Carlo analysis, which concluded that incineration appeared more favourable than anaerobic...

Optimizing decentralized production-distribution planning problem in a multi-period supply chain network under uncertainty

Science.gov (United States)

Nourifar, Raheleh; Mahdavi, Iraj; Mahdavi-Amiri, Nezam; Paydar, Mohammad Mahdi

2017-09-01

Decentralized supply chain management is found to be significantly relevant in today's competitive markets. Production and distribution planning is posed as an important optimization problem in supply chain networks. Here, we propose a multi-period decentralized supply chain network model with uncertainty. The imprecision related to uncertain parameters like demand and price of the final product is appropriated with stochastic and fuzzy numbers. We provide mathematical formulation of the problem as a bi-level mixed integer linear programming model. Due to problem's convolution, a structure to solve is developed that incorporates a novel heuristic algorithm based on Kth-best algorithm, fuzzy approach and chance constraint approach. Ultimately, a numerical example is constructed and worked through to demonstrate applicability of the optimization model. A sensitivity analysis is also made.

Mastering demand and supply uncertainty with combined product and process configuration

NARCIS (Netherlands)

Verdouw, C.N.; Beulens, A.J.M.; Trienekens, J.H.; Verwaart, D.

2010-01-01

The key challenge for mastering high uncertainty of both demand and supply is to attune products and business processes in the entire supply chain continuously to customer requirements. Product configurators have proven to be powerful tools for managing demand uncertainty. This paper assesses how

Optimal grid design and logistic planning for wind and biomass based renewable electricity supply chains under uncertainties

International Nuclear Information System (INIS)

Osmani, Atif; Zhang, Jun

2014-01-01

In this work, the grid design and optimal allocation of wind and biomass resources for renewable electricity supply chains under uncertainties is studied. Due to wind intermittency, generation of wind electricity is not uniform and cannot be counted on to be readily available to meet the demand. Biomass represents a type of stored energy and is the only renewable resource that can be used for producing biofuels and generating electricity whenever required. However, amount of biomass resources are finite and might not be sufficient to meet the demand for electricity and biofuels. Potential of wind and biomass resources is therefore jointly analyzed for electricity generation. Policies are proposed and evaluated for optimal allocation of finite biomass resources for electricity generation. A stochastic programming model is proposed that optimally balances the electricity demand across the available supply from wind and biomass resources under uncertainties in wind speed and electricity sale price. A case study set in the American Midwest is presented to demonstrate the effectiveness of the proposed model by determining the optimal decisions for generation and transmission of renewable electricity. Sensitivity analysis shows that level of subsidy for renewable electricity production has a major impact on the decisions. - Highlights: • Stochastic optimization model for wind/biomass renewable electricity supply chain. • Multiple uncertainties in wind speeds and electricity sale price. • Proposed stochastic model outperforms the deterministic model under uncertainties. • Uncertainty affects grid connectivity and allocation of power generation capacity. • Location of wind farms is found to be insensitive to the stochastic environment

Uncertainties in risk assessment at USDOE facilities

Energy Technology Data Exchange (ETDEWEB)

Hamilton, L.D.; Holtzman, S.; Meinhold, A.F.; Morris, S.C.; Rowe, M.D.

1994-01-01

The United States Department of Energy (USDOE) has embarked on an ambitious program to remediate environmental contamination at its facilities. Decisions concerning cleanup goals, choices among cleanup technologies, and funding prioritization should be largely risk-based. Risk assessments will be used more extensively by the USDOE in the future. USDOE needs to develop and refine risk assessment methods and fund research to reduce major sources of uncertainty in risk assessments at USDOE facilities. The terms{open_quote} risk assessment{close_quote} and{open_quote} risk management{close_quote} are frequently confused. The National Research Council (1983) and the United States Environmental Protection Agency (USEPA, 1991a) described risk assessment as a scientific process that contributes to risk management. Risk assessment is the process of collecting, analyzing and integrating data and information to identify hazards, assess exposures and dose responses, and characterize risks. Risk characterization must include a clear presentation of {open_quotes}... the most significant data and uncertainties...{close_quotes} in an assessment. Significant data and uncertainties are {open_quotes}...those that define and explain the main risk conclusions{close_quotes}. Risk management integrates risk assessment information with other considerations, such as risk perceptions, socioeconomic and political factors, and statutes, to make and justify decisions. Risk assessments, as scientific processes, should be made independently of the other aspects of risk management (USEPA, 1991a), but current methods for assessing health risks are based on conservative regulatory principles, causing unnecessary public concern and misallocation of funds for remediation.

Uncertainties in risk assessment at USDOE facilities

International Nuclear Information System (INIS)

Hamilton, L.D.; Holtzman, S.; Meinhold, A.F.; Morris, S.C.; Rowe, M.D.

1994-01-01

The United States Department of Energy (USDOE) has embarked on an ambitious program to remediate environmental contamination at its facilities. Decisions concerning cleanup goals, choices among cleanup technologies, and funding prioritization should be largely risk-based. Risk assessments will be used more extensively by the USDOE in the future. USDOE needs to develop and refine risk assessment methods and fund research to reduce major sources of uncertainty in risk assessments at USDOE facilities. The terms open-quote risk assessment close-quote and open-quote risk management close-quote are frequently confused. The National Research Council (1983) and the United States Environmental Protection Agency (USEPA, 1991a) described risk assessment as a scientific process that contributes to risk management. Risk assessment is the process of collecting, analyzing and integrating data and information to identify hazards, assess exposures and dose responses, and characterize risks. Risk characterization must include a clear presentation of open-quotes... the most significant data and uncertainties...close quotes in an assessment. Significant data and uncertainties are open-quotes...those that define and explain the main risk conclusionsclose quotes. Risk management integrates risk assessment information with other considerations, such as risk perceptions, socioeconomic and political factors, and statutes, to make and justify decisions. Risk assessments, as scientific processes, should be made independently of the other aspects of risk management (USEPA, 1991a), but current methods for assessing health risks are based on conservative regulatory principles, causing unnecessary public concern and misallocation of funds for remediation

Assessing uncertainties in flood forecasts for decision making: prototype of an operational flood management system integrating ensemble predictions

Directory of Open Access Journals (Sweden)

J. Dietrich

2009-08-01

Full Text Available Ensemble forecasts aim at framing the uncertainties of the potential future development of the hydro-meteorological situation. A probabilistic evaluation can be used to communicate forecast uncertainty to decision makers. Here an operational system for ensemble based flood forecasting is presented, which combines forecasts from the European COSMO-LEPS, SRNWP-PEPS and COSMO-DE prediction systems. A multi-model lagged average super-ensemble is generated by recombining members from different runs of these meteorological forecast systems. A subset of the super-ensemble is selected based on a priori model weights, which are obtained from ensemble calibration. Flood forecasts are simulated by the conceptual rainfall-runoff-model ArcEGMO. Parameter uncertainty of the model is represented by a parameter ensemble, which is a priori generated from a comprehensive uncertainty analysis during model calibration. The use of a computationally efficient hydrological model within a flood management system allows us to compute the hydro-meteorological model chain for all members of the sub-ensemble. The model chain is not re-computed before new ensemble forecasts are available, but the probabilistic assessment of the output is updated when new information from deterministic short range forecasts or from assimilation of measured data becomes available. For hydraulic modelling, with the desired result of a probabilistic inundation map with high spatial resolution, a replacement model can help to overcome computational limitations. A prototype of the developed framework has been applied for a case study in the Mulde river basin. However these techniques, in particular the probabilistic assessment and the derivation of decision rules are still in their infancy. Further research is necessary and promising.

The role of the uncertainty in assessing future scenarios of water shortage in alluvial aquifers

Science.gov (United States)

Romano, Emanuele; Camici, Stefania; Brocca, Luca; Moramarco, Tommaso; Guyennon, Nicolas; Preziosi, Elisabetta

2015-04-01

There are many evidences that the combined effects of variations in precipitation and temperature due to climate change can result in a significant change of the recharge to groundwater at different time scales. A possible reduction of effective infiltration can result in a significant decrease, temporary or permanent, of the availability of the resource and, consequently, the sustainable pumping rate should be reassessed. In addition to this, one should also consider the so called indirect impacts of climate change, resulting from human intervention (e.g. augmentation of abstractions) which are feared to be even more important than the direct ones in the medium term: thus, a possible increase of episodes of shortage (i.e. the inability of the groundwater system to completely supply the water demand) can result both from change in the climate forcing and change in the demand. In order to assess future scenarios of water shortage a modelling chain is often used. It includes: 1) the use of General Circulation Models to estimate changes in temperature and precipitation; 2) downscaling procedures to match modeling scenarios to the observed meteorological time series; 3) soil-atmosphere modelling to estimate the time variation of the recharge to the aquifer; 4) groundwater flow models to simulate the water budget and piezometric head evolution; 5) future scenarios of groundwater quantitative status that include scenarios of demand variation. It is well known that each of these processing steps is affected by an intrinsic uncertainty that propagates through the whole chain leading to a final uncertainty on the piezometric head scenarios. The estimate of such an uncertainty is a key point for a correct management of groundwater resources, in case of water shortage due to prolonged droughts as well as for planning purposes. This study analyzes the uncertainty of the processing chain from GCM scenarios to its impact on an alluvial aquifer in terms of exploitation

A multi-product green supply chain under government supervision with price and demand uncertainty

Science.gov (United States)

Hafezalkotob, Ashkan; Zamani, Soma

2018-05-01

In this paper, a bi-level game-theoretic model is proposed to investigate the effects of governmental financial intervention on green supply chain. This problem is formulated as a bi-level program for a green supply chain that produces various products with different environmental pollution levels. The problem is also regard uncertainties in market demand and sale price of raw materials and products. The model is further transformed into a single-level nonlinear programming problem by replacing the lower-level optimization problem with its Karush-Kuhn-Tucker optimality conditions. Genetic algorithm is applied as a solution methodology to solve nonlinear programming model. Finally, to investigate the validity of the proposed method, the computational results obtained through genetic algorithm are compared with global optimal solution attained by enumerative method. Analytical results indicate that the proposed GA offers better solutions in large size problems. Also, we conclude that financial intervention by government consists of green taxation and subsidization is an effective method to stabilize green supply chain members' performance.

Reevaluation of the role of nuclear uncertainties in experiments on atomic parity violation with isotopic chains

International Nuclear Information System (INIS)

Derevianko, Andrei; Porsev, Sergey G.

2002-01-01

In light of new data on neutron distributions from experiments with antiprotonic atoms [Trzcinska et al., Phys. Rev. Lett. 87, 082501 (2001)], we reexamine the role of nuclear-structure uncertainties in the interpretation of measurements of parity violation in atoms using chains of isotopes of the same element. With these new nuclear data, we find an improvement in the sensitivity of isotopic chain measurements to 'new physics' beyond the standard model. We compare possible constraints on 'new physics' with the most accurate to date single-isotope probe of parity violation in the Cs atom. We conclude that presently isotopic chain experiments employing atoms with nuclear charges Z < or approx. 50 may result in more accurate tests of the weak interaction

Enquiring into the roots of bioenergy - epistemic uncertainties in life cycle assessments

DEFF Research Database (Denmark)

Saez de Bikuna Salinas, Koldo

global warming impacts than the respective fossil fuels they replace unless planted on abandoned lands. With Papers I-II, the selection of the land-use references and time horizons involved in LCA of biofuels was demonstrated to be crucial for the characterization of the resulting environmental impacts......The research for this Thesis was originally framed around the “sustainability assessment of full chain bioenergy”. However, it is known for some years that the critical impacts of dedicated bioenergy relate to induced land use changes (LUC). Their criticality derives from their potential...... to dominate environmental impacts from a life-cycle perspective and from the uncertainty that accompanies them. On the other hand, continued land use may be a concern for soil’s long-term sustainability (understood as fertility), which has recently received attention in environmental life-cycle assessments...

Uncertainty of a hydrological climate change impact assessment - Is it really all about climate uncertainty?

Science.gov (United States)

Honti, Mark; Reichert, Peter; Scheidegger, Andreas; Stamm, Christian

2013-04-01

Climate change impact assessments have become more and more popular in hydrology since the middle 1980's with another boost after the publication of the IPCC AR4 report. During hundreds of impact studies a quasi-standard methodology emerged, which is mainly shaped by the growing public demand for predicting how water resources management or flood protection should change in the close future. The ``standard'' workflow considers future climate under a specific IPCC emission scenario simulated by global circulation models (GCMs), possibly downscaled by a regional climate model (RCM) and/or a stochastic weather generator. The output from the climate models is typically corrected for bias before feeding it into a calibrated hydrological model, which is run on the past and future meteorological data to analyse the impacts of climate change on the hydrological indicators of interest. The impact predictions are as uncertain as any forecast that tries to describe the behaviour of an extremely complex system decades into the future. Future climate predictions are uncertain due to the scenario uncertainty and the GCM model uncertainty that is obvious on finer resolution than continental scale. Like in any hierarchical model system, uncertainty propagates through the descendant components. Downscaling increases uncertainty with the deficiencies of RCMs and/or weather generators. Bias correction adds a strong deterministic shift to the input data. Finally the predictive uncertainty of the hydrological model ends the cascade that leads to the total uncertainty of the hydrological impact assessment. There is an emerging consensus between many studies on the relative importance of the different uncertainty sources. The prevailing perception is that GCM uncertainty dominates hydrological impact studies. There are only few studies, which found that the predictive uncertainty of hydrological models can be in the same range or even larger than climatic uncertainty. We carried out a

Comparing Cost Of New Supply Chain Designs Under Uncertainty

DEFF Research Database (Denmark)

Wæhrens, Brian Vejrum; Kristensen, Jesper; Asmussen, Jesper Normann

2016-01-01

Accounting, Operational Modelling and SCM inform decision making for new SCDs. Through four embedded cases, a gap is found between the practice of a global OEM and literature. Results shows complications when assessing SCDs due to limited understanding of the internal activity costs, supply chain dynamics...

Coping with uncertainty in environmental impact assessments: Open techniques

NARCIS (Netherlands)

Chivatá Cárdenas, Ibsen; Halman, Johannes I.M.

2016-01-01

Uncertainty is virtually unavoidable in environmental impact assessments (EIAs). From the literature related to treating and managing uncertainty, we have identified specific techniques for coping with uncertainty in EIAs. Here, we have focused on basic steps in the decision-making process that take

Assessing uncertainty and risk in exploited marine populations

International Nuclear Information System (INIS)

Fogarty, M.J.; Mayo, R.K.; O'Brien, L.; Serchuk, F.M.; Rosenberg, A.A.

1996-01-01

The assessment and management of exploited fish and invertebrate populations is subject to several types of uncertainty. This uncertainty translates into risk to the population in the development and implementation of fishery management advice. Here, we define risk as the probability that exploitation rates will exceed a threshold level where long term sustainability of the stock is threatened. We distinguish among several sources of error or uncertainty due to (a) stochasticity in demographic rates and processes, particularly in survival rates during the early fife stages; (b) measurement error resulting from sampling variation in the determination of population parameters or in model estimation; and (c) the lack of complete information on population and ecosystem dynamics. The first represents a form of aleatory uncertainty while the latter two factors represent forms of epistemic uncertainty. To illustrate these points, we evaluate the recent status of the Georges Bank cod stock in a risk assessment framework. Short term stochastic projections are made accounting for uncertainty in population size and for random variability in the number of young surviving to enter the fishery. We show that recent declines in this cod stock can be attributed to exploitation rates that have substantially exceeded sustainable levels

Real-Time Optimal Flood Control Decision Making and Risk Propagation Under Multiple Uncertainties

Science.gov (United States)

Zhu, Feilin; Zhong, Ping-An; Sun, Yimeng; Yeh, William W.-G.

2017-12-01

Multiple uncertainties exist in the optimal flood control decision-making process, presenting risks involving flood control decisions. This paper defines the main steps in optimal flood control decision making that constitute the Forecast-Optimization-Decision Making (FODM) chain. We propose a framework for supporting optimal flood control decision making under multiple uncertainties and evaluate risk propagation along the FODM chain from a holistic perspective. To deal with uncertainties, we employ stochastic models at each link of the FODM chain. We generate synthetic ensemble flood forecasts via the martingale model of forecast evolution. We then establish a multiobjective stochastic programming with recourse model for optimal flood control operation. The Pareto front under uncertainty is derived via the constraint method coupled with a two-step process. We propose a novel SMAA-TOPSIS model for stochastic multicriteria decision making. Then we propose the risk assessment model, the risk of decision-making errors and rank uncertainty degree to quantify the risk propagation process along the FODM chain. We conduct numerical experiments to investigate the effects of flood forecast uncertainty on optimal flood control decision making and risk propagation. We apply the proposed methodology to a flood control system in the Daduhe River basin in China. The results indicate that the proposed method can provide valuable risk information in each link of the FODM chain and enable risk-informed decisions with higher reliability.

Vector network analyzer (VNA) measurements and uncertainty assessment

CERN Document Server

Shoaib, Nosherwan

2017-01-01

This book describes vector network analyzer measurements and uncertainty assessments, particularly in waveguide test-set environments, in order to establish their compatibility to the International System of Units (SI) for accurate and reliable characterization of communication networks. It proposes a fully analytical approach to measurement uncertainty evaluation, while also highlighting the interaction and the linear propagation of different uncertainty sources to compute the final uncertainties associated with the measurements. The book subsequently discusses the dimensional characterization of waveguide standards and the quality of the vector network analyzer (VNA) calibration techniques. The book concludes with an in-depth description of the novel verification artefacts used to assess the performance of the VNAs. It offers a comprehensive reference guide for beginners to experts, in both academia and industry, whose work involves the field of network analysis, instrumentation and measurements.

Uncertainty Assessment: What Good Does it Do? (Invited)

Science.gov (United States)

Oreskes, N.; Lewandowsky, S.

2013-12-01

the public debate or advance public policy. We argue that attempts to address public doubts by improving uncertainty assessment are bound to fail, insofar as the motives for doubt-mongering are independent of scientific uncertainty, and therefore remain unaffected even as those uncertainties are diminished. We illustrate this claim by consideration of the evolution of the debate over the past ten years over the relationship between hurricanes and anthropogenic climate change. We suggest that scientists should pursue uncertainty assessment if such assessment improves scientific understanding, but not as a means to reduce public doubts or advance public policy in relation to anthropogenic climate change.

Optimal design of supply chain network under uncertainty environment using hybrid analytical and simulation modeling approach

Science.gov (United States)

Chiadamrong, N.; Piyathanavong, V.

2017-12-01

Models that aim to optimize the design of supply chain networks have gained more interest in the supply chain literature. Mixed-integer linear programming and discrete-event simulation are widely used for such an optimization problem. We present a hybrid approach to support decisions for supply chain network design using a combination of analytical and discrete-event simulation models. The proposed approach is based on iterative procedures until the difference between subsequent solutions satisfies the pre-determined termination criteria. The effectiveness of proposed approach is illustrated by an example, which shows closer to optimal results with much faster solving time than the results obtained from the conventional simulation-based optimization model. The efficacy of this proposed hybrid approach is promising and can be applied as a powerful tool in designing a real supply chain network. It also provides the possibility to model and solve more realistic problems, which incorporate dynamism and uncertainty.

Avoiding climate change uncertainties in Strategic Environmental Assessment

DEFF Research Database (Denmark)

Larsen, Sanne Vammen; Kørnøv, Lone; Driscoll, Patrick Arthur

2013-01-01

This article is concerned with how Strategic Environmental Assessment (SEA) practice handles climate change uncertainties within the Danish planning system. First, a hypothetical model is set up for how uncertainty is handled and not handled in decision-making. The model incorporates the strategies...

Sensitivity and uncertainty analyses for performance assessment modeling

International Nuclear Information System (INIS)

Doctor, P.G.

1988-08-01

Sensitivity and uncertainty analyses methods for computer models are being applied in performance assessment modeling in the geologic high level radioactive waste repository program. The models used in performance assessment tend to be complex physical/chemical models with large numbers of input variables. There are two basic approaches to sensitivity and uncertainty analyses: deterministic and statistical. The deterministic approach to sensitivity analysis involves numerical calculation or employs the adjoint form of a partial differential equation to compute partial derivatives; the uncertainty analysis is based on Taylor series expansions of the input variables propagated through the model to compute means and variances of the output variable. The statistical approach to sensitivity analysis involves a response surface approximation to the model with the sensitivity coefficients calculated from the response surface parameters; the uncertainty analysis is based on simulation. The methods each have strengths and weaknesses. 44 refs

A Fuzzy AHP-TOPSIS Framework for the Risk Assessment of Green Supply Chain Implementation in the Textile Industry

Directory of Open Access Journals (Sweden)

Muhammad Nazam

2015-05-01

Full Text Available In the emerging supply chain environment, green supply chain risk management plays a significant role than ever. Risk is an inherent uncertainty and has tendency to disrupt the typical green supply chain management (GSCM operations and eventually reduce the success rate of industries. In order to mitigate the consequences, a fuzzy multi-criteria group decision making modeling (FMCGDM which could evaluate the potential risks in the context of (GSCM is needed from the industrial point of view. Therefore, this research proposes a combined fuzzy analytical hierarchy process (AHP to calculate the weight of each risk criterion and sub-criterion and technique for order performance by similarity to ideal solution (TOPSIS methodology to rank and assess the risks associated with implementation of (GSCM practices under the fuzzy environment. The proposed fuzzy risk-oriented evaluation model is applied to a practical case of textile manufacturing industry. Finally, the proposed model helps the researchers and practitioners to understand the importance of conducting appropriate risk assessment when implementing green supply chain initiatives.

Communicating uncertainties in assessments of future sea level rise

Science.gov (United States)

Wikman-Svahn, P.

2013-12-01

How uncertainty should be managed and communicated in policy-relevant scientific assessments is directly connected to the role of science and the responsibility of scientists. These fundamentally philosophical issues influence how scientific assessments are made and how scientific findings are communicated to policymakers. It is therefore of high importance to discuss implicit assumptions and value judgments that are made in policy-relevant scientific assessments. The present paper examines these issues for the case of scientific assessments of future sea level rise. The magnitude of future sea level rise is very uncertain, mainly due to poor scientific understanding of all physical mechanisms affecting the great ice sheets of Greenland and Antarctica, which together hold enough land-based ice to raise sea levels more than 60 meters if completely melted. There has been much confusion from policymakers on how different assessments of future sea levels should be interpreted. Much of this confusion is probably due to how uncertainties are characterized and communicated in these assessments. The present paper draws on the recent philosophical debate on the so-called "value-free ideal of science" - the view that science should not be based on social and ethical values. Issues related to how uncertainty is handled in scientific assessments are central to this debate. This literature has much focused on how uncertainty in data, parameters or models implies that choices have to be made, which can have social consequences. However, less emphasis has been on how uncertainty is characterized when communicating the findings of a study, which is the focus of the present paper. The paper argues that there is a tension between on the one hand the value-free ideal of science and on the other hand usefulness for practical applications in society. This means that even if the value-free ideal could be upheld in theory, by carefully constructing and hedging statements characterizing

The role of uncertainty analysis in dose reconstruction and risk assessment

International Nuclear Information System (INIS)

Hoffman, F.O.; Simon, S.L.; Thiessen. K.M.

1996-01-01

Dose reconstruction and risk assessment rely heavily on the use of mathematical models to extrapolate information beyond the realm of direct observation. Because models are merely approximations of real systems, their predictions are inherently uncertain. As a result, full disclosure of uncertainty in dose and risk estimates is essential to achieve scientific credibility and to build public trust. The need for formal analysis of uncertainty in model predictions was presented during the nineteenth annual meeting of the NCRP. At that time, quantitative uncertainty analysis was considered a relatively new and difficult subject practiced by only a few investigators. Today, uncertainty analysis has become synonymous with the assessment process itself. When an uncertainty analysis is used iteratively within the assessment process, it can guide experimental research to refine dose and risk estimates, deferring potentially high cost or high consequence decisions until uncertainty is either acceptable or irreducible. Uncertainty analysis is now mandated for all ongoing dose reconstruction projects within the United States, a fact that distinguishes dose reconstruction from other types of exposure and risk assessments. 64 refs., 6 figs., 1 tab

Systematic approach for the design of sustainable supply chains under quality uncertainty

International Nuclear Information System (INIS)

Medina-González, Sergio; Graells, Moisès; Guillén-Gosálbez, Gonzalo; Espuña, Antonio; Puigjaner, Luis

2017-01-01

Highlights: • This methodology allows taking profit of the uneven biomass quality in the management of energy supply chains. • The proposed strategy allows the solution selection considering multiple decision criteria. • The proposed strategy facilitates decision-making avoiding subjectivity in the solution selection. • The resulting base model is generic and the strategy is flexible enough to be implemented in other real cases. - Abstract: Sustainable processes have recently awaked an increasing interest in the process systems engineering literature. In industry, this kind of problems inevitably required a multi-objective analysis to evaluate the environmental impact in addition to the economic performance. Bio-based processes have the potential to enhance the sustainability level of the energy sector. Nevertheless, such processes very often show variable conditions and present an uncertain behavior. The approaches presented for solving multi-objective problems under uncertainty have neglected the potential effects of different quality streams on the overall system. Here, it is presented an alternative approach, based on a State Task Network formulation, capable of optimizing under uncertain conditions, considering multiple selection criteria and accounting for the material quality effect. The resulting set of Pareto solutions are then assessed using the Elimination and Choice Expressing Reality-IV method, which identify the ones showing better overall performance considering the uncertain parameters space.

Assessing framing of uncertainties in water management practice

NARCIS (Netherlands)

Isendahl, N.; Dewulf, A.; Brugnach, M.; Francois, G.; Möllenkamp, S.; Pahl-Wostl, C.

2009-01-01

Dealing with uncertainties in water management is an important issue and is one which will only increase in light of global changes, particularly climate change. So far, uncertainties in water management have mostly been assessed from a scientific point of view, and in quantitative terms. In this

Subspace-based Inverse Uncertainty Quantification for Nuclear Data Assessment

Energy Technology Data Exchange (ETDEWEB)

Khuwaileh, B.A., E-mail: bakhuwai@ncsu.edu; Abdel-Khalik, H.S.

2015-01-15

Safety analysis and design optimization depend on the accurate prediction of various reactor attributes. Predictions can be enhanced by reducing the uncertainty associated with the attributes of interest. An inverse problem can be defined and solved to assess the sources of uncertainty, and experimental effort can be subsequently directed to further improve the uncertainty associated with these sources. In this work a subspace-based algorithm for inverse sensitivity/uncertainty quantification (IS/UQ) has been developed to enable analysts account for all sources of nuclear data uncertainties in support of target accuracy assessment-type analysis. An approximate analytical solution of the optimization problem is used to guide the search for the dominant uncertainty subspace. By limiting the search to a subspace, the degrees of freedom available for the optimization search are significantly reduced. A quarter PWR fuel assembly is modeled and the accuracy of the multiplication factor and the fission reaction rate are used as reactor attributes whose uncertainties are to be reduced. Numerical experiments are used to demonstrate the computational efficiency of the proposed algorithm. Our ongoing work is focusing on extending the proposed algorithm to account for various forms of feedback, e.g., thermal-hydraulics and depletion effects.

Estimating uncertainty of data limited stock assessments

DEFF Research Database (Denmark)

Kokkalis, Alexandros; Eikeset, Anne Maria; Thygesen, Uffe Høgsbro

2017-01-01

-limited. Particular emphasis is put on providing uncertainty estimates of the data-limited assessment. We assess four cod stocks in the North-East Atlantic and compare our estimates of stock status (F/Fmsy) with the official assessments. The estimated stock status of all four cod stocks followed the established stock...

Robust Optimization on Regional WCO-for-Biodiesel Supply Chain under Supply and Demand Uncertainties

Directory of Open Access Journals (Sweden)

Yong Zhang

2016-01-01

Full Text Available This paper aims to design a robust waste cooking oil- (WCO- for-biodiesel supply chain under WCO supply and price as well as biodiesel demand and price uncertainties, so as to improve biorefineries’ ability to cope with the poor environment. A regional supply chain is firstly introduced based on the biggest WCO-for-biodiesel company in Changzhou, Jiangsu province, and it comprises three components: WCO supplier, biorefinery, and demand zone. And then a robust mixed integer linear model with multiple objectives (economic, environmental, and social objectives is proposed for both biorefinery location and transportation plans. After that, a heuristic algorithm based on genetic algorithm is proposed to solve this model. Finally, the 27 cities in Yangtze River delta are adopted to verify the proposed models and methods, and the sustainability and robustness of biodiesel supply are discussed.

Are Local Food Chains More Sustainable than Global Food Chains? Considerations for Assessment

Directory of Open Access Journals (Sweden)

Gianluca Brunori

2016-05-01

Full Text Available This paper summarizes the main findings of the GLAMUR project which starts with an apparently simple question: is “local” more sustainable than “global”? Sustainability assessment is framed within a post-normal science perspective, advocating the integration of public deliberation and scientific research. The assessment spans 39 local, intermediate and global supply chain case studies across different commodities and countries. Assessment criteria cover environmental, economic, social, health and ethical sustainability dimensions. A closer view of the food system demonstrates a highly dynamic local–global continuum where actors, while adapting to a changing environment, establish multiple relations and animate several chain configurations. The evidence suggests caution when comparing “local” and “global” chains, especially when using the outcomes of the comparison in decision-making. Supply chains are analytical constructs that necessarily—and arbitrarily—are confined by system boundaries, isolating a set of elements from an interconnected whole. Even consolidated approaches, such as Life Cycle Assessment (LCA, assess only a part of sustainability attributes, and the interpretation may be controversial. Many sustainability attributes are not yet measurable and “hard” methodologies need to be complemented by “soft” methodologies which are at least able to identify critical issues and trade-offs. Aware of these limitations, our research shows that comparing local and global chains, with the necessary caution, can help overcome a priori positions that so far have characterized the debate between “localists” and “globalists”. At firm level, comparison between “local” and “global” chains could be useful to identify best practices, benchmarks, critical points, and errors to avoid. As sustainability is not a status to achieve, but a never-ending process, comparison and deliberation can be the basis of a �

Methods for Assessing Uncertainties in Climate Change, Impacts and Responses (Invited)

Science.gov (United States)

Manning, M. R.; Swart, R.

2009-12-01

Assessing the scientific uncertainties or confidence levels for the many different aspects of climate change is particularly important because of the seriousness of potential impacts and the magnitude of economic and political responses that are needed to mitigate climate change effectively. This has made the treatment of uncertainty and confidence a key feature in the assessments carried out by the Intergovernmental Panel on Climate Change (IPCC). Because climate change is very much a cross-disciplinary area of science, adequately dealing with uncertainties requires recognition of their wide range and different perspectives on assessing and communicating those uncertainties. The structural differences that exist across disciplines are often embedded deeply in the corresponding literature that is used as the basis for an IPCC assessment. The assessment of climate change science by the IPCC has from its outset tried to report the levels of confidence and uncertainty in the degree of understanding in both the underlying multi-disciplinary science and in projections for future climate. The growing recognition of the seriousness of this led to the formation of a detailed approach for consistent treatment of uncertainties in the IPCC’s Third Assessment Report (TAR) [Moss and Schneider, 2000]. However, in completing the TAR there remained some systematic differences between the disciplines raising concerns about the level of consistency. So further consideration of a systematic approach to uncertainties was undertaken for the Fourth Assessment Report (AR4). The basis for the approach used in the AR4 was developed at an expert meeting of scientists representing many different disciplines. This led to the introduction of a broader way of addressing uncertainties in the AR4 [Manning et al., 2004] which was further refined by lengthy discussions among many IPCC Lead Authors, for over a year, resulting in a short summary of a standard approach to be followed for that

Uncertainty assessment of equations of state with application to an organic Rankine cycle

DEFF Research Database (Denmark)

Frutiger, Jerome; Bell, Ian; O’Connell, John P.

2017-01-01

Evaluations of equations of state (EoS) should include uncertainty. This study presents a genericmethod to analyse EoS from a detailed uncertainty analysis of the mathematical form and the dataused to obtain EoS parameter values. The method is illustrated by comparison of Soave–Redlich–Kwong (SRK......) cubic EoS with perturbed-chain statistical associating fluid theory (PC-SAFT) EoS for anorganic Rankine cycle (ORC) for heat recovery to power fromthe exhaust gas of a marine diesel engineusing cyclopentane as working fluid. Uncertainties of the EoS input parameters including......Evaluations of equations of state (EoS) should include uncertainty. This study presents a genericmethod to analyse EoS from a detailed uncertainty analysis of the mathematical form and the dataused to obtain EoS parameter values. The method is illustrated by comparison of Soave–Redlich–Kwong (SRK...

Harnessing the theoretical foundations of the exponential and beta-Poisson dose-response models to quantify parameter uncertainty using Markov Chain Monte Carlo.

Science.gov (United States)

Schmidt, Philip J; Pintar, Katarina D M; Fazil, Aamir M; Topp, Edward

2013-09-01

Dose-response models are the essential link between exposure assessment and computed risk values in quantitative microbial risk assessment, yet the uncertainty that is inherent to computed risks because the dose-response model parameters are estimated using limited epidemiological data is rarely quantified. Second-order risk characterization approaches incorporating uncertainty in dose-response model parameters can provide more complete information to decisionmakers by separating variability and uncertainty to quantify the uncertainty in computed risks. Therefore, the objective of this work is to develop procedures to sample from posterior distributions describing uncertainty in the parameters of exponential and beta-Poisson dose-response models using Bayes's theorem and Markov Chain Monte Carlo (in OpenBUGS). The theoretical origins of the beta-Poisson dose-response model are used to identify a decomposed version of the model that enables Bayesian analysis without the need to evaluate Kummer confluent hypergeometric functions. Herein, it is also established that the beta distribution in the beta-Poisson dose-response model cannot address variation among individual pathogens, criteria to validate use of the conventional approximation to the beta-Poisson model are proposed, and simple algorithms to evaluate actual beta-Poisson probabilities of infection are investigated. The developed MCMC procedures are applied to analysis of a case study data set, and it is demonstrated that an important region of the posterior distribution of the beta-Poisson dose-response model parameters is attributable to the absence of low-dose data. This region includes beta-Poisson models for which the conventional approximation is especially invalid and in which many beta distributions have an extreme shape with questionable plausibility. © Her Majesty the Queen in Right of Canada 2013. Reproduced with the permission of the Minister of the Public Health Agency of Canada.

Uncertainty propagation in probabilistic risk assessment: A comparative study

International Nuclear Information System (INIS)

Ahmed, S.; Metcalf, D.R.; Pegram, J.W.

1982-01-01

Three uncertainty propagation techniques, namely method of moments, discrete probability distribution (DPD), and Monte Carlo simulation, generally used in probabilistic risk assessment, are compared and conclusions drawn in terms of the accuracy of the results. For small uncertainty in the basic event unavailabilities, the three methods give similar results. For large uncertainty, the method of moments is in error, and the appropriate method is to propagate uncertainty in the discrete form either by DPD method without sampling or by Monte Carlo. (orig.)

Avoiding climate change uncertainties in Strategic Environmental Assessment

Energy Technology Data Exchange (ETDEWEB)

Larsen, Sanne Vammen, E-mail: sannevl@plan.aau.dk [The Danish Centre for Environmental Assessment, Aalborg University-Copenhagen, A.C. Meyers Vænge 15, 2450 København SV (Denmark); Kørnøv, Lone, E-mail: lonek@plan.aau.dk [The Danish Centre for Environmental Assessment, Aalborg University, Skibbrogade 5, 1. Sal, 9000 Aalborg (Denmark); Driscoll, Patrick, E-mail: patrick@plan.aau.dk [The Danish Centre for Environmental Assessment, Aalborg University-Copenhagen, A.C. Meyers Vænge 15, 2450 København SV (Denmark)

2013-11-15

This article is concerned with how Strategic Environmental Assessment (SEA) practice handles climate change uncertainties within the Danish planning system. First, a hypothetical model is set up for how uncertainty is handled and not handled in decision-making. The model incorporates the strategies ‘reduction’ and ‘resilience’, ‘denying’, ‘ignoring’ and ‘postponing’. Second, 151 Danish SEAs are analysed with a focus on the extent to which climate change uncertainties are acknowledged and presented, and the empirical findings are discussed in relation to the model. The findings indicate that despite incentives to do so, climate change uncertainties were systematically avoided or downplayed in all but 5 of the 151 SEAs that were reviewed. Finally, two possible explanatory mechanisms are proposed to explain this: conflict avoidance and a need to quantify uncertainty.

Avoiding climate change uncertainties in Strategic Environmental Assessment

International Nuclear Information System (INIS)

Larsen, Sanne Vammen; Kørnøv, Lone; Driscoll, Patrick

2013-01-01

This article is concerned with how Strategic Environmental Assessment (SEA) practice handles climate change uncertainties within the Danish planning system. First, a hypothetical model is set up for how uncertainty is handled and not handled in decision-making. The model incorporates the strategies ‘reduction’ and ‘resilience’, ‘denying’, ‘ignoring’ and ‘postponing’. Second, 151 Danish SEAs are analysed with a focus on the extent to which climate change uncertainties are acknowledged and presented, and the empirical findings are discussed in relation to the model. The findings indicate that despite incentives to do so, climate change uncertainties were systematically avoided or downplayed in all but 5 of the 151 SEAs that were reviewed. Finally, two possible explanatory mechanisms are proposed to explain this: conflict avoidance and a need to quantify uncertainty

Assessing complexity of supply chains: evidence from wholesalers

NARCIS (Netherlands)

de Leeuw, S.L.J.M.; Grotenhuis, R.; van Goor, A.R.

2013-01-01

Purpose: The purpose of this paper is to discuss complexity assessment in supply chains, to describe a methodology for measuring supply chain complexity in distributive trade and to illustrate the measurement of supply chain complexity and mechanisms to cope with supply chain complexity in

Evaluation of food chain transfer data for use in accident consequence assessment

International Nuclear Information System (INIS)

Coughtrey, P.J.; Kirton, J.A.; Mitchell, N.G.

1991-01-01

Input data for the food chain transport component of radiological assessment models are summarised in the context of the sources of information available prior to the Chernobyl accident and those derived after the accident. Particular attention is devoted to interception and retention soil-to-plant, and plant-to-animal transfer, and to the applicability of environmental data to both equilibrium and time-dependent models. It is argued that much of the current uncertainty in parameter values for use in radiological assessment models reflects lack of understanding of processes involved in the various stages of transfer of radionuclides to man. The Chernobyl accident highlighted this lack of fundamental knowledge and illustrated a number of areas where further research and model development is justified. These areas are identified and suggestions given for appropriate research to support model development

Uncertainties in Life Cycle Greenhouse Gas Emissions from Advanced Biomass Feedstock Logistics Supply Chains in Kansas

Directory of Open Access Journals (Sweden)

Long Nguyen

2014-11-01

Full Text Available To meet Energy Independence and Security Act (EISA cellulosic biofuel mandates, the United States will require an annual domestic supply of about 242 million Mg of biomass by 2022. To improve the feedstock logistics of lignocellulosic biofuels in order to access available biomass resources from areas with varying yields, commodity systems have been proposed and designed to deliver quality-controlled biomass feedstocks at preprocessing “depots”. Preprocessing depots densify and stabilize the biomass prior to long-distance transport and delivery to centralized biorefineries. The logistics of biomass commodity supply chains could introduce spatially variable environmental impacts into the biofuel life cycle due to needing to harvest, move, and preprocess biomass from multiple distances that have variable spatial density. This study examines the uncertainty in greenhouse gas (GHG emissions of corn stover logistics within a bio-ethanol supply chain in the state of Kansas, where sustainable biomass supply varies spatially. Two scenarios were evaluated each having a different number of depots of varying capacity and location within Kansas relative to a central commodity-receiving biorefinery to test GHG emissions uncertainty. The first scenario sited four preprocessing depots evenly across the state of Kansas but within the vicinity of counties having high biomass supply density. The second scenario located five depots based on the shortest depot-to-biorefinery rail distance and biomass availability. The logistics supply chain consists of corn stover harvest, collection and storage, feedstock transport from field to biomass preprocessing depot, preprocessing depot operations, and commodity transport from the biomass preprocessing depot to the biorefinery. Monte Carlo simulation was used to estimate the spatial uncertainty in the feedstock logistics gate-to-gate sequence. Within the logistics supply chain GHG emissions are most sensitive to the

Uncertainty of fast biological radiation dose assessment for emergency response scenarios.

Science.gov (United States)

Ainsbury, Elizabeth A; Higueras, Manuel; Puig, Pedro; Einbeck, Jochen; Samaga, Daniel; Barquinero, Joan Francesc; Barrios, Lleonard; Brzozowska, Beata; Fattibene, Paola; Gregoire, Eric; Jaworska, Alicja; Lloyd, David; Oestreicher, Ursula; Romm, Horst; Rothkamm, Kai; Roy, Laurence; Sommer, Sylwester; Terzoudi, Georgia; Thierens, Hubert; Trompier, Francois; Vral, Anne; Woda, Clemens

2017-01-01

Reliable dose estimation is an important factor in appropriate dosimetric triage categorization of exposed individuals to support radiation emergency response. Following work done under the EU FP7 MULTIBIODOSE and RENEB projects, formal methods for defining uncertainties on biological dose estimates are compared using simulated and real data from recent exercises. The results demonstrate that a Bayesian method of uncertainty assessment is the most appropriate, even in the absence of detailed prior information. The relative accuracy and relevance of techniques for calculating uncertainty and combining assay results to produce single dose and uncertainty estimates is further discussed. Finally, it is demonstrated that whatever uncertainty estimation method is employed, ignoring the uncertainty on fast dose assessments can have an important impact on rapid biodosimetric categorization.

Uncertainty evaluation methods for waste package performance assessment

International Nuclear Information System (INIS)

Wu, Y.T.; Nair, P.K.; Journel, A.G.; Abramson, L.R.

1991-01-01

This report identifies and investigates methodologies to deal with uncertainties in assessing high-level nuclear waste package performance. Four uncertainty evaluation methods (probability-distribution approach, bounding approach, expert judgment, and sensitivity analysis) are suggested as the elements of a methodology that, without either diminishing or enhancing the input uncertainties, can evaluate performance uncertainty. Such a methodology can also help identify critical inputs as a guide to reducing uncertainty so as to provide reasonable assurance that the risk objectives are met. This report examines the current qualitative waste containment regulation and shows how, in conjunction with the identified uncertainty evaluation methodology, a framework for a quantitative probability-based rule can be developed that takes account of the uncertainties. Current US Nuclear Regulatory Commission (NRC) regulation requires that the waste packages provide ''substantially complete containment'' (SCC) during the containment period. The term ''SCC'' is ambiguous and subject to interpretation. This report, together with an accompanying report that describes the technical considerations that must be addressed to satisfy high-level waste containment requirements, provides a basis for a third report to develop recommendations for regulatory uncertainty reduction in the ''containment''requirement of 10 CFR Part 60. 25 refs., 3 figs., 2 tabs

Assessment of uncertainties in Neutron Multiplicity Counting

International Nuclear Information System (INIS)

Peerani, P.; Marin Ferrer, M.

2008-01-01

This paper describes a methodology for a complete and correct assessment of the errors coming from the uncertainty of each individual component on the final result. A general methodology accounting for all the main sources of error (both type-A and type-B) will be outlined. In order to better illustrate the method, a practical example applying it to the uncertainty estimation for a special case of multiplicity counter, the SNMC developed at JRC, will be given

Replication quality assessment and uncertainty evaluation of a polymer precision injection moulded component

DEFF Research Database (Denmark)

Baruffi, Federico; Calaon, Matteo; Tosello, Guido

2017-01-01

Precision injection moulding holds a central role in manufacturing as only replication process currently capable of accurately producing complex shaped polymer parts integrating micrometric features on a mass scale production. In this scenario, a study on the replication quality of a polymer...... injection moulded precision component for telecommunication applications is presented. The effects of the process parameters on the component dimensional variation have been investigated using a statistical approach. Replication fidelity of produced parts has been assessed using a focus variation microscope...... with sub-micrometric resolution. Measurement uncertainty has then been evaluated, according to the GUM considering contributions from different process settings combinations and mould geometries. The analysis showed that the injection moulding manufacturing process and the utilized measurement chain...

Aiding alternatives assessment with an uncertainty-tolerant hazard scoring method.

Science.gov (United States)

Faludi, Jeremy; Hoang, Tina; Gorman, Patrick; Mulvihill, Martin

2016-11-01

This research developed a single-score system to simplify and clarify decision-making in chemical alternatives assessment, accounting for uncertainty. Today, assessing alternatives to hazardous constituent chemicals is a difficult task-rather than comparing alternatives by a single definitive score, many independent toxicological variables must be considered at once, and data gaps are rampant. Thus, most hazard assessments are only comprehensible to toxicologists, but business leaders and politicians need simple scores to make decisions. In addition, they must balance hazard against other considerations, such as product functionality, and they must be aware of the high degrees of uncertainty in chemical hazard data. This research proposes a transparent, reproducible method to translate eighteen hazard endpoints into a simple numeric score with quantified uncertainty, alongside a similar product functionality score, to aid decisions between alternative products. The scoring method uses Clean Production Action's GreenScreen as a guide, but with a different method of score aggregation. It provides finer differentiation between scores than GreenScreen's four-point scale, and it displays uncertainty quantitatively in the final score. Displaying uncertainty also illustrates which alternatives are early in product development versus well-defined commercial products. This paper tested the proposed assessment method through a case study in the building industry, assessing alternatives to spray polyurethane foam insulation containing methylene diphenyl diisocyanate (MDI). The new hazard scoring method successfully identified trade-offs between different alternatives, showing finer resolution than GreenScreen Benchmarking. Sensitivity analysis showed that different weighting schemes in hazard scores had almost no effect on alternatives ranking, compared to uncertainty from data gaps. Copyright © 2016 Elsevier Ltd. All rights reserved.

The role of sensitivity analysis in assessing uncertainty

International Nuclear Information System (INIS)

Crick, M.J.; Hill, M.D.

1987-01-01

Outside the specialist world of those carrying out performance assessments considerable confusion has arisen about the meanings of sensitivity analysis and uncertainty analysis. In this paper we attempt to reduce this confusion. We then go on to review approaches to sensitivity analysis within the context of assessing uncertainty, and to outline the types of test available to identify sensitive parameters, together with their advantages and disadvantages. The views expressed in this paper are those of the authors; they have not been formally endorsed by the National Radiological Protection Board and should not be interpreted as Board advice

Qualitative uncertainty analysis in probabilistic safety assessment context

International Nuclear Information System (INIS)

Apostol, M.; Constantin, M; Turcu, I.

2007-01-01

In Probabilistic Safety Assessment (PSA) context, an uncertainty analysis is performed either to estimate the uncertainty in the final results (the risk to public health and safety) or to estimate the uncertainty in some intermediate quantities (the core damage frequency, the radionuclide release frequency or fatality frequency). The identification and evaluation of uncertainty are important tasks because they afford credit to the results and help in the decision-making process. Uncertainty analysis can be performed qualitatively or quantitatively. This paper performs a preliminary qualitative uncertainty analysis, by identification of major uncertainty in PSA level 1- level 2 interface and in the other two major procedural steps of a level 2 PSA i.e. the analysis of accident progression and of the containment and analysis of source term for severe accidents. One should mention that a level 2 PSA for a Nuclear Power Plant (NPP) involves the evaluation and quantification of the mechanisms, amount and probabilities of subsequent radioactive material releases from the containment. According to NUREG 1150, an important task in source term analysis is fission products transport analysis. The uncertainties related to the isotopes distribution in CANDU NPP primary circuit and isotopes' masses transferred in the containment, using SOPHAEROS module from ASTEC computer code will be also presented. (authors)

A review of uncertainty research in impact assessment

International Nuclear Information System (INIS)

Leung, Wanda; Noble, Bram; Gunn, Jill; Jaeger, Jochen A.G.

2015-01-01

This paper examines uncertainty research in Impact Assessment (IA) and the focus of attention of the IA scholarly literature. We do so by first exploring ‘outside’ the IA literature, identifying three main themes of uncertainty research, and then apply these themes to examine the focus of scholarly research on uncertainty ‘inside’ IA. Based on a search of the database Scopus, we identified 134 journal papers published between 1970 and 2013 that address uncertainty in IA, 75% of which were published since 2005. We found that 90% of IA research addressing uncertainty focused on uncertainty in the practice of IA, including uncertainty in impact predictions, models and managing environmental impacts. Notwithstanding early guidance on uncertainty treatment in IA from the 1980s, we found no common, underlying conceptual framework that was guiding research on uncertainty in IA practice. Considerably less attention, only 9% of papers, focused on uncertainty communication, disclosure and decision-making under uncertain conditions, the majority of which focused on the need to disclose uncertainties as opposed to providing guidance on how to do so and effectively use that information to inform decisions. Finally, research focused on theory building for explaining human behavior with respect to uncertainty avoidance constituted only 1% of the IA published literature. We suggest the need for further conceptual framework development for researchers focused on identifying and addressing uncertainty in IA practice; the need for guidance on how best to communicate uncertainties in practice, versus criticizing practitioners for not doing so; research that explores how best to interpret and use disclosures about uncertainty when making decisions about project approvals, and the implications of doing so; and academic theory building and exploring the utility of existing theories to better understand and explain uncertainty avoidance behavior in IA. - Highlights: • We

A review of uncertainty research in impact assessment

Energy Technology Data Exchange (ETDEWEB)

Leung, Wanda, E-mail: wanda.leung@usask.ca [Department of Geography and Planning, University of Saskatchewan, 117 Science Place, Saskatoon, Saskatchewan S7N 5A5 (Canada); Noble, Bram, E-mail: b.noble@usask.ca [Department of Geography and Planning, School of Environment and Sustainability, University of Saskatchewan, 117 Science Place, Saskatoon, Saskatchewan S7N 5A5 (Canada); Gunn, Jill, E-mail: jill.gunn@usask.ca [Department of Geography and Planning, University of Saskatchewan, 117 Science Place, Saskatoon, Saskatchewan S7N 5A5 (Canada); Jaeger, Jochen A.G., E-mail: jochen.jaeger@concordia.ca [Department of Geography, Planning and Environment, Concordia University, 1455 de Maisonneuve W., Suite 1255, Montreal, Quebec H3G 1M8 (Canada); Loyola Sustainability Research Centre, Concordia University, 7141 Sherbrooke W., AD-502, Montreal, Quebec H4B 1R6 (Canada)

2015-01-15

This paper examines uncertainty research in Impact Assessment (IA) and the focus of attention of the IA scholarly literature. We do so by first exploring ‘outside’ the IA literature, identifying three main themes of uncertainty research, and then apply these themes to examine the focus of scholarly research on uncertainty ‘inside’ IA. Based on a search of the database Scopus, we identified 134 journal papers published between 1970 and 2013 that address uncertainty in IA, 75% of which were published since 2005. We found that 90% of IA research addressing uncertainty focused on uncertainty in the practice of IA, including uncertainty in impact predictions, models and managing environmental impacts. Notwithstanding early guidance on uncertainty treatment in IA from the 1980s, we found no common, underlying conceptual framework that was guiding research on uncertainty in IA practice. Considerably less attention, only 9% of papers, focused on uncertainty communication, disclosure and decision-making under uncertain conditions, the majority of which focused on the need to disclose uncertainties as opposed to providing guidance on how to do so and effectively use that information to inform decisions. Finally, research focused on theory building for explaining human behavior with respect to uncertainty avoidance constituted only 1% of the IA published literature. We suggest the need for further conceptual framework development for researchers focused on identifying and addressing uncertainty in IA practice; the need for guidance on how best to communicate uncertainties in practice, versus criticizing practitioners for not doing so; research that explores how best to interpret and use disclosures about uncertainty when making decisions about project approvals, and the implications of doing so; and academic theory building and exploring the utility of existing theories to better understand and explain uncertainty avoidance behavior in IA. - Highlights: • We

Uncertainty Assessment of Equations of State with Application to an Organic Rankine Cycle

DEFF Research Database (Denmark)

Frutiger, Jerome; Bell, Ian; O’Connell, John P.

2017-01-01

Evaluations of equations of state (EoS) with application to process systems should include uncertainty analysis. A generic method is presented for determining such uncertainties from both the mathematical formand the data for obtaining EoS parameter values. The method is implemented for the Soave......–Redlich–Kwong (SRK), the Peng-Robinson (PR) cubic EoS, and the perturbed-chain statistical associating fluid theory (PCSAFT) EoS, as applied to an organic Rankine cycle (ORC) power system to recover heat from the exhaust gas of a marine diesel engine with cyclopentane as the working fluid. Uncertainties of the Eo......S input parameters, including their corresponding correlation structure, are quantified from the data using a bootstrap method. A Monte Carlo procedure propagates parameter input uncertainties onto the process output. Regressions have been made of the three cubic EoS parameters from both critical point...

Assessment and uncertainty analysis of groundwater risk.

Science.gov (United States)

Li, Fawen; Zhu, Jingzhao; Deng, Xiyuan; Zhao, Yong; Li, Shaofei

2018-01-01

Groundwater with relatively stable quantity and quality is commonly used by human being. However, as the over-mining of groundwater, problems such as groundwater funnel, land subsidence and salt water intrusion have emerged. In order to avoid further deterioration of hydrogeological problems in over-mining regions, it is necessary to conduct the assessment of groundwater risk. In this paper, risks of shallow and deep groundwater in the water intake area of the South-to-North Water Transfer Project in Tianjin, China, were evaluated. Firstly, two sets of four-level evaluation index system were constructed based on the different characteristics of shallow and deep groundwater. Secondly, based on the normalized factor values and the synthetic weights, the risk values of shallow and deep groundwater were calculated. Lastly, the uncertainty of groundwater risk assessment was analyzed by indicator kriging method. The results meet the decision maker's demand for risk information, and overcome previous risk assessment results expressed in the form of deterministic point estimations, which ignore the uncertainty of risk assessment. Copyright © 2017 Elsevier Inc. All rights reserved.

A Framework for Understanding Uncertainty in Seismic Risk Assessment.

Science.gov (United States)

Foulser-Piggott, Roxane; Bowman, Gary; Hughes, Martin

2017-10-11

A better understanding of the uncertainty that exists in models used for seismic risk assessment is critical to improving risk-based decisions pertaining to earthquake safety. Current models estimating the probability of collapse of a building do not consider comprehensively the nature and impact of uncertainty. This article presents a model framework to enhance seismic risk assessment and thus gives decisionmakers a fuller understanding of the nature and limitations of the estimates. This can help ensure that risks are not over- or underestimated and the value of acquiring accurate data is appreciated fully. The methodology presented provides a novel treatment of uncertainties in input variables, their propagation through the model, and their effect on the results. The study presents ranges of possible annual collapse probabilities for different case studies on buildings in different parts of the world, exposed to different levels of seismicity, and with different vulnerabilities. A global sensitivity analysis was conducted to determine the significance of uncertain variables. Two key outcomes are (1) that the uncertainty in ground-motion conversion equations has the largest effect on the uncertainty in the calculation of annual collapse probability; and (2) the vulnerability of a building appears to have an effect on the range of annual collapse probabilities produced, i.e., the level of uncertainty in the estimate of annual collapse probability, with less vulnerable buildings having a smaller uncertainty. © 2017 Society for Risk Analysis.

Uncertainty estimation in nuclear power plant probabilistic safety assessment

International Nuclear Information System (INIS)

Guarro, S.B.; Cummings, G.E.

1989-01-01

Probabilistic Risk Assessment (PRA) was introduced in the nuclear industry and the nuclear regulatory process in 1975 with the publication of the Reactor Safety Study by the U.S. Nuclear Regulatory Commission. Almost fifteen years later, the state-of-the-art in this field has been expanded and sharpened in many areas, and about thirty-five plant-specific PRAs (Probabilistic Risk Assessments) have been performed by the nuclear utility companies or by the U.S. Nuclear Regulatory commission. Among the areas where the most evident progress has been made in PRA and PSA (Probabilistic Safety Assessment, as these studies are more commonly referred to in the international community outside the U.S.) is the development of a consistent framework for the identification of sources of uncertainty and the estimation of their magnitude as it impacts various risk measures. Techniques to propagate uncertainty in reliability data through the risk models and display its effect on the top level risk estimates were developed in the early PRAs. The Seismic Safety Margin Research Program (SSMRP) study was the first major risk study to develop an approach to deal explicitly with uncertainty in risk estimates introduced not only by uncertainty in component reliability data, but by the incomplete state of knowledge of the assessor(s) with regard to basic phenomena that may trigger and drive a severe accident. More recently NUREG-1150, another major study of reactor risk sponsored by the NRC, has expanded risk uncertainty estimation and analysis into the realm of model uncertainty related to the relatively poorly known post-core-melt phenomena which determine the behavior of the molten core and of the rector containment structures

International survey for good practices in forecasting uncertainty assessment and communication

Science.gov (United States)

Berthet, Lionel; Piotte, Olivier

2014-05-01

Achieving technically sound flood forecasts is a crucial objective for forecasters but remains of poor use if the users do not understand properly their significance and do not use it properly in decision making. One usual way to precise the forecasts limitations is to communicate some information about their uncertainty. Uncertainty assessment and communication to stakeholders are thus important issues for operational flood forecasting services (FFS) but remain open fields for research. French FFS wants to publish graphical streamflow and level forecasts along with uncertainty assessment in near future on its website (available to the greater public). In order to choose the technical options best adapted to its operational context, it carried out a survey among more than 15 fellow institutions. Most of these are providing forecasts and warnings to civil protection officers while some were mostly working for hydroelectricity suppliers. A questionnaire has been prepared in order to standardize the analysis of the practices of the surveyed institutions. The survey was conducted by gathering information from technical reports or from the scientific literature, as well as 'interviews' driven by phone, email discussions or meetings. The questionnaire helped in the exploration of practices in uncertainty assessment, evaluation and communication. Attention was paid to the particular context within which every insitution works, in the analysis drawn from raw results. Results show that most services interviewed assess their forecasts uncertainty. However, practices can differ significantly from a country to another. Popular techniques are ensemble approaches. They allow to take into account several uncertainty sources. Statistical past forecasts analysis (such as the quantile regressions) are also commonly used. Contrary to what was expected, only few services emphasize the role of the forecaster (subjective assessment). Similar contrasts can be observed in uncertainty

An introductory guide to uncertainty analysis in environmental and health risk assessment

International Nuclear Information System (INIS)

Hoffman, F.O.; Hammonds, J.S.

1992-10-01

To compensate for the potential for overly conservative estimates of risk using standard US Environmental Protection Agency methods, an uncertainty analysis should be performed as an integral part of each risk assessment. Uncertainty analyses allow one to obtain quantitative results in the form of confidence intervals that will aid in decision making and will provide guidance for the acquisition of additional data. To perform an uncertainty analysis, one must frequently rely on subjective judgment in the absence of data to estimate the range and a probability distribution describing the extent of uncertainty about a true but unknown value for each parameter of interest. This information is formulated from professional judgment based on an extensive review of literature, analysis of the data, and interviews with experts. Various analytical and numerical techniques are available to allow statistical propagation of the uncertainty in the model parameters to a statement of uncertainty in the risk to a potentially exposed individual. Although analytical methods may be straightforward for relatively simple models, they rapidly become complicated for more involved risk assessments. Because of the tedious efforts required to mathematically derive analytical approaches to propagate uncertainty in complicated risk assessments, numerical methods such as Monte Carlo simulation should be employed. The primary objective of this report is to provide an introductory guide for performing uncertainty analysis in risk assessments being performed for Superfund sites

Assessment of uncertainties in severe accident management strategies

International Nuclear Information System (INIS)

Kastenberg, W.E.; Apostolakis, G.; Catton, I.; Dhir, V.K.; Okrent, D.

1990-01-01

Recent progress on the development of Probabilistic Risk Assessment (PRA) as a tool for qualifying nuclear reactor safety and on research devoted to severe accident phenomena has made severe accident management an achievable goal. Severe accident management strategies may involve operational changes, modification and/or addition of hardware, and institutional changes. In order to achieve the goal of managing severe accidents, a method for assessment of strategies must be developed which integrates PRA methodology and our current knowledge concerning severe accident phenomena, including uncertainty. The research project presented in this paper is aimed at delineating uncertainties in severe accident progression and their impact on severe accident management strategies

Assessment and characterization of the total geometric uncertainty in Gamma Knife radiosurgery using polymer gels

International Nuclear Information System (INIS)

Moutsatsos, A.; Karaiskos, P.; Pantelis, E.; Georgiou, E.; Petrokokkinos, L.; Sakelliou, L.; Torrens, M.; Seimenis, I.

2013-01-01

Purpose: This work proposes and implements an experimental methodology, based on polymer gels, for assessing the total geometric uncertainty and characterizing its contributors in Gamma Knife (GK) radiosurgery. Methods: A treatment plan consisting of 26, 4-mm GK single shot dose distributions, covering an extended region of the Leksell stereotactic space, was prepared and delivered to a polymer gel filled polymethyl methacrylate (PMMA) head phantom (16 cm diameter) used to accurately reproduce every link in the GK treatment chain. The center of each shot served as a “control point” in the assessment of the GK total geometric uncertainty, which depends on (a) the spatial dose delivery uncertainty of the PERFEXION GK unit used in this work, (b) the spatial distortions inherent in MR images commonly used for target delineation, and (c) the geometric uncertainty contributor associated with the image registration procedure performed by the Leksell GammaPlan (LGP) treatment planning system (TPS), in the case that registration is directly based on the apparent fiducial locations depicted in each MR image by the N-shaped rods on the Leksell localization box. The irradiated phantom was MR imaged at 1.5 T employing a T2-weighted pulse sequence. Four image series were acquired by alternating the frequency encoding axis and reversing the read gradient polarity, thus allowing the characterization of the MR-related spatial distortions. Results: MR spatial distortions stemming from main field (B 0 ) inhomogeneity as well as from susceptibility and chemical shift phenomena (also known as sequence dependent distortions) were found to be of the order of 0.5 mm, while those owing to gradient nonlinearities (also known as sequence independent distortions) were found to increase with distance from the MR scanner isocenter extending up to 0.47 mm at an Euclidean distance of 69.6 mm. Regarding the LGP image registration procedure, the corresponding average contribution to the total

Assessment and characterization of the total geometric uncertainty in Gamma Knife radiosurgery using polymer gels.

Science.gov (United States)

Moutsatsos, A; Karaiskos, P; Petrokokkinos, L; Sakelliou, L; Pantelis, E; Georgiou, E; Torrens, M; Seimenis, I

2013-03-01

This work proposes and implements an experimental methodology, based on polymer gels, for assessing the total geometric uncertainty and characterizing its contributors in Gamma Knife (GK) radiosurgery. A treatment plan consisting of 26, 4-mm GK single shot dose distributions, covering an extended region of the Leksell stereotactic space, was prepared and delivered to a polymer gel filled polymethyl methacrylate (PMMA) head phantom (16 cm diameter) used to accurately reproduce every link in the GK treatment chain. The center of each shot served as a "control point" in the assessment of the GK total geometric uncertainty, which depends on (a) the spatial dose delivery uncertainty of the PERFEXION GK unit used in this work, (b) the spatial distortions inherent in MR images commonly used for target delineation, and (c) the geometric uncertainty contributor associated with the image registration procedure performed by the Leksell GammaPlan (LGP) treatment planning system (TPS), in the case that registration is directly based on the apparent fiducial locations depicted in each MR image by the N-shaped rods on the Leksell localization box. The irradiated phantom was MR imaged at 1.5 T employing a T2-weighted pulse sequence. Four image series were acquired by alternating the frequency encoding axis and reversing the read gradient polarity, thus allowing the characterization of the MR-related spatial distortions. MR spatial distortions stemming from main field (B0) inhomogeneity as well as from susceptibility and chemical shift phenomena (also known as sequence dependent distortions) were found to be of the order of 0.5 mm, while those owing to gradient nonlinearities (also known as sequence independent distortions) were found to increase with distance from the MR scanner isocenter extending up to 0.47 mm at an Euclidean distance of 69.6 mm. Regarding the LGP image registration procedure, the corresponding average contribution to the total geometric uncertainty ranged from

An Integrated Method of Supply Chains Vulnerability Assessment

Directory of Open Access Journals (Sweden)

Jiaguo Liu

2016-01-01

Full Text Available Supply chain vulnerability identification and evaluation are extremely important to mitigate the supply chain risk. We present an integrated method to assess the supply chain vulnerability. The potential failure mode of the supply chain vulnerability is analyzed through the SCOR model. Combining the fuzzy theory and the gray theory, the correlation degree of each vulnerability indicator can be calculated and the target improvements can be carried out. In order to verify the effectiveness of the proposed method, we use Kendall’s tau coefficient to measure the effect of different methods. The result shows that the presented method has the highest consistency in the assessment compared with the other two methods.

Sources/treatment of uncertainties in the performance assessment of geologic radioactive waste repositories

International Nuclear Information System (INIS)

Cranwell, R.M.

1987-01-01

Uncertainties in the performance assessment of geologic radioactive waste repositories have several sources. The more important ones include: 1) uncertainty in the conditions of a disposal system over the temporal scales set forth in regulations, 2) uncertainty in the conceptualization of the geohydrologic system, 3) uncertainty in the theoretical description of a given conceptual model of the system, 4) uncertainty in the development of computer codes to implement the solution of a mathematical model, and 5) uncertainty in the parameters and data required in the models and codes used to assess the long-term performance of the disposal system. This paper discusses each of these uncertainties and outlines methods for addressing these uncertainties

Supply chain network design under uncertainty

DEFF Research Database (Denmark)

Govindan, Kannan; Fattahi, Mohammad; Keyvanshokooh, Esmaeil

2017-01-01

Supply chain network design (SCND) is one of the most crucial planning problems in supply chain management (SCM). Nowadays, design decisions should be viable enough to function well under complex and uncertain business environments for many years or decades. Therefore, it is essential to make...... programming, risk-averse stochastic programming, robust optimization, and fuzzy mathematical programming are explored in terms of mathematical modeling and solution approaches. Finally, the drawbacks and missing aspects of the related literature are highlighted and a list of potential issues for future...

Associating uncertainty with datasets using Linked Data and allowing propagation via provenance chains

Science.gov (United States)

Car, Nicholas; Cox, Simon; Fitch, Peter

2015-04-01

With earth-science datasets increasingly being published to enable re-use in projects disassociated from the original data acquisition or generation, there is an urgent need for associated metadata to be connected, in order to guide their application. In particular, provenance traces should support the evaluation of data quality and reliability. However, while standards for describing provenance are emerging (e.g. PROV-O), these do not include the necessary statistical descriptors and confidence assessments. UncertML has a mature conceptual model that may be used to record uncertainty metadata. However, by itself UncertML does not support the representation of uncertainty of multi-part datasets, and provides no direct way of associating the uncertainty information - metadata in relation to a dataset - with dataset objects.We present a method to address both these issues by combining UncertML with PROV-O, and delivering resulting uncertainty-enriched provenance traces through the Linked Data API. UncertProv extends the PROV-O provenance ontology with an RDF formulation of the UncertML conceptual model elements, adds further elements to support uncertainty representation without a conceptual model and the integration of UncertML through links to documents. The Linked ID API provides a systematic way of navigating from dataset objects to their UncertProv metadata and back again. The Linked Data API's 'views' capability enables access to UncertML and non-UncertML uncertainty metadata representations for a dataset. With this approach, it is possible to access and navigate the uncertainty metadata associated with a published dataset using standard semantic web tools, such as SPARQL queries. Where the uncertainty data follows the UncertML model it can be automatically interpreted and may also support automatic uncertainty propagation . Repositories wishing to enable uncertainty propagation for all datasets must ensure that all elements that are associated with uncertainty

An assessment of apple orchard investments in South Africa under uncertainty and irreversibility

Directory of Open Access Journals (Sweden)

MAG Darroch

2004-11-01

Full Text Available The competitiveness of the South African fresh apple export value chain can be improved if local farmers grow and market more new apple cultivars. An ex ante version of the Dixit-Pindyck investment model is used to assess how uncertainty and irreversibility associated with adopting the new Pink Lady cultivar rather than a traditional Golden Delicious cultivar will raise the hurdle rate required to trigger investment. Modified real hurdle rates reflecting the value of the option to delay investment estimated for both cultivars, are about double the real rate of five per cent that is often used in orthodox investment analyses. The Pink Lady investment seems to be relatively more profitable under the assumed conditions, but it also has a relatively greater variance in expected real annual net returns.

Modelling the bioaccumulation of persistent organic pollutants in agricultural food chains for regulatory exposure assessment.

Science.gov (United States)

Takaki, Koki; Wade, Andrew J; Collins, Chris D

2017-02-01

New models for estimating bioaccumulation of persistent organic pollutants in the agricultural food chain were developed using recent improvements to plant uptake and cattle transfer models. One model named AgriSim was based on K OW regressions of bioaccumulation in plants and cattle, while the other was a steady-state mechanistic model, AgriCom. The two developed models and European Union System for the Evaluation of Substances (EUSES), as a benchmark, were applied to four reported food chain (soil/air-grass-cow-milk) scenarios to evaluate the performance of each model simulation against the observed data. The four scenarios considered were as follows: (1) polluted soil and air, (2) polluted soil, (3) highly polluted soil surface and polluted subsurface and (4) polluted soil and air at different mountain elevations. AgriCom reproduced observed milk bioaccumulation well for all four scenarios, as did AgriSim for scenarios 1 and 2, but EUSES only did this for scenario 1. The main causes of the deviation for EUSES and AgriSim were the lack of the soil-air-plant pathway and the ambient air-plant pathway, respectively. Based on the results, it is recommended that soil-air-plant and ambient air-plant pathway should be calculated separately and the K OW regression of transfer factor to milk used in EUSES be avoided. AgriCom satisfied the recommendations that led to the low residual errors between the simulated and the observed bioaccumulation in agricultural food chain for the four scenarios considered. It is therefore recommended that this model should be incorporated into regulatory exposure assessment tools. The model uncertainty of the three models should be noted since the simulated concentration in milk from 5th to 95th percentile of the uncertainty analysis often varied over two orders of magnitude. Using a measured value of soil organic carbon content was effective to reduce this uncertainty by one order of magnitude.

Assessment of Measurement Uncertainty Values of the Scandium Determination in Marine Sediment

International Nuclear Information System (INIS)

Rina-Mulyaningsih, Th.

2005-01-01

The result value of testing is meaningless if it isn't completed with uncertainty value. So that with the analysis result Sc in the marine sediment sample. It was assessed the uncertainty measurement of Sc analysis in marine sediment. The experiment was done in AAN Serpong laboratory. The result of calculation uncertainty on Sc analysis showed that the uncertainty components come from: preparation of sample and standard/comparator, purity of standard, counting statistics (sample and standard), repeatability, nuclear data and decay correction. The assessment on uncertainty must be done for the analysis of others elements, because each elements has difference nuclear and physical properties. (author)

Error and Uncertainty in the Accuracy Assessment of Land Cover Maps

Science.gov (United States)

Sarmento, Pedro Alexandre Reis

Traditionally the accuracy assessment of land cover maps is performed through the comparison of these maps with a reference database, which is intended to represent the "real" land cover, being this comparison reported with the thematic accuracy measures through confusion matrixes. Although, these reference databases are also a representation of reality, containing errors due to the human uncertainty in the assignment of the land cover class that best characterizes a certain area, causing bias in the thematic accuracy measures that are reported to the end users of these maps. The main goal of this dissertation is to develop a methodology that allows the integration of human uncertainty present in reference databases in the accuracy assessment of land cover maps, and analyse the impacts that uncertainty may have in the thematic accuracy measures reported to the end users of land cover maps. The utility of the inclusion of human uncertainty in the accuracy assessment of land cover maps is investigated. Specifically we studied the utility of fuzzy sets theory, more precisely of fuzzy arithmetic, for a better understanding of human uncertainty associated to the elaboration of reference databases, and their impacts in the thematic accuracy measures that are derived from confusion matrixes. For this purpose linguistic values transformed in fuzzy intervals that address the uncertainty in the elaboration of reference databases were used to compute fuzzy confusion matrixes. The proposed methodology is illustrated using a case study in which the accuracy assessment of a land cover map for Continental Portugal derived from Medium Resolution Imaging Spectrometer (MERIS) is made. The obtained results demonstrate that the inclusion of human uncertainty in reference databases provides much more information about the quality of land cover maps, when compared with the traditional approach of accuracy assessment of land cover maps. None

Methodology for qualitative uncertainty assessment of climate impact indicators

Science.gov (United States)

Otto, Juliane; Keup-Thiel, Elke; Rechid, Diana; Hänsler, Andreas; Pfeifer, Susanne; Roth, Ellinor; Jacob, Daniela

2016-04-01

The FP7 project "Climate Information Portal for Copernicus" (CLIPC) is developing an integrated platform of climate data services to provide a single point of access for authoritative scientific information on climate change and climate change impacts. In this project, the Climate Service Center Germany (GERICS) has been in charge of the development of a methodology on how to assess the uncertainties related to climate impact indicators. Existing climate data portals mainly treat the uncertainties in two ways: Either they provide generic guidance and/or express with statistical measures the quantifiable fraction of the uncertainty. However, none of the climate data portals give the users a qualitative guidance how confident they can be in the validity of the displayed data. The need for such guidance was identified in CLIPC user consultations. Therefore, we aim to provide an uncertainty assessment that provides the users with climate impact indicator-specific guidance on the degree to which they can trust the outcome. We will present an approach that provides information on the importance of different sources of uncertainties associated with a specific climate impact indicator and how these sources affect the overall 'degree of confidence' of this respective indicator. To meet users requirements in the effective communication of uncertainties, their feedback has been involved during the development process of the methodology. Assessing and visualising the quantitative component of uncertainty is part of the qualitative guidance. As visual analysis method, we apply the Climate Signal Maps (Pfeifer et al. 2015), which highlight only those areas with robust climate change signals. Here, robustness is defined as a combination of model agreement and the significance of the individual model projections. Reference Pfeifer, S., Bülow, K., Gobiet, A., Hänsler, A., Mudelsee, M., Otto, J., Rechid, D., Teichmann, C. and Jacob, D.: Robustness of Ensemble Climate Projections

Assessment of dose measurement uncertainty using RisoScan

International Nuclear Information System (INIS)

Helt-Hansen, Jakob; Miller, Arne

2006-01-01

The dose measurement uncertainty of the dosimeter system RisoScan, office scanner and Riso B3 dosimeters has been assessed by comparison with spectrophotometer measurements of the same dosimeters. The reproducibility and the combined uncertainty were found to be approximately 2% and 4%, respectively, at one standard deviation. The subroutine in RisoScan for electron energy measurement is shown to give results that are equivalent to the measurements with a scanning spectrophotometer

Quantifying uncertainty and trade-offs in resilience assessments

Directory of Open Access Journals (Sweden)

Craig R. Allen

2018-03-01

Full Text Available Several frameworks have been developed to assess the resilience of social-ecological systems, but most require substantial data inputs, time, and technical expertise. Stakeholders and practitioners often lack the resources for such intensive efforts. Furthermore, most end with problem framing and fail to explicitly address trade-offs and uncertainty. To remedy this gap, we developed a rapid survey assessment that compares the relative resilience of social-ecological systems with respect to a number of resilience properties. This approach generates large amounts of information relative to stakeholder inputs. We targeted four stakeholder categories: government (policy, regulation, management, end users (farmers, ranchers, landowners, industry, agency/public science (research, university, extension, and NGOs (environmental, citizen, social justice in four North American watersheds, to assess social-ecological resilience through surveys. Conceptually, social-ecological systems are comprised of components ranging from strictly human to strictly ecological, but that relate directly or indirectly to one another. They have soft boundaries and several important dimensions or axes that together describe the nature of social-ecological interactions, e.g., variability, diversity, modularity, slow variables, feedbacks, capital, innovation, redundancy, and ecosystem services. There is no absolute measure of resilience, so our design takes advantage of cross-watershed comparisons and therefore focuses on relative resilience. Our approach quantifies and compares the relative resilience across watershed systems and potential trade-offs among different aspects of the social-ecological system, e.g., between social, economic, and ecological contributions. This approach permits explicit assessment of several types of uncertainty (e.g., self-assigned uncertainty for stakeholders; uncertainty across respondents, watersheds, and subsystems, and subjectivity in

Uncertainties in environmental radiological assessment models and their implications

International Nuclear Information System (INIS)

Hoffman, F.O.; Miller, C.W.

1983-01-01

Environmental radiological assessments rely heavily on the use of mathematical models. The predictions of these models are inherently uncertain because these models are inexact representations of real systems. The major sources of this uncertainty are related to biases in model formulation and parameter estimation. The best approach for estimating the actual extent of over- or underprediction is model validation, a procedure that requires testing over the range of the intended realm of model application. Other approaches discussed are the use of screening procedures, sensitivity and stochastic analyses, and model comparison. The magnitude of uncertainty in model predictions is a function of the questions asked of the model and the specific radionuclides and exposure pathways of dominant importance. Estimates are made of the relative magnitude of uncertainty for situations requiring predictions of individual and collective risks for both chronic and acute releases of radionuclides. It is concluded that models developed as research tools should be distinguished from models developed for assessment applications. Furthermore, increased model complexity does not necessarily guarantee increased accuracy. To improve the realism of assessment modeling, stochastic procedures are recommended that translate uncertain parameter estimates into a distribution of predicted values. These procedures also permit the importance of model parameters to be ranked according to their relative contribution to the overall predicted uncertainty. Although confidence in model predictions can be improved through site-specific parameter estimation and increased model validation, risk factors and internal dosimetry models will probably remain important contributors to the amount of uncertainty that is irreducible

Uncertainty of Energy Consumption Assessment of Domestic Buildings

DEFF Research Database (Denmark)

Brohus, Henrik; Heiselberg, Per; Simonsen, A.

2009-01-01

In order to assess the influence of energy reduction initiatives, to determine the expected annual cost, to calculate life cycle cost, emission impact, etc. it is crucial to be able to assess the energy consumption reasonably accurate. The present work undertakes a theoretical and empirical study...... of the uncertainty of energy consumption assessment of domestic buildings. The calculated energy consumption of a number of almost identical domestic buildings in Denmark is compared with the measured energy consumption. Furthermore, the uncertainty is determined by means of stochastic modelling based on input...... to correspond reasonably well; however, it is also found that significant differences may occur between calculated and measured energy consumption due to the spread and due to the fact that the result can only be determined with a certain probability. It is found that occupants' behaviour is the major...

Evaluating variability and uncertainty in radiological impact assessment using SYMBIOSE

International Nuclear Information System (INIS)

Simon-Cornu, M.; Beaugelin-Seiller, K.; Boyer, P.; Calmon, P.; Garcia-Sanchez, L.; Mourlon, C.; Nicoulaud, V.; Sy, M.; Gonze, M.A.

2015-01-01

SYMBIOSE is a modelling platform that accounts for variability and uncertainty in radiological impact assessments, when simulating the environmental fate of radionuclides and assessing doses to human populations. The default database of SYMBIOSE is partly based on parameter values that are summarized within International Atomic Energy Agency (IAEA) documents. To characterize uncertainty on the transfer parameters, 331 Probability Distribution Functions (PDFs) were defined from the summary statistics provided within the IAEA documents (i.e. sample size, minimal and maximum values, arithmetic and geometric means, standard and geometric standard deviations) and are made available as spreadsheet files. The methods used to derive the PDFs without complete data sets, but merely the summary statistics, are presented. Then, a simple case-study illustrates the use of the database in a second-order Monte Carlo calculation, separating parametric uncertainty and inter-individual variability. - Highlights: • Parametric uncertainty in radioecology was derived from IAEA documents. • 331 Probability Distribution Functions were defined for transfer parameters. • Parametric uncertainty and inter-individual variability were propagated

Guidance for treatment of variability and uncertainty in ecological risk assessments of contaminated sites

International Nuclear Information System (INIS)

1998-06-01

Uncertainty is a seemingly simple concept that has caused great confusion and conflict in the field of risk assessment. This report offers guidance for the analysis and presentation of variability and uncertainty in ecological risk assessments, an important issue in the remedial investigation and feasibility study processes. This report discusses concepts of probability in terms of variance and uncertainty, describes how these concepts differ in ecological risk assessment from human health risk assessment, and describes probabilistic aspects of specific ecological risk assessment techniques. The report ends with 17 points to consider in performing an uncertainty analysis for an ecological risk assessment of a contaminated site

Exploring the uncertainties in cancer risk assessment using the integrated probabilistic risk assessment (IPRA) approach.

Science.gov (United States)

Slob, Wout; Bakker, Martine I; Biesebeek, Jan Dirk Te; Bokkers, Bas G H

2014-08-01

Current methods for cancer risk assessment result in single values, without any quantitative information on the uncertainties in these values. Therefore, single risk values could easily be overinterpreted. In this study, we discuss a full probabilistic cancer risk assessment approach in which all the generally recognized uncertainties in both exposure and hazard assessment are quantitatively characterized and probabilistically evaluated, resulting in a confidence interval for the final risk estimate. The methodology is applied to three example chemicals (aflatoxin, N-nitrosodimethylamine, and methyleugenol). These examples illustrate that the uncertainty in a cancer risk estimate may be huge, making single value estimates of cancer risk meaningless. Further, a risk based on linear extrapolation tends to be lower than the upper 95% confidence limit of a probabilistic risk estimate, and in that sense it is not conservative. Our conceptual analysis showed that there are two possible basic approaches for cancer risk assessment, depending on the interpretation of the dose-incidence data measured in animals. However, it remains unclear which of the two interpretations is the more adequate one, adding an additional uncertainty to the already huge confidence intervals for cancer risk estimates. © 2014 Society for Risk Analysis.

An assessment of chain management practice within the industrial pork value chain: Beijing Ciity, China

NARCIS (Netherlands)

Wenzhao, M.

2008-01-01

The research assesses the value chain and quality control of the pork industry in Beijing municipality of China. Through interviewing all the actors of pork industrial value chain in Beijing, the roles functions and problems of each of the actor of the pork industrial value chain of Beijing were

Uncertainties on hydrocarbon exploration assessments in both the absence and presence of optioning

International Nuclear Information System (INIS)

Lerche, I.

1998-01-01

For hydrocarbon exploration opportunities a decision tree evaluation including variance in expected value leads to an extra uncertainty on the quality and worth of expected values as a decision device, due to both intrinsic uncertainties in success probability, assessed gains and assessed costs, and to the fact that the expected value is not one of the realizable outcomes. This paper shows how these uncertainty factors can be properly taken into account to provide a revised assessment of worth. In addition, a similar sense of logic prevails when options are considered for an opportunity. The uncertainty and success probability for an optional opportunity are also assessed in terms of the volatility of the maximum option worth. (author)

Assessing flood forecast uncertainty with fuzzy arithmetic

Directory of Open Access Journals (Sweden)

de Bruyn Bertrand

2016-01-01

Full Text Available Providing forecasts for flow rates and water levels during floods have to be associated with uncertainty estimates. The forecast sources of uncertainty are plural. For hydrological forecasts (rainfall-runoff performed using a deterministic hydrological model with basic physics, two main sources can be identified. The first obvious source is the forcing data: rainfall forecast data are supplied in real time by meteorological forecasting services to the Flood Forecasting Service within a range between a lowest and a highest predicted discharge. These two values define an uncertainty interval for the rainfall variable provided on a given watershed. The second source of uncertainty is related to the complexity of the modeled system (the catchment impacted by the hydro-meteorological phenomenon, the number of variables that may describe the problem and their spatial and time variability. The model simplifies the system by reducing the number of variables to a few parameters. Thus it contains an intrinsic uncertainty. This model uncertainty is assessed by comparing simulated and observed rates for a large number of hydro-meteorological events. We propose a method based on fuzzy arithmetic to estimate the possible range of flow rates (and levels of water making a forecast based on possible rainfalls provided by forcing and uncertainty model. The model uncertainty is here expressed as a range of possible values. Both rainfall and model uncertainties are combined with fuzzy arithmetic. This method allows to evaluate the prediction uncertainty range. The Flood Forecasting Service of Oise and Aisne rivers, in particular, monitors the upstream watershed of the Oise at Hirson. This watershed’s area is 310 km2. Its response time is about 10 hours. Several hydrological models are calibrated for flood forecasting in this watershed and use the rainfall forecast. This method presents the advantage to be easily implemented. Moreover, it permits to be carried out

Agricultural Supply Chain Risk Assessment in the Caribbean

OpenAIRE

Arias Carballo, Diego; Laura, dos Reis

2013-01-01

A rapid agricultural supply chain risk assessment, recently developed by the World Bank, constitutes a useful tool for a system-wide approach to identify risks, risk exposure, the severity of potential loses, and options for risk management either by supply chain participants (individually or collectively) or by third parties (government). Supply chain risk management is the systematic pro...

Development and comparison of Bayesian modularization method in uncertainty assessment of hydrological models

Science.gov (United States)

Li, L.; Xu, C.-Y.; Engeland, K.

2012-04-01

With respect to model calibration, parameter estimation and analysis of uncertainty sources, different approaches have been used in hydrological models. Bayesian method is one of the most widely used methods for uncertainty assessment of hydrological models, which incorporates different sources of information into a single analysis through Bayesian theorem. However, none of these applications can well treat the uncertainty in extreme flows of hydrological models' simulations. This study proposes a Bayesian modularization method approach in uncertainty assessment of conceptual hydrological models by considering the extreme flows. It includes a comprehensive comparison and evaluation of uncertainty assessments by a new Bayesian modularization method approach and traditional Bayesian models using the Metropolis Hasting (MH) algorithm with the daily hydrological model WASMOD. Three likelihood functions are used in combination with traditional Bayesian: the AR (1) plus Normal and time period independent model (Model 1), the AR (1) plus Normal and time period dependent model (Model 2) and the AR (1) plus multi-normal model (Model 3). The results reveal that (1) the simulations derived from Bayesian modularization method are more accurate with the highest Nash-Sutcliffe efficiency value, and (2) the Bayesian modularization method performs best in uncertainty estimates of entire flows and in terms of the application and computational efficiency. The study thus introduces a new approach for reducing the extreme flow's effect on the discharge uncertainty assessment of hydrological models via Bayesian. Keywords: extreme flow, uncertainty assessment, Bayesian modularization, hydrological model, WASMOD

Probability and Confidence Trade-space (PACT) Evaluation: Accounting for Uncertainty in Sparing Assessments

Science.gov (United States)

Anderson, Leif; Box, Neil; Carter, Katrina; DiFilippo, Denise; Harrington, Sean; Jackson, David; Lutomski, Michael

2012-01-01

There are two general shortcomings to the current annual sparing assessment: 1. The vehicle functions are currently assessed according to confidence targets, which can be misleading- overly conservative or optimistic. 2. The current confidence levels are arbitrarily determined and do not account for epistemic uncertainty (lack of knowledge) in the ORU failure rate. There are two major categories of uncertainty that impact Sparing Assessment: (a) Aleatory Uncertainty: Natural variability in distribution of actual failures around an Mean Time Between Failure (MTBF) (b) Epistemic Uncertainty : Lack of knowledge about the true value of an Orbital Replacement Unit's (ORU) MTBF We propose an approach to revise confidence targets and account for both categories of uncertainty, an approach we call Probability and Confidence Trade-space (PACT) evaluation.

Analytical Propagation of Uncertainty in Life Cycle Assessment Using Matrix Formulation

DEFF Research Database (Denmark)

Imbeault-Tétreault, Hugues; Jolliet, Olivier; Deschênes, Louise

2013-01-01

with Monte Carlo results. The sensitivity and contribution of input parameters to output uncertainty were also analytically calculated. This article outlines an uncertainty analysis of the comparison between two case study scenarios. We conclude that the analytical method provides a good approximation...... on uncertainty calculation. This article shows the importance of the analytical method in uncertainty calculation, which could lead to a more complete uncertainty analysis in LCA practice....... uncertainty assessment is not a regular step in LCA. An analytical approach based on Taylor series expansion constitutes an effective means to overcome the drawbacks of the Monte Carlo method. This project aimed to test the approach on a real case study, and the resulting analytical uncertainty was compared...

Communicating uncertainty: lessons learned and suggestions for climate change assessment

International Nuclear Information System (INIS)

Patt, A.; Dessai, S.

2005-01-01

Assessments of climate change face the task of making information about uncertainty accessible and useful to decision-makers. The literature in behavior economics provides many examples of how people make decisions under conditions of uncertainty relying on inappropriate heuristics, leading to inconsistent and counterproductive choices. Modern risk communication practices recommend a number of methods to overcome these hurdles, which have been recommended for the Intergovernmental Panel on Climate Change (IPCC) assessment reports. This paper evaluates the success of the most recent IPCC approach to uncertainty communication, based on a controlled survey of climate change experts. Evaluating the results from the survey, and from a similar survey recently conducted among university students, the paper suggests that the most recent IPCC approach leaves open the possibility for biased and inconsistent responses to the information. The paper concludes by suggesting ways to improve the approach for future IPCC assessment reports. (authors)

Uncertainty in Impact Assessment – EIA in Denmark

DEFF Research Database (Denmark)

Larsen, Sanne Vammen

as problematic, as this is important information for decision makers and public actors. Taking point of departure in these issues, this paper seeks to add to the discussions by presenting the results of a study on the handling of uncertainty in Environmental Impact Assessment (EIA) reports in Denmark. The study...... is based on analysis of 100 EIA reports. The results will shed light on the extent to which uncertainties is addressed in EIA in Denmark and discuss how the practice can be categorised....

Standard Review Risk Assessment on Medium-chain and Long-chain Chlorinated paraffin PMN submissions by Dover Chemical

Science.gov (United States)

This assessment was conducted under EPA’s TSCA Section 5 New Chemicals Program. EPA is assessing Medium-chain Chlorinated Paraffin (MCCP) and Long-Chain Chlorinated Paraffin (LCCP) chemicals as part of its New Chemicals Review program.

Standard Review Risk Assessment on Medium-chain and Long-chain Chlorinated paraffin PMN submissions by Qualice, LLC

Science.gov (United States)

This assessment was conducted under EPA’s TSCA Section 5 New Chemicals Program. EPA is assessing Medium-chain Chlorinated Paraffin (MCCP) and Long-Chain Chlorinated Paraffin (LCCP) chemicals as part of its New Chemicals Review program.

Assessing student understanding of measurement and uncertainty

Science.gov (United States)

Jirungnimitsakul, S.; Wattanakasiwich, P.

2017-09-01

The objectives of this study were to develop and assess student understanding of measurement and uncertainty. A test has been adapted and translated from the Laboratory Data Analysis Instrument (LDAI) test, consists of 25 questions focused on three topics including measures of central tendency, experimental errors and uncertainties, and fitting regression lines. The test was evaluated its content validity by three physics experts in teaching physics laboratory. In the pilot study, Thai LDAI was administered to 93 freshmen enrolled in a fundamental physics laboratory course. The final draft of the test was administered to three groups—45 freshmen taking fundamental physics laboratory, 16 sophomores taking intermediated physics laboratory and 21 juniors taking advanced physics laboratory at Chiang Mai University. As results, we found that the freshmen had difficulties in experimental errors and uncertainties. Most students had problems with fitting regression lines. These results will be used to improve teaching and learning physics laboratory for physics students in the department.

A trans-dimensional Bayesian Markov chain Monte Carlo algorithm for model assessment using frequency-domain electromagnetic data

Science.gov (United States)

Minsley, Burke J.

2011-01-01

A meaningful interpretation of geophysical measurements requires an assessment of the space of models that are consistent with the data, rather than just a single, ‘best’ model which does not convey information about parameter uncertainty. For this purpose, a trans-dimensional Bayesian Markov chain Monte Carlo (MCMC) algorithm is developed for assessing frequencydomain electromagnetic (FDEM) data acquired from airborne or ground-based systems. By sampling the distribution of models that are consistent with measured data and any prior knowledge, valuable inferences can be made about parameter values such as the likely depth to an interface, the distribution of possible resistivity values as a function of depth and non-unique relationships between parameters. The trans-dimensional aspect of the algorithm allows the number of layers to be a free parameter that is controlled by the data, where models with fewer layers are inherently favoured, which provides a natural measure of parsimony and a significant degree of flexibility in parametrization. The MCMC algorithm is used with synthetic examples to illustrate how the distribution of acceptable models is affected by the choice of prior information, the system geometry and configuration and the uncertainty in the measured system elevation. An airborne FDEM data set that was acquired for the purpose of hydrogeological characterization is also studied. The results compare favorably with traditional least-squares analysis, borehole resistivity and lithology logs from the site, and also provide new information about parameter uncertainty necessary for model assessment.

Status of uncertainty assessment in k0-NAA measurement. Anything still missing?

International Nuclear Information System (INIS)

Borut Smodis; Tinkara Bucar

2014-01-01

Several approaches to quantifying measurement uncertainty in k 0 -based neutron activation analysis (k 0 -NAA) are reviewed, comprising the original approach, the spreadsheet approach, the dedicated computer program involving analytical calculations and the two k 0 -NAA programs available on the market. Two imperfectness in the dedicated programs are identified, their impact assessed and possible improvements presented for a concrete experimental situation. The status of uncertainty assessment in k 0 -NAA is discussed and steps for improvement are recommended. It is concluded that the present magnitude of measurement uncertainty should further be improved by making additional efforts in reducing uncertainties of the relevant nuclear constants used. (author)

Uncertainty assessing of measure result of tungsten in U3O8 by ICP-AES

International Nuclear Information System (INIS)

Du Guirong; Nie Jie; Tang Lilei

2011-01-01

According as the determining method and the assessing criterion,the uncertainty assessing of measure result of tungsten in U 3 O 8 by ICP-AES is researched. With the assessment of each component in detail, the result shows that u rel (sc)> u rel (c)> u rel (F)> u rel (m) by uncertainty contribution. Other uncertainty is random, calculated by repetition. u rel (sc) is contributed to uncertainty mainly. So the general uncertainty is reduced with strict operation to reduce u rel (sc). (authors)

Assessing performance of flaw characterization methods through uncertainty propagation

Science.gov (United States)

Miorelli, R.; Le Bourdais, F.; Artusi, X.

2018-04-01

In this work, we assess the inversion performance in terms of crack characterization and localization based on synthetic signals associated to ultrasonic and eddy current physics. More precisely, two different standard iterative inversion algorithms are used to minimize the discrepancy between measurements (i.e., the tested data) and simulations. Furthermore, in order to speed up the computational time and get rid of the computational burden often associated to iterative inversion algorithms, we replace the standard forward solver by a suitable metamodel fit on a database built offline. In a second step, we assess the inversion performance by adding uncertainties on a subset of the database parameters and then, through the metamodel, we propagate these uncertainties within the inversion procedure. The fast propagation of uncertainties enables efficiently evaluating the impact due to the lack of knowledge on some parameters employed to describe the inspection scenarios, which is a situation commonly encountered in the industrial NDE context.

Uncertainty Assessments in Fast Neutron Activation Analysis

International Nuclear Information System (INIS)

W. D. James; R. Zeisler

2000-01-01

Fast neutron activation analysis (FNAA) carried out with the use of small accelerator-based neutron generators is routinely used for major/minor element determinations in industry, mineral and petroleum exploration, and to some extent in research. While the method shares many of the operational procedures and therefore errors inherent to conventional thermal neutron activation analysis, its unique implementation gives rise to additional specific concerns that can result in errors or increased uncertainties of measured quantities. The authors were involved in a recent effort to evaluate irreversible incorporation of oxygen into a standard reference material (SRM) by direct measurement of oxygen by FNAA. That project required determination of oxygen in bottles of the SRM stored in varying environmental conditions and a comparison of the results. We recognized the need to accurately describe the total uncertainty of the measurements to accurately characterize any differences in the resulting average concentrations. It is our intent here to discuss the breadth of potential parameters that have the potential to contribute to the random and nonrandom errors of the method and provide estimates of the magnitude of uncertainty introduced. In addition, we will discuss the steps taken in this recent FNAA project to control quality, assess the uncertainty of the measurements, and evaluate results based on the statistical reproducibility

Environmental impact and risk assessments and key factors contributing to the overall uncertainties

International Nuclear Information System (INIS)

Salbu, Brit

2016-01-01

There is a significant number of nuclear and radiological sources that have contributed, are still contributing, or have the potential to contribute to radioactive contamination of the environment in the future. To protect the environment from radioactive contamination, impact and risk assessments are performed prior to or during a release event, short or long term after deposition or prior and after implementation of countermeasures. When environmental impact and risks are assessed, however, a series of factors will contribute to the overall uncertainties. To provide environmental impact and risk assessments, information on processes, kinetics and a series of input variables is needed. Adding problems such as variability, questionable assumptions, gaps in knowledge, extrapolations and poor conceptual model structures, a series of factors are contributing to large and often unacceptable uncertainties in impact and risk assessments. Information on the source term and the release scenario is an essential starting point in impact and risk models; the source determines activity concentrations and atom ratios of radionuclides released, while the release scenario determine the physico-chemical forms of released radionuclides such as particle size distribution, structure and density. Releases will most often contain other contaminants such as metals, and due to interactions, contaminated sites should be assessed as a multiple stressor scenario. Following deposition, a series of stressors, interactions and processes will influence the ecosystem transfer of radionuclide species and thereby influence biological uptake (toxicokinetics) and responses (toxicodynamics) in exposed organisms. Due to the variety of biological species, extrapolation is frequently needed to fill gaps in knowledge e.g., from effects to no effects, from effects in one organism to others, from one stressor to mixtures. Most toxtests are, however, performed as short term exposure of adult organisms

Uncertainties in human health risk assessment of environmental contaminants: A review and perspective.

Science.gov (United States)

Dong, Zhaomin; Liu, Yanju; Duan, Luchun; Bekele, Dawit; Naidu, Ravi

2015-12-01

Addressing uncertainties in human health risk assessment is a critical issue when evaluating the effects of contaminants on public health. A range of uncertainties exist through the source-to-outcome continuum, including exposure assessment, hazard and risk characterisation. While various strategies have been applied to characterising uncertainty, classical approaches largely rely on how to maximise the available resources. Expert judgement, defaults and tools for characterising quantitative uncertainty attempt to fill the gap between data and regulation requirements. The experiences of researching 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) illustrated uncertainty sources and how to maximise available information to determine uncertainties, and thereby provide an 'adequate' protection to contaminant exposure. As regulatory requirements and recurring issues increase, the assessment of complex scenarios involving a large number of chemicals requires more sophisticated tools. Recent advances in exposure and toxicology science provide a large data set for environmental contaminants and public health. In particular, biomonitoring information, in vitro data streams and computational toxicology are the crucial factors in the NexGen risk assessment, as well as uncertainties minimisation. Although in this review we cannot yet predict how the exposure science and modern toxicology will develop in the long-term, current techniques from emerging science can be integrated to improve decision-making. Copyright © 2015 Elsevier Ltd. All rights reserved.

Assessment of supply chain management in Nigerian construction ...

African Journals Online (AJOL)

This paper on assessment of supply Chain Management (SCM) in Effective Project ... in Imo state focused on identifying the challenges of construction supply chain ... and diverse objective were ranked highest whereas unfair risk allocation and poor ... The study recommends that construction stakeholders should embrace ...

Uncertainty and sensitivity analysis on probabilistic safety assessment of an experimental facility

International Nuclear Information System (INIS)

Burgazzi, L.

2000-01-01

The aim of this work is to perform an uncertainty and sensitivity analysis on the probabilistic safety assessment of the International Fusion Materials Irradiation Facility (IFMIF), in order to assess the effect on the final risk values of the uncertainties associated with the generic data used for the initiating events and component reliability and to identify the key quantities contributing to this uncertainty. The analysis is conducted on the expected frequency calculated for the accident sequences, defined through the event tree (ET) modeling. This is in order to increment credit to the ET model quantification, to calculate frequency distributions for the occurrence of events and, consequently, to assess if sequences have been correctly selected on the probability standpoint and finally to verify the fulfillment of the safety conditions. Uncertainty and sensitivity analysis are performed using respectively Monte Carlo sampling and an importance parameter technique. (author)

Standard Review Risk Assessment on Medium-chain and Long-chain Chlorinated paraffin PMN submissions by INEOS Chlor Americas

Science.gov (United States)

This assessment was conducted under EPA’s TSCA Section 5 New Chemicals Program. EPA is assessing Medium-chain Chlorinated Paraffin (MCCP) and Long-Chain Chlorinated Paraffin (LCCP) chemicals as part of its New Chemicals Review program.

Perspectives on dosimetric uncertainties and radiological assessments of radioactive waste management

International Nuclear Information System (INIS)

Smith, G.M.; Pinedo, P.; Cancio, D.

1997-01-01

The purpose of this paper is to raise some issues concerning uncertainties in the estimation of doses of ionizing radiation arising from waste management practices and the contribution to those uncertainties arising from dosimetry modelling. The intentions are: (a) to provide perspective on the relative uncertainties in the different aspects of radiological assessments of waste management; (b) to give pointers as to where resources could best be targeted as regards reduction in overall uncertainties; and (c) to provide regulatory insight to decisions on low dose management as related to waste management practices. (author)

Sensitivity to Uncertainty in Asteroid Impact Risk Assessment

Science.gov (United States)

Mathias, D.; Wheeler, L.; Prabhu, D. K.; Aftosmis, M.; Dotson, J.; Robertson, D. K.

2015-12-01

The Engineering Risk Assessment (ERA) team at NASA Ames Research Center is developing a physics-based impact risk model for probabilistically assessing threats from potential asteroid impacts on Earth. The model integrates probabilistic sampling of asteroid parameter ranges with physics-based analyses of entry, breakup, and impact to estimate damage areas and casualties from various impact scenarios. Assessing these threats is a highly coupled, dynamic problem involving significant uncertainties in the range of expected asteroid characteristics, how those characteristics may affect the level of damage, and the fidelity of various modeling approaches and assumptions. The presented model is used to explore the sensitivity of impact risk estimates to these uncertainties in order to gain insight into what additional data or modeling refinements are most important for producing effective, meaningful risk assessments. In the extreme cases of very small or very large impacts, the results are generally insensitive to many of the characterization and modeling assumptions. However, the nature of the sensitivity can change across moderate-sized impacts. Results will focus on the value of additional information in this critical, mid-size range, and how this additional data can support more robust mitigation decisions.

Uncertainty analysis with a view towards applications in accident consequence assessments

International Nuclear Information System (INIS)

Fischer, F.; Erhardt, J.

1985-09-01

Since the publication of the US-Reactor Safety Study WASH-1400 there has been an increasing interest to develop and apply methods which allow to quantify the uncertainty inherent in probabilistic risk assessments (PRAs) and accident consequence assessments (ACAs) for installations of the nuclear fuel cycle. Research and development in this area is forced by the fact that PRA and ACA are more and more used for comparative, decisive and fact finding studies initiated by industry and regulatory commissions. This report summarizes and reviews some of the main methods and gives some hints to do sensitivity and uncertainty analyses. Some first investigations aiming at the application of the method mentioned above to a submodel of the ACA-code UFOMOD (KfK) are presented. Sensitivity analyses and some uncertainty studies an important submodel of UFOMOD are carried out to identify the relevant parameters for subsequent uncertainty calculations. (orig./HP) [de

Uncertainty analysis in the applications of nuclear probabilistic risk assessment

International Nuclear Information System (INIS)

Le Duy, T.D.

2011-01-01

The aim of this thesis is to propose an approach to model parameter and model uncertainties affecting the results of risk indicators used in the applications of nuclear Probabilistic Risk assessment (PRA). After studying the limitations of the traditional probabilistic approach to represent uncertainty in PRA model, a new approach based on the Dempster-Shafer theory has been proposed. The uncertainty analysis process of the proposed approach consists in five main steps. The first step aims to model input parameter uncertainties by belief and plausibility functions according to the data PRA model. The second step involves the propagation of parameter uncertainties through the risk model to lay out the uncertainties associated with output risk indicators. The model uncertainty is then taken into account in the third step by considering possible alternative risk models. The fourth step is intended firstly to provide decision makers with information needed for decision making under uncertainty (parametric and model) and secondly to identify the input parameters that have significant uncertainty contributions on the result. The final step allows the process to be continued in loop by studying the updating of beliefs functions given new data. The proposed methodology was implemented on a real but simplified application of PRA model. (author)

Uncertainty studies and risk assessment for CO2 storage in geological formations

International Nuclear Information System (INIS)

Walter, Lena Sophie

2013-01-01

Carbon capture and storage (CCS) in deep geological formations is one possible option to mitigate the greenhouse gas effect by reducing CO 2 emissions into the atmosphere. The assessment of the risks related to CO 2 storage is an important task. Events such as CO 2 leakage and brine displacement could result in hazards for human health and the environment. In this thesis, a systematic and comprehensive risk assessment concept is presented to investigate various levels of uncertainties and to assess risks using numerical simulations. Depending on the risk and the processes, which should be assessed, very complex models, large model domains, large time scales, and many simulations runs for estimating probabilities are required. To reduce the resulting high computational costs, a model reduction technique (the arbitrary polynomial chaos expansion) and a method for model coupling in space are applied. The different levels of uncertainties are: statistical uncertainty in parameter distributions, scenario uncertainty, e.g. different geological features, and recognized ignorance due to assumptions in the conceptual model set-up. Recognized ignorance and scenario uncertainty are investigated by simulating well defined model set-ups and scenarios. According to damage values, which are defined as a model output, the set-ups and scenarios can be compared and ranked. For statistical uncertainty probabilities can be determined by running Monte Carlo simulations with the reduced model. The results are presented in various ways: e.g., mean damage, probability density function, cumulative distribution function, or an overall risk value by multiplying the damage with the probability. If the model output (damage) cannot be compared to provided criteria (e.g. water quality criteria), analytical approximations are presented to translate the damage into comparable values. The overall concept is applied for the risks related to brine displacement and infiltration into drinking water

Quantified Uncertainties in Comparative Life Cycle Assessment : What Can Be Concluded?

NARCIS (Netherlands)

Mendoza Beltran, Angelica; Prado, Valentina; Font Vivanco, David; Henriksson, Patrik J.G.; Guinée, Jeroen B.; Heijungs, Reinout

2018-01-01

Interpretation of comparative Life Cycle Assessment (LCA) results can be challenging in the presence of uncertainty. To aid in interpreting such results under the goal of any comparative LCA, we aim to provide guidance to practitioners by gaining insights into uncertainty-statistics methods (USMs).

Risk assessment and management logistics chains

Directory of Open Access Journals (Sweden)

Vladimir Vikulov

2014-03-01

Full Text Available Background: In the context of economic globalization and increasing complexity of economic relations enterprises need methods and techniques to improve and sustain their position on the global market. Integration processes offer business new opportunities, but at the same time present new challenges for the management, including the key objectives of the risk management. Method: On the basis of analysis tools known from the pertinent literature (Supply Chain Management and Supply Chain Risk Management methods, methods of probability theory, methods of risk management, methods of statistics the authors of this paper proposed their own risk assessment method and the method of management of logistics chains. The proposed tool is a specific hybrid of solutions known from the literature. Results: The presented method has been successfully used within the frames of economic-mathematical model of industrial enterprises. Indicators of supply chain risks, including risks caused by supplier are considered in this paper. Authors formed a method of optimizing the level of supply chain risk in the integration with suppliers and customers. Conclusion: Every organization, which starting the process of integration with supplier and customers, needs to use tools, methodologies and techniques for identification of "weak links" in the supply chain. The proposed method allows to fix risk origin places in various links of the supply chain and to identify "weak links" of a logistic chain that may occur in the future. The method is a useful tool for managing not only risks and risk situations, but also to improve the efficiency of current assets management by providing the ability to optimize the level of risk in the current assets management of the industrial enterprise.

Effectiveness of the food recovery at the retailing stage under shelf life uncertainty: An application to Italian food chains.

Science.gov (United States)

Muriana, Cinzia

2015-07-01

Food losses represent a significant issue affecting food supply chains. The possibility of recovering such products can be seen as an effective way to reduce such a phenomenon, improve supply chain performances and ameliorate the conditions of undernourished people. The topic has been already investigated by a previous paper enforcing the hypothesis of deterministic and constant Shelf Life (SL) of products. However, such a model cannot be properly extended to products affected by uncertainties of the SL as it does not take into account the deterioration costs and loss of profits due to the overcoming of the SL within the cycle time. Thus the present paper presents an extension of the previous one under stochastic conditions of the food quality. Differently from the previous publication, this work represents a general model applicable to all supply chains, especially to those managing fresh products characterized by uncertain SL such as fruits and vegetables. The deterioration costs and loss of profits are included in the model and the optimal time at which to withdraw the products from the shelves as well as the quantities to be shipped at each alternative destination have been determined. A comparison of the proposed model with that reported in the previous publication has been carried out in order to underline the impact of the SL variability on the optimality conditions. The results show that the food recovery strategy in the presence of uncertainty of the food quality is rewarding, even if the optimal profit is lower than that of the deterministic case. Copyright © 2015 Elsevier Ltd. All rights reserved.

Risk Assessment and Decision-Making under Uncertainty in Tunnel and Underground Engineering

Directory of Open Access Journals (Sweden)

Yuanpu Xia

2017-10-01

Full Text Available The impact of uncertainty on risk assessment and decision-making is increasingly being prioritized, especially for large geotechnical projects such as tunnels, where uncertainty is often the main source of risk. Epistemic uncertainty, which can be reduced, is the focus of attention. In this study, the existing entropy-risk decision model is first discussed and analyzed, and its deficiencies are improved upon and overcome. Then, this study addresses the fact that existing studies only consider parameter uncertainty and ignore the influence of the model uncertainty. Here, focus is on the issue of model uncertainty and differences in risk consciousness with different decision-makers. The utility theory is introduced in the model. Finally, a risk decision model is proposed based on the sensitivity analysis and the tolerance cost, which can improve decision-making efficiency. This research can provide guidance or reference for the evaluation and decision-making of complex systems engineering problems, and indicate a direction for further research of risk assessment and decision-making issues.

New challenges on uncertainty propagation assessment of flood risk analysis

Science.gov (United States)

Martins, Luciano; Aroca-Jiménez, Estefanía; Bodoque, José M.; Díez-Herrero, Andrés

2016-04-01

Natural hazards, such as floods, cause considerable damage to the human life, material and functional assets every year and around the World. Risk assessment procedures has associated a set of uncertainties, mainly of two types: natural, derived from stochastic character inherent in the flood process dynamics; and epistemic, that are associated with lack of knowledge or the bad procedures employed in the study of these processes. There are abundant scientific and technical literature on uncertainties estimation in each step of flood risk analysis (e.g. rainfall estimates, hydraulic modelling variables); but very few experience on the propagation of the uncertainties along the flood risk assessment. Therefore, epistemic uncertainties are the main goal of this work, in particular,understand the extension of the propagation of uncertainties throughout the process, starting with inundability studies until risk analysis, and how far does vary a proper analysis of the risk of flooding. These methodologies, such as Polynomial Chaos Theory (PCT), Method of Moments or Monte Carlo, are used to evaluate different sources of error, such as data records (precipitation gauges, flow gauges...), hydrologic and hydraulic modelling (inundation estimation), socio-demographic data (damage estimation) to evaluate the uncertainties propagation (UP) considered in design flood risk estimation both, in numerical and cartographic expression. In order to consider the total uncertainty and understand what factors are contributed most to the final uncertainty, we used the method of Polynomial Chaos Theory (PCT). It represents an interesting way to handle to inclusion of uncertainty in the modelling and simulation process. PCT allows for the development of a probabilistic model of the system in a deterministic setting. This is done by using random variables and polynomials to handle the effects of uncertainty. Method application results have a better robustness than traditional analysis

Can Bayesian Belief Networks help tackling conceptual model uncertainties in contaminated site risk assessment?

DEFF Research Database (Denmark)

Troldborg, Mads; Thomsen, Nanna Isbak; McKnight, Ursula S.

different conceptual models may describe the same contaminated site equally well. In many cases, conceptual model uncertainty has been shown to be one of the dominant sources for uncertainty and is therefore essential to account for when quantifying uncertainties in risk assessments. We present here......A key component in risk assessment of contaminated sites is the formulation of a conceptual site model. The conceptual model is a simplified representation of reality and forms the basis for the mathematical modelling of contaminant fate and transport at the site. A conceptual model should...... a Bayesian Belief Network (BBN) approach for evaluating the uncertainty in risk assessment of groundwater contamination from contaminated sites. The approach accounts for conceptual model uncertainty by considering multiple conceptual models, each of which represents an alternative interpretation of the site...

Incorporating rainfall uncertainty in a SWAT model: the river Zenne basin (Belgium) case study

Science.gov (United States)

Tolessa Leta, Olkeba; Nossent, Jiri; van Griensven, Ann; Bauwens, Willy

2013-04-01

The European Union Water Framework Directive (EU-WFD) called its member countries to achieve a good ecological status for all inland and coastal water bodies by 2015. According to recent studies, the river Zenne (Belgium) is far from this objective. Therefore, an interuniversity and multidisciplinary project "Towards a Good Ecological Status in the river Zenne (GESZ)" was launched to evaluate the effects of wastewater management plans on the river. In this project, different models have been developed and integrated using the Open Modelling Interface (OpenMI). The hydrologic, semi-distributed Soil and Water Assessment Tool (SWAT) is hereby used as one of the model components in the integrated modelling chain in order to model the upland catchment processes. The assessment of the uncertainty of SWAT is an essential aspect of the decision making process, in order to design robust management strategies that take the predicted uncertainties into account. Model uncertainty stems from the uncertainties on the model parameters, the input data (e.g, rainfall), the calibration data (e.g., stream flows) and on the model structure itself. The objective of this paper is to assess the first three sources of uncertainty in a SWAT model of the river Zenne basin. For the assessment of rainfall measurement uncertainty, first, we identified independent rainfall periods, based on the daily precipitation and stream flow observations and using the Water Engineering Time Series PROcessing tool (WETSPRO). Secondly, we assigned a rainfall multiplier parameter for each of the independent rainfall periods, which serves as a multiplicative input error corruption. Finally, we treated these multipliers as latent parameters in the model optimization and uncertainty analysis (UA). For parameter uncertainty assessment, due to the high number of parameters of the SWAT model, first, we screened out its most sensitive parameters using the Latin Hypercube One-factor-At-a-Time (LH-OAT) technique

Confronting Uncertainty in Life Cycle Assessment Used for Decision Support

DEFF Research Database (Denmark)

Herrmann, Ivan Tengbjerg; Hauschild, Michael Zwicky; Sohn, Michael D.

2014-01-01

the decision maker (DM) in making the best possible choice for the environment. At present, some DMs do not trust the LCA to be a reliable decisionsupport tool—often because DMs consider the uncertainty of an LCA to be too large. The standard evaluation of uncertainty in LCAs is an ex-post approach that can...... regarding which type of LCA study to employ for the decision context at hand. This taxonomy enables the derivation of an LCA classification matrix to clearly identify and communicate the type of a given LCA. By relating the LCA classification matrix to statistical principles, we can also rank the different......The aim of this article is to help confront uncertainty in life cycle assessments (LCAs) used for decision support. LCAs offer a quantitative approach to assess environmental effects of products, technologies, and services and are conducted by an LCA practitioner or analyst (AN) to support...

Assessment of dose measurement uncertainty using RisøScan

DEFF Research Database (Denmark)

Helt-Hansen, J.; Miller, A.

2006-01-01

The dose measurement uncertainty of the dosimeter system RisoScan, office scanner and Riso B3 dosimeters has been assessed by comparison with spectrophotometer measurements of the same dosimeters. The reproducibility and the combined uncertainty were found to be approximately 2% and 4%, respectiv......%, respectively, at one standard deviation. The subroutine in RisoScan for electron energy measurement is shown to give results that are equivalent to the measurements with a scanning spectrophotometer. (c) 2006 Elsevier Ltd. All rights reserved....

Assessing measurement uncertainty in meteorology in urban environments

International Nuclear Information System (INIS)

Curci, S; Lavecchia, C; Frustaci, G; Pilati, S; Paganelli, C; Paolini, R

2017-01-01

Measurement uncertainty in meteorology has been addressed in a number of recent projects. In urban environments, uncertainty is also affected by local effects which are more difficult to deal with than for synoptic stations. In Italy, beginning in 2010, an urban meteorological network (Climate Network ® ) was designed, set up and managed at national level according to high metrological standards and homogeneity criteria to support energy applications. The availability of such a high-quality operative automatic weather station network represents an opportunity to investigate the effects of station siting and sensor exposure and to estimate the related measurement uncertainty. An extended metadata set was established for the stations in Milan, including siting and exposure details. Statistical analysis on an almost 3-year-long operational period assessed network homogeneity, quality and reliability. Deviations from reference mean values were then evaluated in selected low-gradient local weather situations in order to investigate siting and exposure effects. In this paper the methodology is depicted and preliminary results of its application to air temperature discussed; this allowed the setting of an upper limit of 1 °C for the added measurement uncertainty at the top of the urban canopy layer. (paper)

Assessing measurement uncertainty in meteorology in urban environments

Science.gov (United States)

Curci, S.; Lavecchia, C.; Frustaci, G.; Paolini, R.; Pilati, S.; Paganelli, C.

2017-10-01

Measurement uncertainty in meteorology has been addressed in a number of recent projects. In urban environments, uncertainty is also affected by local effects which are more difficult to deal with than for synoptic stations. In Italy, beginning in 2010, an urban meteorological network (Climate Network®) was designed, set up and managed at national level according to high metrological standards and homogeneity criteria to support energy applications. The availability of such a high-quality operative automatic weather station network represents an opportunity to investigate the effects of station siting and sensor exposure and to estimate the related measurement uncertainty. An extended metadata set was established for the stations in Milan, including siting and exposure details. Statistical analysis on an almost 3-year-long operational period assessed network homogeneity, quality and reliability. Deviations from reference mean values were then evaluated in selected low-gradient local weather situations in order to investigate siting and exposure effects. In this paper the methodology is depicted and preliminary results of its application to air temperature discussed; this allowed the setting of an upper limit of 1 °C for the added measurement uncertainty at the top of the urban canopy layer.

Food-chain and dose model, CALDOS, for assessing Canada's Nuclear Fuel Waste Management concept

International Nuclear Information System (INIS)

Zach, R.; Sheppard, S.C.

1991-01-01

The food-chain and dose model, CALculation of DOSe (CALDOS), was developed for assessing Canada's concept for nuclear fuel waste disposal in a vault deep in crystalline rock of the Canadian Shield. The model is very general and based on the Shield as a whole. The critical group is totally self-sufficient and represented by ICRP (1975) Reference Man for dose prediction. CALDOS assumes steady-state conditions and deals with variation and uncertainty through Monte Carlo simulation techniques. Ingrowth of some radioactive daughters is considered during food-chain transfer. A limit is set on root uptake to avoid unrealistic plant concentrations. Integrated ingestion and inhalation rates of man are calculated in a unique way, based on energy needs. Soil ingestion by man and external exposure from building material are unique pathways considered. Tritium, 129 I, and 222 Rn are treated through special models, and 14 C and 129 I involve unique geosphere dose limits. All transfer coefficients are lognormally distributed, and the plant/soil concentration ratio is correlated with the soil partition coefficient. Animals' ingestion rates are normally distributed and correlated with each other. Comprehensive sets of internal and external dose conversion factors were calculated for CALDOS. Sample calculations show that dose distributions tend to be strongly right-skewed. Many features of CALDOS are relevant for environmental assessment in general

An uncertainty inventory demonstration - a primary step in uncertainty quantification

Energy Technology Data Exchange (ETDEWEB)

Langenbrunner, James R. [Los Alamos National Laboratory; Booker, Jane M [Los Alamos National Laboratory; Hemez, Francois M [Los Alamos National Laboratory; Salazar, Issac F [Los Alamos National Laboratory; Ross, Timothy J [UNM

2009-01-01

Tools, methods, and theories for assessing and quantifying uncertainties vary by application. Uncertainty quantification tasks have unique desiderata and circumstances. To realistically assess uncertainty requires the engineer/scientist to specify mathematical models, the physical phenomena of interest, and the theory or framework for assessments. For example, Probabilistic Risk Assessment (PRA) specifically identifies uncertainties using probability theory, and therefore, PRA's lack formal procedures for quantifying uncertainties that are not probabilistic. The Phenomena Identification and Ranking Technique (PIRT) proceeds by ranking phenomena using scoring criteria that results in linguistic descriptors, such as importance ranked with words, 'High/Medium/Low.' The use of words allows PIRT to be flexible, but the analysis may then be difficult to combine with other uncertainty theories. We propose that a necessary step for the development of a procedure or protocol for uncertainty quantification (UQ) is the application of an Uncertainty Inventory. An Uncertainty Inventory should be considered and performed in the earliest stages of UQ.

Model structural uncertainty quantification and hydrogeophysical data integration using airborne electromagnetic data (Invited)

DEFF Research Database (Denmark)

Minsley, Burke; Christensen, Nikolaj Kruse; Christensen, Steen

of airborne electromagnetic (AEM) data to estimate large-scale model structural geometry, i.e. the spatial distribution of different lithological units based on assumed or estimated resistivity-lithology relationships, and the uncertainty in those structures given imperfect measurements. Geophysically derived...... estimates of model structural uncertainty are then combined with hydrologic observations to assess the impact of model structural error on hydrologic calibration and prediction errors. Using a synthetic numerical model, we describe a sequential hydrogeophysical approach that: (1) uses Bayesian Markov chain...... Monte Carlo (McMC) methods to produce a robust estimate of uncertainty in electrical resistivity parameter values, (2) combines geophysical parameter uncertainty estimates with borehole observations of lithology to produce probabilistic estimates of model structural uncertainty over the entire AEM...

Probabilistic Radiological Performance Assessment Modeling and Uncertainty

Science.gov (United States)

Tauxe, J.

2004-12-01

A generic probabilistic radiological Performance Assessment (PA) model is presented. The model, built using the GoldSim systems simulation software platform, concerns contaminant transport and dose estimation in support of decision making with uncertainty. Both the U.S. Nuclear Regulatory Commission (NRC) and the U.S. Department of Energy (DOE) require assessments of potential future risk to human receptors of disposal of LLW. Commercially operated LLW disposal facilities are licensed by the NRC (or agreement states), and the DOE operates such facilities for disposal of DOE-generated LLW. The type of PA model presented is probabilistic in nature, and hence reflects the current state of knowledge about the site by using probability distributions to capture what is expected (central tendency or average) and the uncertainty (e.g., standard deviation) associated with input parameters, and propagating through the model to arrive at output distributions that reflect expected performance and the overall uncertainty in the system. Estimates of contaminant release rates, concentrations in environmental media, and resulting doses to human receptors well into the future are made by running the model in Monte Carlo fashion, with each realization representing a possible combination of input parameter values. Statistical summaries of the results can be compared to regulatory performance objectives, and decision makers are better informed of the inherently uncertain aspects of the model which supports their decision-making. While this information may make some regulators uncomfortable, they must realize that uncertainties which were hidden in a deterministic analysis are revealed in a probabilistic analysis, and the chance of making a correct decision is now known rather than hoped for. The model includes many typical features and processes that would be part of a PA, but is entirely fictitious. This does not represent any particular site and is meant to be a generic example. A

Modeling sustainability in renewable energy supply chain systems

Science.gov (United States)

Xie, Fei

This dissertation aims at modeling sustainability of renewable fuel supply chain systems against emerging challenges. In particular, the dissertation focuses on the biofuel supply chain system design, and manages to develop advanced modeling framework and corresponding solution methods in tackling challenges in sustaining biofuel supply chain systems. These challenges include: (1) to integrate "environmental thinking" into the long-term biofuel supply chain planning; (2) to adopt multimodal transportation to mitigate seasonality in biofuel supply chain operations; (3) to provide strategies in hedging against uncertainty from conversion technology; and (4) to develop methodologies in long-term sequential planning of the biofuel supply chain under uncertainties. All models are mixed integer programs, which also involves multi-objective programming method and two-stage/multistage stochastic programming methods. In particular for the long-term sequential planning under uncertainties, to reduce the computational challenges due to the exponential expansion of the scenario tree, I also developed efficient ND-Max method which is more efficient than CPLEX and Nested Decomposition method. Through result analysis of four independent studies, it is found that the proposed modeling frameworks can effectively improve the economic performance, enhance environmental benefits and reduce risks due to systems uncertainties for the biofuel supply chain systems.

Bayesian uncertainty quantification for flows in heterogeneous porous media using reversible jump Markov chain Monte Carlo methods

KAUST Repository

Mondal, A.

2010-03-01

In this paper, we study the uncertainty quantification in inverse problems for flows in heterogeneous porous media. Reversible jump Markov chain Monte Carlo algorithms (MCMC) are used for hierarchical modeling of channelized permeability fields. Within each channel, the permeability is assumed to have a lognormal distribution. Uncertainty quantification in history matching is carried out hierarchically by constructing geologic facies boundaries as well as permeability fields within each facies using dynamic data such as production data. The search with Metropolis-Hastings algorithm results in very low acceptance rate, and consequently, the computations are CPU demanding. To speed-up the computations, we use a two-stage MCMC that utilizes upscaled models to screen the proposals. In our numerical results, we assume that the channels intersect the wells and the intersection locations are known. Our results show that the proposed algorithms are capable of capturing the channel boundaries and describe the permeability variations within the channels using dynamic production history at the wells. © 2009 Elsevier Ltd. All rights reserved.

Ecosystem Services Mapping Uncertainty Assessment: A Case Study in the Fitzroy Basin Mining Region

Directory of Open Access Journals (Sweden)

Zhenyu Wang

2018-01-01

Full Text Available Ecosystem services mapping is becoming increasingly popular through the use of various readily available mapping tools, however, uncertainties in assessment outputs are commonly ignored. Uncertainties from different sources have the potential to lower the accuracy of mapping outputs and reduce their reliability for decision-making. Using a case study in an Australian mining region, this paper assessed the impact of uncertainties on the modelling of the hydrological ecosystem service, water provision. Three types of uncertainty were modelled using multiple uncertainty scenarios: (1 spatial data sources; (2 modelling scales (temporal and spatial and (3 parameterization and model selection. We found that the mapping scales can induce significant changes to the spatial pattern of outputs and annual totals of water provision. In addition, differences in parameterization using differing sources from the literature also led to obvious differences in base flow. However, the impact of each uncertainty associated with differences in spatial data sources were not so great. The results of this study demonstrate the importance of uncertainty assessment and highlight that any conclusions drawn from ecosystem services mapping, such as the impacts of mining, are likely to also be a property of the uncertainty in ecosystem services mapping methods.

Reliability assessment of complex electromechanical systems under epistemic uncertainty

International Nuclear Information System (INIS)

Mi, Jinhua; Li, Yan-Feng; Yang, Yuan-Jian; Peng, Weiwen; Huang, Hong-Zhong

2016-01-01

The appearance of macro-engineering and mega-project have led to the increasing complexity of modern electromechanical systems (EMSs). The complexity of the system structure and failure mechanism makes it more difficult for reliability assessment of these systems. Uncertainty, dynamic and nonlinearity characteristics always exist in engineering systems due to the complexity introduced by the changing environments, lack of data and random interference. This paper presents a comprehensive study on the reliability assessment of complex systems. In view of the dynamic characteristics within the system, it makes use of the advantages of the dynamic fault tree (DFT) for characterizing system behaviors. The lifetime of system units can be expressed as bounded closed intervals by incorporating field failures, test data and design expertize. Then the coefficient of variation (COV) method is employed to estimate the parameters of life distributions. An extended probability-box (P-Box) is proposed to convey the present of epistemic uncertainty induced by the incomplete information about the data. By mapping the DFT into an equivalent Bayesian network (BN), relevant reliability parameters and indexes have been calculated. Furthermore, the Monte Carlo (MC) simulation method is utilized to compute the DFT model with consideration of system replacement policy. The results show that this integrated approach is more flexible and effective for assessing the reliability of complex dynamic systems. - Highlights: • A comprehensive study on the reliability assessment of complex system is presented. • An extended probability-box is proposed to convey the present of epistemic uncertainty. • The dynamic fault tree model is built. • Bayesian network and Monte Carlo simulation methods are used. • The reliability assessment of a complex electromechanical system is performed.

Dealing with uncertainty arising out of probabilistic risk assessment

International Nuclear Information System (INIS)

Solomon, K.A.; Kastenberg, W.E.; Nelson, P.F.

1984-03-01

In addressing the area of safety goal implementation, the question of uncertainty arises. This report suggests that the Nuclear Regulatory Commission (NRC) should examine how other regulatory organizations have addressed the issue. Several examples are given from the chemical industry, and comparisons are made to nuclear power risks. Recommendations are made as to various considerations that the NRC should require in probabilistic risk assessments in order to properly treat uncertainties in the implementation of the safety goal policy. 40 references, 7 figures, 5 tables

Understanding uncertainty propagation in life cycle assessments of waste management systems

DEFF Research Database (Denmark)

Bisinella, Valentina; Conradsen, Knut; Christensen, Thomas Højlund

2015-01-01

Uncertainty analysis in Life Cycle Assessments (LCAs) of waste management systems often results obscure and complex, with key parameters rarely determined on a case-by-case basis. The paper shows an application of a simplified approach to uncertainty coupled with a Global Sensitivity Analysis (GSA......) perspective on three alternative waste management systems for Danish single-family household waste. The approach provides a fast and systematic method to select the most important parameters in the LCAs, understand their propagation and contribution to uncertainty....

A risk assessment methodology for incorporating uncertainties using fuzzy concepts

International Nuclear Information System (INIS)

Cho, Hyo-Nam; Choi, Hyun-Ho; Kim, Yoon-Bae

2002-01-01

This paper proposes a new methodology for incorporating uncertainties using fuzzy concepts into conventional risk assessment frameworks. This paper also introduces new forms of fuzzy membership curves, designed to consider the uncertainty range that represents the degree of uncertainties involved in both probabilistic parameter estimates and subjective judgments, since it is often difficult or even impossible to precisely estimate the occurrence rate of an event in terms of one single crisp probability. It is to be noted that simple linguistic variables such as 'High/Low' and 'Good/Bad' have the limitations in quantifying the various risks inherent in construction projects, but only represent subjective mental cognition adequately. Therefore, in this paper, the statements that include some quantification with giving specific value or scale, such as 'Close to any value' or 'Higher/Lower than analyzed value', are used in order to get over the limitations. It may be stated that the proposed methodology will be very useful for the systematic and rational risk assessment of construction projects

Development and comparison in uncertainty assessment based Bayesian modularization method in hydrological modeling

Science.gov (United States)

Li, Lu; Xu, Chong-Yu; Engeland, Kolbjørn

2013-04-01

SummaryWith respect to model calibration, parameter estimation and analysis of uncertainty sources, various regression and probabilistic approaches are used in hydrological modeling. A family of Bayesian methods, which incorporates different sources of information into a single analysis through Bayes' theorem, is widely used for uncertainty assessment. However, none of these approaches can well treat the impact of high flows in hydrological modeling. This study proposes a Bayesian modularization uncertainty assessment approach in which the highest streamflow observations are treated as suspect information that should not influence the inference of the main bulk of the model parameters. This study includes a comprehensive comparison and evaluation of uncertainty assessments by our new Bayesian modularization method and standard Bayesian methods using the Metropolis-Hastings (MH) algorithm with the daily hydrological model WASMOD. Three likelihood functions were used in combination with standard Bayesian method: the AR(1) plus Normal model independent of time (Model 1), the AR(1) plus Normal model dependent on time (Model 2) and the AR(1) plus Multi-normal model (Model 3). The results reveal that the Bayesian modularization method provides the most accurate streamflow estimates measured by the Nash-Sutcliffe efficiency and provide the best in uncertainty estimates for low, medium and entire flows compared to standard Bayesian methods. The study thus provides a new approach for reducing the impact of high flows on the discharge uncertainty assessment of hydrological models via Bayesian method.

Evaluation of uncertainty associated with parameters for long-term safety assessments of geological disposal

International Nuclear Information System (INIS)

Yamaguchi, Tetsuji; Minase, Naofumi; Iida, Yoshihisa; Tanaka, Tadao; Nakayama, Shinichi

2005-01-01

This paper describes the current status of our data acquisition on quantifying uncertainties associated with parameters for safety assessment on groundwater scenarios for geological disposal of radioactive wastes. First, sources of uncertainties and the resulting priority in data acquisition were briefed. Then, the current status of data acquisition for quantifying the uncertainties in assessing solubility, diffusivity in bentonite buffer and distribution coefficient on rocks is introduced. The uncertainty with the solubility estimation is quantified from that associated with thermodynamic data and that in estimating groundwater chemistry. The uncertainty associated with the diffusivity in bentonite buffer is composed of variations of relevant factors such as porosity of the bentonite buffer, montmorillonite content, chemical composition of pore water and temperature. The uncertainty of factors such as the specific surface area of the rock, pH, ionic strength, carbonate concentration in groundwater compose uncertainty of the distribution coefficient of radionuclides on rocks. Based on these investigations, problems to be solved in future studies are summarized. (author)

A Stochastic Programming Approach for a Multi-Site Supply Chain Planning in Textile and Apparel Industry under Demand Uncertainty

Directory of Open Access Journals (Sweden)

Houssem Felfel

2015-11-01

Full Text Available In this study, a new stochastic model is proposed to deal with a multi-product, multi-period, multi-stage, multi-site production and transportation supply chain planning problem under demand uncertainty. A two-stage stochastic linear programming approach is used to maximize the expected profit. Decisions such as the production amount, the inventory level of finished and semi-finished product, the amount of backorder and the quantity of products to be transported between upstream and downstream plants in each period are considered. The robustness of production supply chain plan is then evaluated using statistical and risk measures. A case study from a real textile and apparel industry is shown in order to compare the performances of the proposed stochastic programming model and the deterministic model.

Uncertainty studies and risk assessment for CO{sub 2} storage in geological formations

Energy Technology Data Exchange (ETDEWEB)

Walter, Lena Sophie

2013-07-01

Carbon capture and storage (CCS) in deep geological formations is one possible option to mitigate the greenhouse gas effect by reducing CO{sub 2} emissions into the atmosphere. The assessment of the risks related to CO{sub 2} storage is an important task. Events such as CO{sub 2} leakage and brine displacement could result in hazards for human health and the environment. In this thesis, a systematic and comprehensive risk assessment concept is presented to investigate various levels of uncertainties and to assess risks using numerical simulations. Depending on the risk and the processes, which should be assessed, very complex models, large model domains, large time scales, and many simulations runs for estimating probabilities are required. To reduce the resulting high computational costs, a model reduction technique (the arbitrary polynomial chaos expansion) and a method for model coupling in space are applied. The different levels of uncertainties are: statistical uncertainty in parameter distributions, scenario uncertainty, e.g. different geological features, and recognized ignorance due to assumptions in the conceptual model set-up. Recognized ignorance and scenario uncertainty are investigated by simulating well defined model set-ups and scenarios. According to damage values, which are defined as a model output, the set-ups and scenarios can be compared and ranked. For statistical uncertainty probabilities can be determined by running Monte Carlo simulations with the reduced model. The results are presented in various ways: e.g., mean damage, probability density function, cumulative distribution function, or an overall risk value by multiplying the damage with the probability. If the model output (damage) cannot be compared to provided criteria (e.g. water quality criteria), analytical approximations are presented to translate the damage into comparable values. The overall concept is applied for the risks related to brine displacement and infiltration into

Markov chain Monte Carlo methods for statistical analysis of RF photonic devices

DEFF Research Database (Denmark)

Piels, Molly; Zibar, Darko

2016-01-01

uncertainty is shown to give unsatisfactory and incorrect results due to the nonlinear relationship between the circuit parameters and the measured data. Markov chain Monte Carlo methods are shown to provide superior results, both for individual devices and for assessing within-die variation...

THE UNCERTAINTIES ON THE GIS BASED LAND SUITABILITY ASSESSMENT FOR URBAN AND RURAL PLANNING

Directory of Open Access Journals (Sweden)

H. Liu

2017-09-01

Full Text Available The majority of the research on the uncertainties of spatial data and spatial analysis focuses on some specific data feature or analysis tool. Few have accomplished the uncertainties of the whole process of an application like planning, making the research of uncertainties detached from practical applications. The paper discusses the uncertainties of the geographical information systems (GIS based land suitability assessment in planning on the basis of literature review. The uncertainties considered range from index system establishment to the classification of the final result. Methods to reduce the uncertainties arise from the discretization of continuous raster data and the index weight determination are summarized. The paper analyzes the merits and demerits of the “Nature Breaks” method which is broadly used by planners. It also explores the other factors which impact the accuracy of the final classification like the selection of class numbers, intervals and the autocorrelation of the spatial data. In the conclusion part, the paper indicates that the adoption of machine learning methods should be modified to integrate the complexity of land suitability assessment. The work contributes to the application of spatial data and spatial analysis uncertainty research on land suitability assessment, and promotes the scientific level of the later planning and decision-making.

Quantification of Wave Model Uncertainties Used for Probabilistic Reliability Assessments of Wave Energy Converters

DEFF Research Database (Denmark)

Ambühl, Simon; Kofoed, Jens Peter; Sørensen, John Dalsgaard

2015-01-01

Wave models used for site assessments are subjected to model uncertainties, which need to be quantified when using wave model results for probabilistic reliability assessments. This paper focuses on determination of wave model uncertainties. Four different wave models are considered, and validation...... data are collected from published scientific research. The bias and the root-mean-square error, as well as the scatter index, are considered for the significant wave height as well as the mean zero-crossing wave period. Based on an illustrative generic example, this paper presents how the quantified...... uncertainties can be implemented in probabilistic reliability assessments....

Determination of Wave Model Uncertainties used for Probabilistic Reliability Assessments of Wave Energy Devices

DEFF Research Database (Denmark)

Ambühl, Simon; Kofoed, Jens Peter; Sørensen, John Dalsgaard

2014-01-01

Wave models used for site assessments are subject to model uncertainties, which need to be quantified when using wave model results for probabilistic reliability assessments. This paper focuses on determination of wave model uncertainties. Considered are four different wave models and validation...... data is collected from published scientific research. The bias, the root-mean-square error as well as the scatter index are considered for the significant wave height as well as the mean zero-crossing wave period. Based on an illustrative generic example it is shown how the estimated uncertainties can...... be implemented in probabilistic reliability assessments....

On economic resolution and uncertainty in hydrocarbon exploration assessment

International Nuclear Information System (INIS)

Lerche, I.

1998-01-01

When assessment of parameters of a decision tree for a hydrocarbon exploration project can lie within estimated ranges, it is shown that the ensemble average expected value has two sorts of uncertainties: one is due to the expected value of each realization of the decision tree being different than the average; the second is due to intrinsic variance of each decision tree. The total standard error of the average expected value combines both sorts. The use of additional statistical measures, such as standard error, volatility, and cumulative probability of making a profit, provide insight into the selection process leading to a more appropriate decision. In addition, the use of relative contributions and relative importance for the uncertainty measures guides one to a better determination of those parameters that dominantly influence the total ensemble uncertainty. In this way one can concentrate resources on efforts to minimize the uncertainty ranges of such dominant parameters. A numerical illustration is provided to indicate how such calculations can be performed simply with a hand calculator. (author)

Impact of Climate Change on high and low flows across Great Britain: a temporal analysis and uncertainty assessment.

Science.gov (United States)

Beevers, Lindsay; Collet, Lila

2017-04-01

Over the past decade there have been significant challenges to water management posed by both floods and droughts. In the UK, since 2000 flooding has caused over £5Bn worth of damage, and direct costs from the recent drought (2011-12) are estimated to be between £70-165M, arising from impacts on public and industrial water supply. Projections of future climate change suggest an increase in temperature and precipitation trends which may exacerbate the frequency and severity of such hazards, but there is significant uncertainty associated with these projections. It thus becomes urgent to assess the possible impact of these changes on extreme flows and evaluate the uncertainties related to these projections, particularly changes in the seasonality of such hazards. This paper aims to assess the changes in seasonality of peak and low flows across Great Britain as a result of climate change. It is based on the Future Flow database; an 11-member ensemble of transient river flow projections from January 1951 to December 2098. We analyse the daily river flow over the baseline (1961-1990) and the 2080s (2069-2098) for 281 gauging stations. For each ensemble member, annual maxima (AMAX) and minima (AMIN) are extracted for both time periods for each gauging station. The month of the year the AMAX and AMIN occur respectively are recorded for each of the 30 years in the past and the future time periods. The uncertainty of the AMAX and AMIN occurrence temporally (monthly) is assessed across the 11 ensemble members, as well as the changes to this temporal signal between the baseline and the 2080s. Ultimately, this work gives a national picture (spatially) of high and low flows occurrence temporally and allows the assessment of possible changes in hydrological dynamics as a result of climate change in a statistical framework. Results will quantify the uncertainty related to the Climate Model parameters which are cascaded into the modelling chain. This study highlights the issues

A review of the uncertainties in the assessment of radiological consequences of spent nuclear fuel disposal

International Nuclear Information System (INIS)

Wiborgh, M.; Elert, M.; Hoeglund, L.O.; Jones, C.; Grundfelt, B.; Skagius, K.; Bengtsson, A.

1992-06-01

Radioactive waste disposal systems for spent nuclear fuel are designed to isolate the radioactive waste from the human environment for long period of time. The isolation is provided by a combination of engineered and natural barriers. Safety assessments are performed to describe and quantify the performance of the individual barriers and the disposal system over long-term periods. These assessments will always be associated with uncertainties. Uncertainties can originate from the variability of natural systems and will also be introduced in the predictive modelling performed to quantitatively evaluate the behaviour of the disposal system as a consequence of the incomplete knowledge about the governing processes. Uncertainties in safety assessments can partly be reduced by additional measurements and research. The aim of this study has been to identify uncertainties in assessments of radiological consequences from the disposal of spent nuclear fuel based on the Swedish KBS-3 concept. The identified uncertainties have been classified with respect to their origin, i.e. in conceptual, modelling and data uncertainties. The possibilities to reduce the uncertainties are also commented upon. In assessments it is important to decrease uncertainties which are of major importance for the performance of the disposal system. These could to some extent be identified by uncertainty analysis. However, conceptual uncertainties and some type of model uncertainties are difficult to evaluate. To be able to decrease uncertainties in conceptual models, it is essential that the processes describing and influencing the radionuclide transport in the engineered and natural barriers are sufficiently understood. In this study a qualitative approach has been used. The importance of different barriers and processes are indicated by their influence on the release of some representative radionuclides. (122 refs.) (au)

How to quantify uncertainty and variability in life cycle assessment: the case of greenhouse gas emissions of gas power generation in the US

Science.gov (United States)

Hauck, M.; Steinmann, Z. J. N.; Laurenzi, I. J.; Karuppiah, R.; Huijbregts, M. A. J.

2014-07-01

This study quantified the contributions of uncertainty and variability to the range of life-cycle greenhouse gas (LCGHG) emissions associated with conventional gas-fired electricity generation in the US. Whereas uncertainty is defined as lack of knowledge and can potentially be reduced by additional research, variability is an inherent characteristic of supply chains and cannot be reduced without physically modifying the system. The life-cycle included four stages: production, processing, transmission and power generation, and utilized a functional unit of 1 kWh of electricity generated at plant. Technological variability requires analyses of life cycles of individual power plants, e.g. combined cycle plants or boilers. Parameter uncertainty was modeled via Monte Carlo simulation. Our approach reveals that technological differences are the predominant cause for the range of LCGHG emissions associated with gas power, primarily due to variability in plant efficiencies. Uncertainties in model parameters played a minor role for 100 year time horizon. Variability in LCGHG emissions was a factor of 1.4 for combined cycle plants, and a factor of 1.3 for simple cycle plants (95% CI, 100 year horizon). The results can be used to assist decision-makers in assessing factors that contribute to LCGHG emissions despite uncertainties in parameters employed to estimate those emissions.

How to quantify uncertainty and variability in life cycle assessment: the case of greenhouse gas emissions of gas power generation in the US

International Nuclear Information System (INIS)

Hauck, M; Steinmann, Z J N; Huijbregts, M A J; Laurenzi, I J; Karuppiah, R

2014-01-01

This study quantified the contributions of uncertainty and variability to the range of life-cycle greenhouse gas (LCGHG) emissions associated with conventional gas-fired electricity generation in the US. Whereas uncertainty is defined as lack of knowledge and can potentially be reduced by additional research, variability is an inherent characteristic of supply chains and cannot be reduced without physically modifying the system. The life-cycle included four stages: production, processing, transmission and power generation, and utilized a functional unit of 1 kWh of electricity generated at plant. Technological variability requires analyses of life cycles of individual power plants, e.g. combined cycle plants or boilers. Parameter uncertainty was modeled via Monte Carlo simulation. Our approach reveals that technological differences are the predominant cause for the range of LCGHG emissions associated with gas power, primarily due to variability in plant efficiencies. Uncertainties in model parameters played a minor role for 100 year time horizon. Variability in LCGHG emissions was a factor of 1.4 for combined cycle plants, and a factor of 1.3 for simple cycle plants (95% CI, 100 year horizon). The results can be used to assist decision-makers in assessing factors that contribute to LCGHG emissions despite uncertainties in parameters employed to estimate those emissions. (letter)

An introductory guide to uncertainty analysis in environmental and health risk assessment. Environmental Restoration Program

International Nuclear Information System (INIS)

Hammonds, J.S.; Hoffman, F.O.; Bartell, S.M.

1994-12-01

This report presents guidelines for evaluating uncertainty in mathematical equations and computer models applied to assess human health and environmental risk. Uncertainty analyses involve the propagation of uncertainty in model parameters and model structure to obtain confidence statements for the estimate of risk and identify the model components of dominant importance. Uncertainty analyses are required when there is no a priori knowledge about uncertainty in the risk estimate and when there is a chance that the failure to assess uncertainty may affect the selection of wrong options for risk reduction. Uncertainty analyses are effective when they are conducted in an iterative mode. When the uncertainty in the risk estimate is intolerable for decision-making, additional data are acquired for the dominant model components that contribute most to uncertainty. This process is repeated until the level of residual uncertainty can be tolerated. A analytical and numerical methods for error propagation are presented along with methods for identifying the most important contributors to uncertainty. Monte Carlo simulation with either Simple Random Sampling (SRS) or Latin Hypercube Sampling (LHS) is proposed as the most robust method for propagating uncertainty through either simple or complex models. A distinction is made between simulating a stochastically varying assessment endpoint (i.e., the distribution of individual risks in an exposed population) and quantifying uncertainty due to lack of knowledge about a fixed but unknown quantity (e.g., a specific individual, the maximally exposed individual, or the mean, median, or 95%-tile of the distribution of exposed individuals). Emphasis is placed on the need for subjective judgement to quantify uncertainty when relevant data are absent or incomplete

A Conceptual Methodology for Assessing Acquisition Requirements Robustness against Technology Uncertainties

Science.gov (United States)

Chou, Shuo-Ju

2011-12-01

In recent years the United States has shifted from a threat-based acquisition policy that developed systems for countering specific threats to a capabilities-based strategy that emphasizes the acquisition of systems that provide critical national defense capabilities. This shift in policy, in theory, allows for the creation of an "optimal force" that is robust against current and future threats regardless of the tactics and scenario involved. In broad terms, robustness can be defined as the insensitivity of an outcome to "noise" or non-controlled variables. Within this context, the outcome is the successful achievement of defense strategies and the noise variables are tactics and scenarios that will be associated with current and future enemies. Unfortunately, a lack of system capability, budget, and schedule robustness against technology performance and development uncertainties has led to major setbacks in recent acquisition programs. This lack of robustness stems from the fact that immature technologies have uncertainties in their expected performance, development cost, and schedule that cause to variations in system effectiveness and program development budget and schedule requirements. Unfortunately, the Technology Readiness Assessment process currently used by acquisition program managers and decision-makers to measure technology uncertainty during critical program decision junctions does not adequately capture the impact of technology performance and development uncertainty on program capability and development metrics. The Technology Readiness Level metric employed by the TRA to describe program technology elements uncertainties can only provide a qualitative and non-descript estimation of the technology uncertainties. In order to assess program robustness, specifically requirements robustness, against technology performance and development uncertainties, a new process is needed. This process should provide acquisition program managers and decision

Uncertainty characterization approaches for risk assessment of DBPs in drinking water: a review.

Science.gov (United States)

Chowdhury, Shakhawat; Champagne, Pascale; McLellan, P James

2009-04-01

The management of risk from disinfection by-products (DBPs) in drinking water has become a critical issue over the last three decades. The areas of concern for risk management studies include (i) human health risk from DBPs, (ii) disinfection performance, (iii) technical feasibility (maintenance, management and operation) of treatment and disinfection approaches, and (iv) cost. Human health risk assessment is typically considered to be the most important phase of the risk-based decision-making or risk management studies. The factors associated with health risk assessment and other attributes are generally prone to considerable uncertainty. Probabilistic and non-probabilistic approaches have both been employed to characterize uncertainties associated with risk assessment. The probabilistic approaches include sampling-based methods (typically Monte Carlo simulation and stratified sampling) and asymptotic (approximate) reliability analysis (first- and second-order reliability methods). Non-probabilistic approaches include interval analysis, fuzzy set theory and possibility theory. However, it is generally accepted that no single method is suitable for the entire spectrum of problems encountered in uncertainty analyses for risk assessment. Each method has its own set of advantages and limitations. In this paper, the feasibility and limitations of different uncertainty analysis approaches are outlined for risk management studies of drinking water supply systems. The findings assist in the selection of suitable approaches for uncertainty analysis in risk management studies associated with DBPs and human health risk.

Collaborative framework for PIV uncertainty quantification: comparative assessment of methods

International Nuclear Information System (INIS)

Sciacchitano, Andrea; Scarano, Fulvio; Neal, Douglas R; Smith, Barton L; Warner, Scott O; Vlachos, Pavlos P; Wieneke, Bernhard

2015-01-01

A posteriori uncertainty quantification of particle image velocimetry (PIV) data is essential to obtain accurate estimates of the uncertainty associated with a given experiment. This is particularly relevant when measurements are used to validate computational models or in design and decision processes. In spite of the importance of the subject, the first PIV uncertainty quantification (PIV-UQ) methods have been developed only in the last three years. The present work is a comparative assessment of four approaches recently proposed in the literature: the uncertainty surface method (Timmins et al 2012), the particle disparity approach (Sciacchitano et al 2013), the peak ratio criterion (Charonko and Vlachos 2013) and the correlation statistics method (Wieneke 2015). The analysis is based upon experiments conducted for this specific purpose, where several measurement techniques are employed simultaneously. The performances of the above approaches are surveyed across different measurement conditions and flow regimes. (paper)

Using sequential indicator simulation to assess the uncertainty of delineating heavy-metal contaminated soils

International Nuclear Information System (INIS)

Juang, Kai-Wei; Chen, Yue-Shin; Lee, Dar-Yuan

2004-01-01

Mapping the spatial distribution of soil pollutants is essential for delineating contaminated areas. Currently, geostatistical interpolation, kriging, is increasingly used to estimate pollutant concentrations in soils. The kriging-based approach, indicator kriging (IK), may be used to model the uncertainty of mapping. However, a smoothing effect is usually produced when using kriging in pollutant mapping. The detailed spatial patterns of pollutants could, therefore, be lost. The local uncertainty of mapping pollutants derived by the IK technique is referred to as the conditional cumulative distribution function (ccdf) for one specific location (i.e. single-location uncertainty). The local uncertainty information obtained by IK is not sufficient as the uncertainty of mapping at several locations simultaneously (i.e. multi-location uncertainty or spatial uncertainty) is required to assess the reliability of the delineation of contaminated areas. The simulation approach, sequential indicator simulation (SIS), which has the ability to model not only single, but also multi-location uncertainties, was used, in this study, to assess the uncertainty of the delineation of heavy metal contaminated soils. To illustrate this, a data set of Cu concentrations in soil from Taiwan was used. The results show that contour maps of Cu concentrations generated by the SIS realizations exhausted all the spatial patterns of Cu concentrations without the smoothing effect found when using the kriging method. Based on the SIS realizations, the local uncertainty of Cu concentrations at a specific location of x', refers to the probability of the Cu concentration z(x') being higher than the defined threshold level of contamination (z c ). This can be written as Prob SIS [z(x')>z c ], representing the probability of contamination. The probability map of Prob SIS [z(x')>z c ] can then be used for delineating contaminated areas. In addition, the multi-location uncertainty of an area A

Communicating uncertainty in hydrological forecasts: mission impossible?

Science.gov (United States)

Ramos, Maria-Helena; Mathevet, Thibault; Thielen, Jutta; Pappenberger, Florian

2010-05-01

Cascading uncertainty in meteo-hydrological modelling chains for forecasting and integrated flood risk assessment is an essential step to improve the quality of hydrological forecasts. Although the best methodology to quantify the total predictive uncertainty in hydrology is still debated, there is a common agreement that one must avoid uncertainty misrepresentation and miscommunication, as well as misinterpretation of information by users. Several recent studies point out that uncertainty, when properly explained and defined, is no longer unwelcome among emergence response organizations, users of flood risk information and the general public. However, efficient communication of uncertain hydro-meteorological forecasts is far from being a resolved issue. This study focuses on the interpretation and communication of uncertain hydrological forecasts based on (uncertain) meteorological forecasts and (uncertain) rainfall-runoff modelling approaches to decision-makers such as operational hydrologists and water managers in charge of flood warning and scenario-based reservoir operation. An overview of the typical flow of uncertainties and risk-based decisions in hydrological forecasting systems is presented. The challenges related to the extraction of meaningful information from probabilistic forecasts and the test of its usefulness in assisting operational flood forecasting are illustrated with the help of two case-studies: 1) a study on the use and communication of probabilistic flood forecasting within the European Flood Alert System; 2) a case-study on the use of probabilistic forecasts by operational forecasters from the hydroelectricity company EDF in France. These examples show that attention must be paid to initiatives that promote or reinforce the active participation of expert forecasters in the forecasting chain. The practice of face-to-face forecast briefings, focusing on sharing how forecasters interpret, describe and perceive the model output forecasted

Environmental impact and risk assessments and key factors contributing to the overall uncertainties.

Science.gov (United States)

Salbu, Brit

2016-01-01

There is a significant number of nuclear and radiological sources that have contributed, are still contributing, or have the potential to contribute to radioactive contamination of the environment in the future. To protect the environment from radioactive contamination, impact and risk assessments are performed prior to or during a release event, short or long term after deposition or prior and after implementation of countermeasures. When environmental impact and risks are assessed, however, a series of factors will contribute to the overall uncertainties. To provide environmental impact and risk assessments, information on processes, kinetics and a series of input variables is needed. Adding problems such as variability, questionable assumptions, gaps in knowledge, extrapolations and poor conceptual model structures, a series of factors are contributing to large and often unacceptable uncertainties in impact and risk assessments. Information on the source term and the release scenario is an essential starting point in impact and risk models; the source determines activity concentrations and atom ratios of radionuclides released, while the release scenario determine the physico-chemical forms of released radionuclides such as particle size distribution, structure and density. Releases will most often contain other contaminants such as metals, and due to interactions, contaminated sites should be assessed as a multiple stressor scenario. Following deposition, a series of stressors, interactions and processes will influence the ecosystem transfer of radionuclide species and thereby influence biological uptake (toxicokinetics) and responses (toxicodynamics) in exposed organisms. Due to the variety of biological species, extrapolation is frequently needed to fill gaps in knowledge e.g., from effects to no effects, from effects in one organism to others, from one stressor to mixtures. Most toxtests are, however, performed as short term exposure of adult organisms

Uncertainty assessment of urban pluvial flood risk in a context of climate change adaptation decision making

DEFF Research Database (Denmark)

Arnbjerg-Nielsen, Karsten; Zhou, Qianqian

2014-01-01

uncertainty analysis, which can assess and quantify the overall uncertainty in relation to climate change adaptation to urban flash floods. The analysis is based on an uncertainty cascade that by means of Monte Carlo simulations of flood risk assessments incorporates climate change impacts as a key driver......There has been a significant increase in climatic extremes in many regions. In Central and Northern Europe, this has led to more frequent and more severe floods. Along with improved flood modelling technologies this has enabled development of economic assessment of climate change adaptation...... to increasing urban flood risk. Assessment of adaptation strategies often requires a comprehensive risk-based economic analysis of current risk, drivers of change of risk over time, and measures to reduce the risk. However, such studies are often associated with large uncertainties. The uncertainties arise from...

Risk Management in Biopharmaceutical Supply Chains

OpenAIRE

Ma, Yao

2011-01-01

Biopharmaceutical supply chains present considerable complexity issue for the formulation of optimal plans due to significant uncertainty in the supply chain. The primary goal of biopharmaceutical supply chain planning is to provide reliable supply to patients while coping with various supply chain risks. In chapter 1 first I discuss the key elements and basic characteristics of the biopharmaceutical supply chain . Then I present the major challenges in biopharmaceutical supply chain planning...

Holistic uncertainty analysis in river basin modeling for climate vulnerability assessment

Science.gov (United States)

Taner, M. U.; Wi, S.; Brown, C.

2017-12-01

The challenges posed by uncertain future climate are a prominent concern for water resources managers. A number of frameworks exist for assessing the impacts of climate-related uncertainty, including internal climate variability and anthropogenic climate change, such as scenario-based approaches and vulnerability-based approaches. While in many cases climate uncertainty may be dominant, other factors such as future evolution of the river basin, hydrologic response and reservoir operations are potentially significant sources of uncertainty. While uncertainty associated with modeling hydrologic response has received attention, very little attention has focused on the range of uncertainty and possible effects of the water resources infrastructure and management. This work presents a holistic framework that allows analysis of climate, hydrologic and water management uncertainty in water resources systems analysis with the aid of a water system model designed to integrate component models for hydrology processes and water management activities. The uncertainties explored include those associated with climate variability and change, hydrologic model parameters, and water system operation rules. A Bayesian framework is used to quantify and model the uncertainties at each modeling steps in integrated fashion, including prior and the likelihood information about model parameters. The framework is demonstrated in a case study for the St. Croix Basin located at border of United States and Canada.

Uncertainty Estimate in Resources Assessment: A Geostatistical Contribution

International Nuclear Information System (INIS)

Souza, Luis Eduardo de; Costa, Joao Felipe C. L.; Koppe, Jair C.

2004-01-01

For many decades the mining industry regarded resources/reserves estimation and classification as a mere calculation requiring basic mathematical and geological knowledge. Most methods were based on geometrical procedures and spatial data distribution. Therefore, uncertainty associated with tonnages and grades either were ignored or mishandled, although various mining codes require a measure of confidence in the values reported. Traditional methods fail in reporting the level of confidence in the quantities and grades. Conversely, kriging is known to provide the best estimate and its associated variance. Among kriging methods, Ordinary Kriging (OK) probably is the most widely used one for mineral resource/reserve estimation, mainly because of its robustness and its facility in uncertainty assessment by using the kriging variance. It also is known that OK variance is unable to recognize local data variability, an important issue when heterogeneous mineral deposits with higher and poorer grade zones are being evaluated. Alternatively, stochastic simulation are used to build local or global uncertainty about a geological attribute respecting its statistical moments. This study investigates methods capable of incorporating uncertainty to the estimates of resources and reserves via OK and sequential gaussian and sequential indicator simulation The results showed that for the type of mineralization studied all methods classified the tonnages similarly. The methods are illustrated using an exploration drill hole data sets from a large Brazilian coal deposit

Supply Chain Maturity Assessment of Coca Cola Ghana Ltd

OpenAIRE

Tetteh, Quaye

2015-01-01

This study aims to assess the supply chain process management of Coca Cola Ghana Ltd; this research is of importance due to its potential to point out processes that can yield higher performance of the supply chain and those processes that can be enhanced for higher per-formance levels. The sample for this study was selected using purposive sampling; five supply chain man-agement practitioners in the company were used for the data extraction. Questionnaires were sent to these five respond...

Flood risk assessment and robust management under deep uncertainty: Application to Dhaka City

Science.gov (United States)

Mojtahed, Vahid; Gain, Animesh Kumar; Giupponi, Carlo

2014-05-01

The socio-economic changes as well as climatic changes have been the main drivers of uncertainty in environmental risk assessment and in particular flood. The level of future uncertainty that researchers face when dealing with problems in a future perspective with focus on climate change is known as Deep Uncertainty (also known as Knightian uncertainty), since nobody has already experienced and undergone those changes before and our knowledge is limited to the extent that we have no notion of probabilities, and therefore consolidated risk management approaches have limited potential.. Deep uncertainty is referred to circumstances that analysts and experts do not know or parties to decision making cannot agree on: i) the appropriate models describing the interaction among system variables, ii) probability distributions to represent uncertainty about key parameters in the model 3) how to value the desirability of alternative outcomes. The need thus emerges to assist policy-makers by providing them with not a single and optimal solution to the problem at hand, such as crisp estimates for the costs of damages of natural hazards considered, but instead ranges of possible future costs, based on the outcomes of ensembles of assessment models and sets of plausible scenarios. Accordingly, we need to substitute optimality as a decision criterion with robustness. Under conditions of deep uncertainty, the decision-makers do not have statistical and mathematical bases to identify optimal solutions, while instead they should prefer to implement "robust" decisions that perform relatively well over all conceivable outcomes out of all future unknown scenarios. Under deep uncertainty, analysts cannot employ probability theory or other statistics that usually can be derived from observed historical data and therefore, we turn to non-statistical measures such as scenario analysis. We construct several plausible scenarios with each scenario being a full description of what may happen

Value chain assesment in a CCS business development setting

Energy Technology Data Exchange (ETDEWEB)

Hektor, Erik A.; Lyngroth, Steinar; Midtsund, Marte Aaberg; Bratfos, Hans A.

2010-09-15

Carbon Capture and Storage (CCS) is perceived by many as a necessary bridge to a sustainable future solely based on renewable energy. However, one of the barriers to the commercial implementation of CCS is cost. Today's cost estimates are high due to the large amount of uncertainty relating to this new technology and hence restrain the utility sector from investing in the development of CCS and making it a viable business. This paper presents Value Chain Assessment (VCA) as a powerful tool to help understand how such uncertainties influence the NPV for the various stakeholders in CCS development projects.

Assessment of volcanic hazards, vulnerability, risk and uncertainty (Invited)

Science.gov (United States)

Sparks, R. S.

2009-12-01

many sources of uncertainty in forecasting the areas that volcanic activity will effect and the severity of the effects. Uncertainties arise from: natural variability, inadequate data, biased data, incomplete data, lack of understanding of the processes, limitations to predictive models, ambiguity, and unknown unknowns. The description of volcanic hazards is thus necessarily probabilistic and requires assessment of the attendant uncertainties. Several issues arise from the probabilistic nature of volcanic hazards and the intrinsic uncertainties. Although zonation maps require well-defined boundaries for administrative pragmatism, such boundaries cannot divide areas that are completely safe from those that are unsafe. Levels of danger or safety need to be defined to decide on and justify boundaries through the concepts of vulnerability and risk. More data, better observations, improved models may reduce uncertainties, but can increase uncertainties and may lead to re-appraisal of zone boundaries. Probabilities inferred by statistical techniques are hard to communicate. Expert elicitation is an emerging methodology for risk assessment and uncertainty evaluation. The method has been applied at one major volcanic crisis (Soufrière Hills Volcano, Montserrat), and is being applied in planning for volcanic crises at Vesuvius.

Decomposing the uncertainty in climate impact projections of Dynamic Vegetation Models: a test with the forest models LANDCLIM and FORCLIM

Science.gov (United States)

Cailleret, Maxime; Snell, Rebecca; von Waldow, Harald; Kotlarski, Sven; Bugmann, Harald

2015-04-01

Different levels of uncertainty should be considered in climate impact projections by Dynamic Vegetation Models (DVMs), particularly when it comes to managing climate risks. Such information is useful to detect the key processes and uncertainties in the climate model - impact model chain and may be used to support recommendations for future improvements in the simulation of both climate and biological systems. In addition, determining which uncertainty source is dominant is an important aspect to recognize the limitations of climate impact projections by a multi-model ensemble mean approach. However, to date, few studies have clarified how each uncertainty source (baseline climate data, greenhouse gas emission scenario, climate model, and DVM) affects the projection of ecosystem properties. Focusing on one greenhouse gas emission scenario, we assessed the uncertainty in the projections of a forest landscape model (LANDCLIM) and a stand-scale forest gap model (FORCLIM) that is caused by linking climate data with an impact model. LANDCLIM was used to assess the uncertainty in future landscape properties of the Visp valley in Switzerland that is due to (i) the use of different 'baseline' climate data (gridded data vs. data from weather stations), and (ii) differences in climate projections among 10 GCM-RCM chains. This latter point was also considered for the projections of future forest properties by FORCLIM at several sites along an environmental gradient in Switzerland (14 GCM-RCM chains), for which we also quantified the uncertainty caused by (iii) the model chain specific statistical properties of the climate time-series, and (iv) the stochasticity of the demographic processes included in the model, e.g., the annual number of saplings that establish, or tree mortality. Using methods of variance decomposition analysis, we found that (i) The use of different baseline climate data strongly impacts the prediction of forest properties at the lowest and highest, but

An Integrated Approach for Characterization of Uncertainty in Complex Best Estimate Safety Assessment

International Nuclear Information System (INIS)

Pourgol-Mohamad, Mohammad; Modarres, Mohammad; Mosleh, Ali

2013-01-01

This paper discusses an approach called Integrated Methodology for Thermal-Hydraulics Uncertainty Analysis (IMTHUA) to characterize and integrate a wide range of uncertainties associated with the best estimate models and complex system codes used for nuclear power plant safety analyses. Examples of applications include complex thermal hydraulic and fire analysis codes. In identifying and assessing uncertainties, the proposed methodology treats the complex code as a 'white box', thus explicitly treating internal sub-model uncertainties in addition to the uncertainties related to the inputs to the code. The methodology accounts for uncertainties related to experimental data used to develop such sub-models, and efficiently propagates all uncertainties during best estimate calculations. Uncertainties are formally analyzed and probabilistically treated using a Bayesian inference framework. This comprehensive approach presents the results in a form usable in most other safety analyses such as the probabilistic safety assessment. The code output results are further updated through additional Bayesian inference using any available experimental data, for example from thermal hydraulic integral test facilities. The approach includes provisions to account for uncertainties associated with user-specified options, for example for choices among alternative sub-models, or among several different correlations. Complex time-dependent best-estimate calculations are computationally intense. The paper presents approaches to minimize computational intensity during the uncertainty propagation. Finally, the paper will report effectiveness and practicality of the methodology with two applications to a complex thermal-hydraulics system code as well as a complex fire simulation code. In case of multiple alternative models, several techniques, including dynamic model switching, user-controlled model selection, and model mixing, are discussed. (authors)

Uncertainty characteristics of EPA's ground-water transport model for low-level waste performance assessment

International Nuclear Information System (INIS)

Yim, Man-Sung

1995-01-01

Performance assessment is an essential step either in design or in licensing processes to ensure the safety of any proposed radioactive waste disposal facilities. Since performance assessment requires the use of computer codes, understanding the characteristics of computer models used and the uncertainties of the estimated results is important. The PRESTO-EPA code, which was the basis of the Environmental Protection Agency's analysis for low-level-waste rulemaking, is widely used for various performance assessment activities in the country with no adequate information available for the uncertainty characteristics of the results. In this study, the groundwater transport model PRESTO-EPA was examined based on the analysis of 14 C transport along with the investigation of uncertainty characteristics

Adaptive Markov Chain Monte Carlo

KAUST Repository

Jadoon, Khan

2016-08-08

A substantial interpretation of electromagnetic induction (EMI) measurements requires quantifying optimal model parameters and uncertainty of a nonlinear inverse problem. For this purpose, an adaptive Bayesian Markov chain Monte Carlo (MCMC) algorithm is used to assess multi-orientation and multi-offset EMI measurements in an agriculture field with non-saline and saline soil. In the MCMC simulations, posterior distribution was computed using Bayes rule. The electromagnetic forward model based on the full solution of Maxwell\\'s equations was used to simulate the apparent electrical conductivity measured with the configurations of EMI instrument, the CMD mini-Explorer. The model parameters and uncertainty for the three-layered earth model are investigated by using synthetic data. Our results show that in the scenario of non-saline soil, the parameters of layer thickness are not well estimated as compared to layers electrical conductivity because layer thicknesses in the model exhibits a low sensitivity to the EMI measurements, and is hence difficult to resolve. Application of the proposed MCMC based inversion to the field measurements in a drip irrigation system demonstrate that the parameters of the model can be well estimated for the saline soil as compared to the non-saline soil, and provide useful insight about parameter uncertainty for the assessment of the model outputs.

A tactical supply chain planning model with multiple flexibility options

DEFF Research Database (Denmark)

Esmaeilikia, Masoud; Fahimnia, Behnam; Sarkis, Joeseph

2016-01-01

Supply chain flexibility is widely recognized as an approach to manage uncertainty. Uncertainty in the supply chain may arise from a number of sources such as demand and supply interruptions and lead time variability. A tactical supply chain planning model with multiple flexibility options...... incorporated in sourcing, manufacturing and logistics functions can be used for the analysis of flexibility adjustment in an existing supply chain. This paper develops such a tactical supply chain planning model incorporating a realistic range of flexibility options. A novel solution method is designed...

Operational Implementation of a Pc Uncertainty Construct for Conjunction Assessment Risk Analysis

Science.gov (United States)

Newman, Lauri K.; Hejduk, Matthew D.; Johnson, Lauren C.

2016-01-01

Earlier this year the NASA Conjunction Assessment and Risk Analysis (CARA) project presented the theoretical and algorithmic aspects of a method to include the uncertainties in the calculation inputs when computing the probability of collision (Pc) between two space objects, principally uncertainties in the covariances and the hard-body radius. The output of this calculation approach is to produce rather than a single Pc value an entire probability density function that will represent the range of possible Pc values given the uncertainties in the inputs and bring CA risk analysis methodologies more in line with modern risk management theory. The present study provides results from the exercise of this method against an extended dataset of satellite conjunctions in order to determine the effect of its use on the evaluation of conjunction assessment (CA) event risk posture. The effects are found to be considerable: a good number of events are downgraded from or upgraded to a serious risk designation on the basis of consideration of the Pc uncertainty. The findings counsel the integration of the developed methods into NASA CA operations.

Nonlinear Uncertainty Propagation of Satellite State Error for Tracking and Conjunction Risk Assessment

Science.gov (United States)

2017-12-18

AFRL-RV-PS- AFRL-RV-PS- TR-2017-0177 TR-2017-0177 NONLINEAR UNCERTAINTY PROPAGATION OF SATELLITE STATE ERROR FOR TRACKING AND CONJUNCTION RISK...Uncertainty Propagation of Satellite State Error for Tracking and Conjunction Risk Assessment 5a. CONTRACT NUMBER FA9453-16-1-0084 5b. GRANT NUMBER...prediction and satellite conjunction analysis. Statistical approach utilizes novel methods to build better uncertainty state characterization in the context

Simplified quantitative treatment of uncertainty and interindividual variability in health risk assessment

International Nuclear Information System (INIS)

Bogen, K.T.

1993-01-01

A distinction between uncertainty (or the extent of lack of knowledge) and interindividual variability (or the extent of person-to-person heterogeneity) regarding the values of input variates must be maintained if a quantitative characterization of uncertainty in population risk or in individual risk is sought. Here, some practical methods are presented that should facilitate implementation of the analytic framework for uncertainty and variability proposed by Bogen and Spear. (1,2) Two types of methodology are discussed: one that facilitates the distinction between uncertainty and variability per se, and another that may be used to simplify quantitative analysis of distributed inputs representing either uncertainty or variability. A simple and a complex form for modeled increased risk are presented and then used to illustrate methods facilitating the distinction between uncertainty and variability in reference to characterization of both population and individual risk. Finally, a simple form of discrete probability calculus is proposed as an easily implemented, practical altemative to Monte-Carlo based procedures to quantitative integration of uncertainty and variability in risk assessment

Performance Assessment Uncertainty Analysis for Japan's HLW Program Feasibility Study (H12)

International Nuclear Information System (INIS)

BABA, T.; ISHIGURO, K.; ISHIHARA, Y.; SAWADA, A.; UMEKI, H.; WAKASUGI, K.; WEBB, ERIK K.

1999-01-01

Most HLW programs in the world recognize that any estimate of long-term radiological performance must be couched in terms of the uncertainties derived from natural variation, changes through time and lack of knowledge about the essential processes. The Japan Nuclear Cycle Development Institute followed a relatively standard procedure to address two major categories of uncertainty. First, a FEatures, Events and Processes (FEPs) listing, screening and grouping activity was pursued in order to define the range of uncertainty in system processes as well as possible variations in engineering design. A reference and many alternative cases representing various groups of FEPs were defined and individual numerical simulations performed for each to quantify the range of conceptual uncertainty. Second, parameter distributions were developed for the reference case to represent the uncertainty in the strength of these processes, the sequencing of activities and geometric variations. Both point estimates using high and low values for individual parameters as well as a probabilistic analysis were performed to estimate parameter uncertainty. A brief description of the conceptual model uncertainty analysis is presented. This paper focuses on presenting the details of the probabilistic parameter uncertainty assessment

Competition of two supply chains with different risk structures: applying market research option

Directory of Open Access Journals (Sweden)

A. Hafezolkotob

2012-01-01

Full Text Available Demand uncertainty obliges all participants through a supply chain to make decisions under uncertainty. These decisions extend across price, investment, production, and inventory quantities. We take account of competition between two supply chains under demand uncertainty. These chains internally are involved in vertical pricing competition; however, they externally participate in horizontal pricing and service level competitions by offering a single-type product to the market. Since firms may have various attitudes against demand uncertainty and its related risks, different risk structures for competitive supply chains are considered. We assume that risk-averse firms are able to decrease demand uncertainty by information gathered from market research. For risk-averse participants in a chain, market research investment is an appropriate ground for vertical coordination, which diminishes risk through a supply chain. Optimal strategies based on game theory are obtained for different risk structures; furthermore, for each structure the effects of risk sensitivity as well as market research efficiency on these optimal strategies are investigated. Finally, we propose two scenarios for information sharing between risk-averse participants.

On the proper use of Ensembles for Predictive Uncertainty assessment

Science.gov (United States)

Todini, Ezio; Coccia, Gabriele; Ortiz, Enrique

2015-04-01

uncertainty of the ensemble mean and that of the ensemble spread. The results of this new approach are illustrated by using data and forecasts from an operational real time flood forecasting. Coccia, G. and Todini, E. 2011. Recent developments in predictive uncertainty assessment based on the Model Conditional Processor approach. Hydrology and Earth System Sciences, 15, 3253-3274. doi:10.5194/hess-15-3253-2011. Krzysztofowicz, R. 1999 Bayesian theory of probabilistic forecasting via deterministic hydrologic model, Water Resour. Res., 35, 2739-2750. Raftery, A. E., T. Gneiting, F. Balabdaoui, and M. Polakowski, 2005. Using Bayesian model averaging to calibrate forecast ensembles, Mon. Weather Rev., 133, 1155-1174. Reggiani, P., Renner, M., Weerts, A., and van Gelder, P., 2009. Uncertainty assessment via Bayesian revision of ensemble streamflow predictions in the operational river Rhine forecasting system, Water Resour. Res., 45, W02428, doi:10.1029/2007WR006758. Todini E. 2004. Role and treatment of uncertainty in real-time flood forecasting. Hydrological Processes 18(14), 2743_2746 Todini, E. 2008. A model conditional processor to assess predictive uncertainty in flood forecasting. Intl. J. River Basin Management, 6(2): 123-137.

Risk assessment of Short and Medium Chain Chlorinated Paraffin’s (SCCP and MCCP)

DEFF Research Database (Denmark)

Christensen, Frans Møller; Olsen, Stig Irving

2002-01-01

findings of the Short Chain Chlorinated Paraffin (SCCP) and the draft Medium Chain Chlorinated Paraffin (MCCP) risk assessments. The political actions taken as a consequence of the assessments are also described. The risk assessments have been prepared according to the EU Technical Guidance Document (TGD...

Evaluation of uncertainties in selected environmental dispersion models

International Nuclear Information System (INIS)

Little, C.A.; Miller, C.W.

1979-01-01

Compliance with standards of radiation dose to the general public has necessitated the use of dispersion models to predict radionuclide concentrations in the environment due to releases from nuclear facilities. Because these models are only approximations of reality and because of inherent variations in the input parameters used in these models, their predictions are subject to uncertainty. Quantification of this uncertainty is necessary to assess the adequacy of these models for use in determining compliance with protection standards. This paper characterizes the capabilities of several dispersion models to predict accurately pollutant concentrations in environmental media. Three types of models are discussed: aquatic or surface water transport models, atmospheric transport models, and terrestrial and aquatic food chain models. Using data published primarily by model users, model predictions are compared to observations

Effect of user interpretation on uncertainty estimates: examples from the air-to-milk transfer of radiocesium

International Nuclear Information System (INIS)

Kirchner, G.; Ring Peterson, S.; Bergstroem, U.; Bushell, S.; Davis, P.; Filistovic, V.; Hinton, T.G.; Krajewski, P.; Riesen, T.; Uijt de Haag, P.

1998-01-01

An important source of uncertainty in predictions of numerical simulation codes of environmental transport processes arises from the assumptions made by the user when interpreting the model and the scenario to be assessed. This type of uncertainty was examined systematically in this study and was compared with uncertainty due to varying parameter values in a code. Three terrestrial food chain codes that are driven by deposition of radionuclides from the atmosphere were used by up to ten participants to predict total deposition of 137 Cs and concentrations on pasture and in milk for two release scenarios. Collective uncertainty among the predictions of the ten users for concentrations in milk calculated for one scenario by one code was a factor of 2000, while the largest individual uncertainty was 20 times lower. Choice of parameter values contributed most to user-induced uncertainty, followed by scenario interpretation. Due to the significant disparity in predictions, it is recommended that assessments should not be carried out alone by a single code user. (Copyright (c) 1998 Elsevier Science B.V., Amsterdam. All rights reserved.)

Managing geological uncertainty in CO2-EOR reservoir assessments

Science.gov (United States)

Welkenhuysen, Kris; Piessens, Kris

2014-05-01

Recently the European Parliament has agreed that an atlas for the storage potential of CO2 is of high importance to have a successful commercial introduction of CCS (CO2 capture and geological storage) technology in Europe. CO2-enhanced oil recovery (CO2-EOR) is often proposed as a promising business case for CCS, and likely has a high potential in the North Sea region. Traditional economic assessments for CO2-EOR largely neglect the geological reality of reservoir uncertainties because these are difficult to introduce realistically in such calculations. There is indeed a gap between the outcome of a reservoir simulation and the input values for e.g. cost-benefit evaluations, especially where it concerns uncertainty. The approach outlined here is to turn the procedure around, and to start from which geological data is typically (or minimally) requested for an economic assessment. Thereafter it is evaluated how this data can realistically be provided by geologists and reservoir engineers. For the storage of CO2 these parameters are total and yearly CO2 injection capacity, and containment or potential on leakage. Specifically for the EOR operation, two additional parameters can be defined: the EOR ratio, or the ratio of recovered oil over injected CO2, and the CO2 recycling ratio of CO2 that is reproduced after breakthrough at the production well. A critical but typically estimated parameter for CO2-EOR projects is the EOR ratio, taken in this brief outline as an example. The EOR ratio depends mainly on local geology (e.g. injection per well), field design (e.g. number of wells), and time. Costs related to engineering can be estimated fairly good, given some uncertainty range. The problem is usually to reliably estimate the geological parameters that define the EOR ratio. Reliable data is only available from (onshore) CO2-EOR projects in the US. Published studies for the North Sea generally refer to these data in a simplified form, without uncertainty ranges, and are

Overview of methods for uncertainty analysis and sensitivity analysis in probabilistic risk assessment

International Nuclear Information System (INIS)

Iman, R.L.; Helton, J.C.

1985-01-01

Probabilistic Risk Assessment (PRA) is playing an increasingly important role in the nuclear reactor regulatory process. The assessment of uncertainties associated with PRA results is widely recognized as an important part of the analysis process. One of the major criticisms of the Reactor Safety Study was that its representation of uncertainty was inadequate. The desire for the capability to treat uncertainties with the MELCOR risk code being developed at Sandia National Laboratories is indicative of the current interest in this topic. However, as yet, uncertainty analysis and sensitivity analysis in the context of PRA is a relatively immature field. In this paper, available methods for uncertainty analysis and sensitivity analysis in a PRA are reviewed. This review first treats methods for use with individual components of a PRA and then considers how these methods could be combined in the performance of a complete PRA. In the context of this paper, the goal of uncertainty analysis is to measure the imprecision in PRA outcomes of interest, and the goal of sensitivity analysis is to identify the major contributors to this imprecision. There are a number of areas that must be considered in uncertainty analysis and sensitivity analysis for a PRA: (1) information, (2) systems analysis, (3) thermal-hydraulic phenomena/fission product behavior, (4) health and economic consequences, and (5) display of results. Each of these areas and the synthesis of them into a complete PRA are discussed

Development of probabilistic assessment methodology for geologic disposal of radioactive wastes

International Nuclear Information System (INIS)

Kimura, H.; Takahashi, T.

1998-01-01

The probabilistic assessment methodology is essential to evaluate uncertainties of long-term radiological consequences associated with geologic disposal of radioactive wastes. We have developed a probabilistic assessment methodology to estimate the influences of parameter uncertainties/variabilities. An exposure scenario considered here is based on a groundwater migration scenario. A computer code system GSRW-PSA thus developed is based on a non site-specific model, and consists of a set of sub modules for sampling of model parameters, calculating the release of radionuclides from engineered barriers, calculating the transport of radionuclides through the geosphere, calculating radiation exposures of the public, and calculating the statistical values relating the uncertainties and sensitivities. The results of uncertainty analyses for α-nuclides quantitatively indicate that natural uranium ( 238 U) concentration is suitable for an alternative safety indicator of long-lived radioactive waste disposal, because the estimated range of individual dose equivalent due to 238 U decay chain is narrower that that due to other decay chain ( 237 Np decay chain). It is internationally necessary to have detailed discussion on the PDF of model parameters and the PSA methodology to evaluated the uncertainties due to conceptual models and scenarios. (author)

Accounting for multiple sources of uncertainty in impact assessments: The example of the BRACE study

Science.gov (United States)

O'Neill, B. C.

2015-12-01

Assessing climate change impacts often requires the use of multiple scenarios, types of models, and data sources, leading to a large number of potential sources of uncertainty. For example, a single study might require a choice of a forcing scenario, climate model, bias correction and/or downscaling method, societal development scenario, model (typically several) for quantifying elements of societal development such as economic and population growth, biophysical model (such as for crop yields or hydrology), and societal impact model (e.g. economic or health model). Some sources of uncertainty are reduced or eliminated by the framing of the question. For example, it may be useful to ask what an impact outcome would be conditional on a given societal development pathway, forcing scenario, or policy. However many sources of uncertainty remain, and it is rare for all or even most of these sources to be accounted for. I use the example of a recent integrated project on the Benefits of Reduced Anthropogenic Climate changE (BRACE) to explore useful approaches to uncertainty across multiple components of an impact assessment. BRACE comprises 23 papers that assess the differences in impacts between two alternative climate futures: those associated with Representative Concentration Pathways (RCPs) 4.5 and 8.5. It quantifies difference in impacts in terms of extreme events, health, agriculture, tropical cyclones, and sea level rise. Methodologically, it includes climate modeling, statistical analysis, integrated assessment modeling, and sector-specific impact modeling. It employs alternative scenarios of both radiative forcing and societal development, but generally uses a single climate model (CESM), partially accounting for climate uncertainty by drawing heavily on large initial condition ensembles. Strengths and weaknesses of the approach to uncertainty in BRACE are assessed. Options under consideration for improving the approach include the use of perturbed physics

Measurement, simulation and uncertainty assessment of implant heating during MRI

International Nuclear Information System (INIS)

Neufeld, E; Kuehn, S; Kuster, N; Szekely, G

2009-01-01

The heating of tissues around implants during MRI can pose severe health risks, and careful evaluation is required for leads to be labeled as MR conditionally safe. A recent interlaboratory comparison study has shown that different groups can produce widely varying results (sometimes with more than a factor of 5 difference) when performing measurements according to current guidelines. To determine the related difficulties and to derive optimized procedures, two different generic lead structures have been investigated in this study by using state-of-the-art temperature and dosimetric probes, as well as simulations for which detailed uncertainty budgets have been determined. The agreement between simulations and measurements is well within the combined uncertainty. The study revealed that the uncertainty can be kept below 17% if appropriate instrumentation and procedures are applied. Optimized experimental assessment techniques can be derived from the findings presented herein.

Measurement, simulation and uncertainty assessment of implant heating during MRI

Energy Technology Data Exchange (ETDEWEB)

Neufeld, E; Kuehn, S; Kuster, N [Foundation for Research on Information Technologies in Society (IT' IS), Zeughausstr. 43, 8004 Zurich (Switzerland); Szekely, G [Computer Vision Laboratory, Swiss Federal Institute of Technology (ETHZ), Sternwartstr 7, ETH Zentrum, 8092 Zurich (Switzerland)], E-mail: neufeld@itis.ethz.ch

2009-07-07

The heating of tissues around implants during MRI can pose severe health risks, and careful evaluation is required for leads to be labeled as MR conditionally safe. A recent interlaboratory comparison study has shown that different groups can produce widely varying results (sometimes with more than a factor of 5 difference) when performing measurements according to current guidelines. To determine the related difficulties and to derive optimized procedures, two different generic lead structures have been investigated in this study by using state-of-the-art temperature and dosimetric probes, as well as simulations for which detailed uncertainty budgets have been determined. The agreement between simulations and measurements is well within the combined uncertainty. The study revealed that the uncertainty can be kept below 17% if appropriate instrumentation and procedures are applied. Optimized experimental assessment techniques can be derived from the findings presented herein.

Quality in environmental science for policy: Assessing uncertainty as a component of policy analysis

International Nuclear Information System (INIS)

Maxim, Laura; Sluijs, Jeroen P. van der

2011-01-01

The sheer number of attempts to define and classify uncertainty reveals an awareness of its importance in environmental science for policy, though the nature of uncertainty is often misunderstood. The interdisciplinary field of uncertainty analysis is unstable; there are currently several incomplete notions of uncertainty leading to different and incompatible uncertainty classifications. One of the most salient shortcomings of present-day practice is that most of these classifications focus on quantifying uncertainty while ignoring the qualitative aspects that tend to be decisive in the interface between science and policy. Consequently, the current practices of uncertainty analysis contribute to increasing the perceived precision of scientific knowledge, but do not adequately address its lack of socio-political relevance. The 'positivistic' uncertainty analysis models (like those that dominate the fields of climate change modelling and nuclear or chemical risk assessment) have little social relevance, as they do not influence negotiations between stakeholders. From the perspective of the science-policy interface, the current practices of uncertainty analysis are incomplete and incorrectly focused. We argue that although scientific knowledge produced and used in a context of political decision-making embodies traditional scientific characteristics, it also holds additional properties linked to its influence on social, political, and economic relations. Therefore, the significance of uncertainty cannot be assessed based on quality criteria that refer to the scientific content only; uncertainty must also include quality criteria specific to the properties and roles of this scientific knowledge within political, social, and economic contexts and processes. We propose a conceptual framework designed to account for such substantive, contextual, and procedural criteria of knowledge quality. At the same time, the proposed framework includes and synthesizes the various

Assessing uncertainty in high-resolution spatial climate data across the US Northeast.

Science.gov (United States)

Bishop, Daniel A; Beier, Colin M

2013-01-01

Local and regional-scale knowledge of climate change is needed to model ecosystem responses, assess vulnerabilities and devise effective adaptation strategies. High-resolution gridded historical climate (GHC) products address this need, but come with multiple sources of uncertainty that are typically not well understood by data users. To better understand this uncertainty in a region with a complex climatology, we conducted a ground-truthing analysis of two 4 km GHC temperature products (PRISM and NRCC) for the US Northeast using 51 Cooperative Network (COOP) weather stations utilized by both GHC products. We estimated GHC prediction error for monthly temperature means and trends (1980-2009) across the US Northeast and evaluated any landscape effects (e.g., elevation, distance from coast) on those prediction errors. Results indicated that station-based prediction errors for the two GHC products were similar in magnitude, but on average, the NRCC product predicted cooler than observed temperature means and trends, while PRISM was cooler for means and warmer for trends. We found no evidence for systematic sources of uncertainty across the US Northeast, although errors were largest at high elevations. Errors in the coarse-scale (4 km) digital elevation models used by each product were correlated with temperature prediction errors, more so for NRCC than PRISM. In summary, uncertainty in spatial climate data has many sources and we recommend that data users develop an understanding of uncertainty at the appropriate scales for their purposes. To this end, we demonstrate a simple method for utilizing weather stations to assess local GHC uncertainty and inform decisions among alternative GHC products.

Assessment of uncertainty in full core reactor physics calculations using statistical methods

International Nuclear Information System (INIS)

McEwan, C.

2012-01-01

The best estimate method of safety analysis involves choosing a realistic set of input parameters for a proposed safety case and evaluating the uncertainty in the results. Determining the uncertainty in code outputs remains a challenge and is the subject of a benchmarking exercise proposed by the Organization for Economic Cooperation and Development. The work proposed in this paper will contribute to this benchmark by assessing the uncertainty in a depletion calculation of the final nuclide concentrations for an experiment performed in the Fukushima-2 reactor. This will be done using lattice transport code DRAGON and a tool known as DINOSAUR. (author)

Assessment of uncertainty in full core reactor physics calculations using statistical methods

Energy Technology Data Exchange (ETDEWEB)

McEwan, C., E-mail: mcewac2@mcmaster.ca [McMaster Univ., Hamilton, Ontario (Canada)

2012-07-01

The best estimate method of safety analysis involves choosing a realistic set of input parameters for a proposed safety case and evaluating the uncertainty in the results. Determining the uncertainty in code outputs remains a challenge and is the subject of a benchmarking exercise proposed by the Organization for Economic Cooperation and Development. The work proposed in this paper will contribute to this benchmark by assessing the uncertainty in a depletion calculation of the final nuclide concentrations for an experiment performed in the Fukushima-2 reactor. This will be done using lattice transport code DRAGON and a tool known as DINOSAUR. (author)

Quantifying remarks to the question of uncertainties of the 'general dose assessment fundamentals'

International Nuclear Information System (INIS)

Brenk, H.D.; Vogt, K.J.

1982-12-01

Dose prediction models are always subject to uncertainties due to a number of factors including deficiencies in the model structure and uncertainties of the model input parameter values. In lieu of validation experiments the evaluation of these uncertainties is restricted to scientific judgement. Several attempts have been made in the literature to evaluate the uncertainties of the current dose assessment models resulting from uncertainties of the model input parameter values using stochastic approaches. Less attention, however, has been paid to potential sources of systematic over- and underestimations of the predicted doses due to deficiencies in the model structure. The present study addresses this aspect with regard to dose assessment models currently used for regulatory purposes. The influence of a number of basic simplifications and conservative assumptions has been investigated. Our systematic approach is exemplified by a comparison of doses evaluated on the basis of the regulatory guide model and a more realistic model respectively. This is done for 3 critical exposure pathways. As a result of this comparison it can be concluded that the currently used regularoty-type models include significant safety factors resulting in a systematic overprediction of dose to man up to two orders of magnitude. For this reason there are some indications that these models usually more than compensate the bulk of the stochastic uncertainties caused by the variability of the input parameter values. (orig.) [de

Assessing cold chain status in a metro city of India: an intervention study.

Science.gov (United States)

Mallik, S; Mandal, P K; Chatterjee, C; Ghosh, P; Manna, N; Chakrabarty, D; Bagchi, S N; Dasgupta, S

2011-03-01

Cold chain maintenance is an essential activity to maintain the potency of vaccines and to prevent adverse events following immunization. One baseline study highlighted the unsatisfactory cold chain status in city of Kolkata in India. To assess the changes which occurred in the cold chain status after the intervention undertaken to improve the status and also to assess the awareness of the cold chain handlers regarding cold chain maintenance. Intervention consisted of reorganization of cold chain points and training of health manpower in Kolkata Municipal area regarding immunization and cold chain following the guidelines as laid by Govt of India. Reevaluation of cold chain status was done at 20 institutions selected by stratified systematic random sampling after the intervention. The results were compared with baseline survey. Significant improvement had been observed in correct placing of cold chain equipment, maintenance of stock security, orderly placing of ice packs, diluents and vaccines inside the equipment, temperature recording and maintenance. But awareness and skill of cold chain handlers regarding basics of cold chain maintenance was not satisfactory. The success of intervention included significant improvement of cold chain status including creation of a designated cold chain handler. The gaps lay in non-availability of non-electrical cold chain equipment and separate cold chain room, policy makers should stress. Cold chain handlers need reorientation training regarding heat & cold sensitive vaccines, preventive maintenance and correct contingency plan.

Uncertainty assessment of PM2.5 contamination mapping using spatiotemporal sequential indicator simulations and multi-temporal monitoring data

Science.gov (United States)

Yang, Yong; Christakos, George; Huang, Wei; Lin, Chengda; Fu, Peihong; Mei, Yang

2016-04-01

Because of the rapid economic growth in China, many regions are subjected to severe particulate matter pollution. Thus, improving the methods of determining the spatiotemporal distribution and uncertainty of air pollution can provide considerable benefits when developing risk assessments and environmental policies. The uncertainty assessment methods currently in use include the sequential indicator simulation (SIS) and indicator kriging techniques. However, these methods cannot be employed to assess multi-temporal data. In this work, a spatiotemporal sequential indicator simulation (STSIS) based on a non-separable spatiotemporal semivariogram model was used to assimilate multi-temporal data in the mapping and uncertainty assessment of PM2.5 distributions in a contaminated atmosphere. PM2.5 concentrations recorded throughout 2014 in Shandong Province, China were used as the experimental dataset. Based on the number of STSIS procedures, we assessed various types of mapping uncertainties, including single-location uncertainties over one day and multiple days and multi-location uncertainties over one day and multiple days. A comparison of the STSIS technique with the SIS technique indicate that a better performance was obtained with the STSIS method.

Effectiveness of the food recovery at the retailing stage under shelf life uncertainty: An application to Italian food chains

International Nuclear Information System (INIS)

Muriana, Cinzia

2015-01-01

Highlights: • The food recovery is seen as suitable way to manage food near to its expiry date. • The variability of the products shelf life must be taken into account. • The paper addresses the mathematic modeling of the profit related to food recovery. • The optimal time to withdraw the products is determinant for food recovery. - Abstract: Food losses represent a significant issue affecting food supply chains. The possibility of recovering such products can be seen as an effective way to reduce such a phenomenon, improve supply chain performances and ameliorate the conditions of undernourished people. The topic has been already investigated by a previous paper enforcing the hypothesis of deterministic and constant Shelf Life (SL) of products. However, such a model cannot be properly extended to products affected by uncertainties of the SL as it does not take into account the deterioration costs and loss of profits due to the overcoming of the SL within the cycle time. Thus the present paper presents an extension of the previous one under stochastic conditions of the food quality. Differently from the previous publication, this work represents a general model applicable to all supply chains, especially to those managing fresh products characterized by uncertain SL such as fruits and vegetables. The deterioration costs and loss of profits are included in the model and the optimal time at which to withdraw the products from the shelves as well as the quantities to be shipped at each alternative destination have been determined. A comparison of the proposed model with that reported in the previous publication has been carried out in order to underline the impact of the SL variability on the optimality conditions. The results show that the food recovery strategy in the presence of uncertainty of the food quality is rewarding, even if the optimal profit is lower than that of the deterministic case

Effectiveness of the food recovery at the retailing stage under shelf life uncertainty: An application to Italian food chains

Energy Technology Data Exchange (ETDEWEB)

Muriana, Cinzia, E-mail: cinzia.muriana@unipa.it

2015-07-15

Highlights: • The food recovery is seen as suitable way to manage food near to its expiry date. • The variability of the products shelf life must be taken into account. • The paper addresses the mathematic modeling of the profit related to food recovery. • The optimal time to withdraw the products is determinant for food recovery. - Abstract: Food losses represent a significant issue affecting food supply chains. The possibility of recovering such products can be seen as an effective way to reduce such a phenomenon, improve supply chain performances and ameliorate the conditions of undernourished people. The topic has been already investigated by a previous paper enforcing the hypothesis of deterministic and constant Shelf Life (SL) of products. However, such a model cannot be properly extended to products affected by uncertainties of the SL as it does not take into account the deterioration costs and loss of profits due to the overcoming of the SL within the cycle time. Thus the present paper presents an extension of the previous one under stochastic conditions of the food quality. Differently from the previous publication, this work represents a general model applicable to all supply chains, especially to those managing fresh products characterized by uncertain SL such as fruits and vegetables. The deterioration costs and loss of profits are included in the model and the optimal time at which to withdraw the products from the shelves as well as the quantities to be shipped at each alternative destination have been determined. A comparison of the proposed model with that reported in the previous publication has been carried out in order to underline the impact of the SL variability on the optimality conditions. The results show that the food recovery strategy in the presence of uncertainty of the food quality is rewarding, even if the optimal profit is lower than that of the deterministic case.

Resilience of Agricultural Value Chains in Developing Country Contexts: A Framework and Assessment Approach

Directory of Open Access Journals (Sweden)

Ryan Vroegindewey

2018-03-01

Full Text Available Although agricultural value chain resilience is a crucial component to food security and sustainable food systems in developing countries, it has received little attention. This paper synthesizes knowledge from the social-ecological systems (SES, supply chain management, and value chain development literature to make three contributions to this research gap. First, we conceptualize agricultural value chain resilience and relate it to overall food system resilience. Second, we identify seven principles that are hypothesized to contribute to SES resilience, relate them to supply chain management theory, and discuss their application in agricultural value chains. A key insight is that the appropriateness of these principles are important to assess on a case-by-case basis, and depend in part on trade-offs between resilience and other dimensions of value chain performance. Third, we integrate two common tools, the Resilience Alliance’s assessment framework and value chain analysis techniques, to outline an adaptable participatory approach for assessing the resilience of agricultural value chains in developing countries. The objectives of the approach are to cultivate a chain-wide awareness for past and potential disturbances that could affect food security and other essential services provided by the value chain, and to identify upgrades that can build resilience against these key disturbances.

Coupling Uncertainties with Accuracy Assessment in Object-Based Slum Detections, Case Study: Jakarta, Indonesia

NARCIS (Netherlands)

Pratomo, J.; Kuffer, M.; Martinez, Javier; Kohli, D.

2017-01-01

Object-Based Image Analysis (OBIA) has been successfully used to map slums. In general, the occurrence of uncertainties in producing geographic data is inevitable. However, most studies concentrated solely on assessing the classification accuracy and neglecting the inherent uncertainties. Our

Uncertainty assessment in gamma spectrometric measurements of plutonium isotope ratios and age

Energy Technology Data Exchange (ETDEWEB)

Ramebaeck, H., E-mail: henrik.ramebeck@foi.se [Swedish Defence Research Agency, FOI, Division of CBRN Defence and Security, SE-901 82 Umea (Sweden); Chalmers University of Technology, Department of Chemical and Biological Engineering, Nuclear Chemistry, SE-412 96 Goeteborg (Sweden); Nygren, U.; Tovedal, A. [Swedish Defence Research Agency, FOI, Division of CBRN Defence and Security, SE-901 82 Umea (Sweden); Ekberg, C.; Skarnemark, G. [Chalmers University of Technology, Department of Chemical and Biological Engineering, Nuclear Chemistry, SE-412 96 Goeteborg (Sweden)

2012-09-15

A method for the assessment of the combined uncertainty in gamma spectrometric measurements of plutonium composition and age was evaluated. Two materials were measured. Isotope dilution inductively coupled plasma sector field mass spectrometry (ID-ICP-SFMS) was used as a reference method for comparing the results obtained with the gamma spectrometric method for one of the materials. For this material (weapons grade plutonium) the measurement results were in agreement between the two methods for all measurands. Moreover, the combined uncertainty in all isotope ratios considered in this material (R{sub Pu238/Pu239}, R{sub Pu240/Pu239}, R{sub Pu241/Pu239}, and R{sub Am241/Pu241} for age determination) were limited by counting statistics. However, the combined uncertainty for the other material (fuel grade plutonium) were limited by the response fit, which shows that the uncertainty in the response function is important to include in the combined measurement uncertainty of gamma spectrometric measurements of plutonium.

Reliable design of a closed loop supply chain network under uncertainty: An interval fuzzy possibilistic chance-constrained model

Science.gov (United States)

Vahdani, Behnam; Tavakkoli-Moghaddam, Reza; Jolai, Fariborz; Baboli, Arman

2013-06-01

This article seeks to offer a systematic approach to establishing a reliable network of facilities in closed loop supply chains (CLSCs) under uncertainties. Facilities that are located in this article concurrently satisfy both traditional objective functions and reliability considerations in CLSC network designs. To attack this problem, a novel mathematical model is developed that integrates the network design decisions in both forward and reverse supply chain networks. The model also utilizes an effective reliability approach to find a robust network design. In order to make the results of this article more realistic, a CLSC for a case study in the iron and steel industry has been explored. The considered CLSC is multi-echelon, multi-facility, multi-product and multi-supplier. Furthermore, multiple facilities exist in the reverse logistics network leading to high complexities. Since the collection centres play an important role in this network, the reliability concept of these facilities is taken into consideration. To solve the proposed model, a novel interactive hybrid solution methodology is developed by combining a number of efficient solution approaches from the recent literature. The proposed solution methodology is a bi-objective interval fuzzy possibilistic chance-constraint mixed integer linear programming (BOIFPCCMILP). Finally, computational experiments are provided to demonstrate the applicability and suitability of the proposed model in a supply chain environment and to help decision makers facilitate their analyses.

Applications of Probabilistic Consequence Assessment Uncertainty Analysis for Plant Management (invited paper)

International Nuclear Information System (INIS)

Boardman, J.; Pearce, K.I.; Ponting, A.C.

2000-01-01

Probabilistic Consequence Assessment (PCA) models describe the dispersion of released radioactive materials and predict the resulting interaction with and influence on the environment and man. Increasing use is being made of PCA tools as an input to the evaluation and improvement of safety for nuclear installations. The nature and extent of the assessment performed varies considerably according to its intended purpose. Nevertheless with the increasing use of such techniques, greater attention has been given to the reliability of the methods used and the inherent uncertainty associated with their predictions. Uncertainty analyses can provide the decision-maker with information to quantify how uncertain the answer is and what drives that uncertainty. They often force a review of the baseline assumptions for any PCA methodology and provide a benchmark against which the impact of further changes in models and recommendations can be compared. This process provides valuable management information to help prioritise further actions or research. (author)

Uncertainty analysis on probabilistic fracture mechanics assessment methodology

International Nuclear Information System (INIS)

Rastogi, Rohit; Vinod, Gopika; Chandra, Vikas; Bhasin, Vivek; Babar, A.K.; Rao, V.V.S.S.; Vaze, K.K.; Kushwaha, H.S.; Venkat-Raj, V.

1999-01-01

Fracture Mechanics has found a profound usage in the area of design of components and assessing fitness for purpose/residual life estimation of an operating component. Since defect size and material properties are statistically distributed, various probabilistic approaches have been employed for the computation of fracture probability. Monte Carlo Simulation is one such procedure towards the analysis of fracture probability. This paper deals with uncertainty analysis using the Monte Carlo Simulation methods. These methods were developed based on the R6 failure assessment procedure, which has been widely used in analysing the integrity of structures. The application of this method is illustrated with a case study. (author)

Uncertainty propagation and sensitivity analysis in system reliability assessment via unscented transformation

International Nuclear Information System (INIS)

Rocco Sanseverino, Claudio M.; Ramirez-Marquez, José Emmanuel

2014-01-01

The reliability of a system, notwithstanding it intended function, can be significantly affected by the uncertainty in the reliability estimate of the components that define the system. This paper implements the Unscented Transformation to quantify the effects of the uncertainty of component reliability through two approaches. The first approach is based on the concept of uncertainty propagation, which is the assessment of the effect that the variability of the component reliabilities produces on the variance of the system reliability. This assessment based on UT has been previously considered in the literature but only for system represented through series/parallel configuration. In this paper the assessment is extended to systems whose reliability cannot be represented through analytical expressions and require, for example, Monte Carlo Simulation. The second approach consists on the evaluation of the importance of components, i.e., the evaluation of the components that most contribute to the variance of the system reliability. An extension of the UT is proposed to evaluate the so called “main effects” of each component, as well to assess high order component interaction. Several examples with excellent results illustrate the proposed approach. - Highlights: • Simulation based approach for computing reliability estimates. • Computation of reliability variance via 2n+1 points. • Immediate computation of component importance. • Application to network systems

Assessing the Feasibility of Managed Aquifer Recharge for Irrigation under Uncertainty

Directory of Open Access Journals (Sweden)

Muhammad Arshad

2014-09-01

Full Text Available Additional storage of water is a potential option to meet future water supply goals. Financial comparisons are needed to improve decision making about whether to store water in surface reservoirs or below ground, using managed aquifer recharge (MAR. In some places, the results of cost-benefit analysis show that MAR is financially superior to surface storage. However, uncertainty often exists as to whether MAR systems will remain operationally effective and profitable in the future, because the profitability of MAR is dependent on many uncertain technical and financial variables. This paper introduces a method to assess the financial feasibility of MAR under uncertainty. We assess such uncertainties by identification of cross-over points in break-even analysis. Cross-over points are the thresholds where MAR and surface storage have equal financial returns. Such thresholds can be interpreted as a set of minimum requirements beyond which an investment in MAR may no longer be worthwhile. Checking that these thresholds are satisfied can improve confidence in decision making. Our suggested approach can also be used to identify areas that may not be suitable for MAR, thereby avoiding expensive hydrogeological and geophysical investigations.

Using measurement uncertainty in decision-making and conformity assessment

Science.gov (United States)

Pendrill, L. R.

2014-08-01

Measurements often provide an objective basis for making decisions, perhaps when assessing whether a product conforms to requirements or whether one set of measurements differs significantly from another. There is increasing appreciation of the need to account for the role of measurement uncertainty when making decisions, so that a ‘fit-for-purpose’ level of measurement effort can be set prior to performing a given task. Better mutual understanding between the metrologist and those ordering such tasks about the significance and limitations of the measurements when making decisions of conformance will be especially useful. Decisions of conformity are, however, currently made in many important application areas, such as when addressing the grand challenges (energy, health, etc), without a clear and harmonized basis for sharing the risks that arise from measurement uncertainty between the consumer, supplier and third parties. In reviewing, in this paper, the state of the art of the use of uncertainty evaluation in conformity assessment and decision-making, two aspects in particular—the handling of qualitative observations and of impact—are considered key to bringing more order to the present diverse rules of thumb of more or less arbitrary limits on measurement uncertainty and percentage risk in the field. (i) Decisions of conformity can be made on a more or less quantitative basis—referred in statistical acceptance sampling as by ‘variable’ or by ‘attribute’ (i.e. go/no-go decisions)—depending on the resources available or indeed whether a full quantitative judgment is needed or not. There is, therefore, an intimate relation between decision-making, relating objects to each other in terms of comparative or merely qualitative concepts, and nominal and ordinal properties. (ii) Adding measures of impact, such as the costs of incorrect decisions, can give more objective and more readily appreciated bases for decisions for all parties concerned. Such

A real-time assessment of measurement uncertainty in the experimental characterization of sprays

International Nuclear Information System (INIS)

Panão, M R O; Moreira, A L N

2008-01-01

This work addresses the estimation of the measurement uncertainty of discrete probability distributions used in the characterization of sprays. A real-time assessment of this measurement uncertainty is further investigated, particularly concerning the informative quality of the measured distribution and the influence of acquiring additional information on the knowledge retrieved from statistical analysis. The informative quality is associated with the entropy concept as understood in information theory (Shannon entropy), normalized by the entropy of the most informative experiment. A new empirical correlation is derived between the error accuracy of a discrete cumulative probability distribution and the normalized Shannon entropy. The results include case studies using: (i) spray impingement measurements to study the applicability of the real-time assessment of measurement uncertainty, and (ii) the simulation of discrete probability distributions of unknown shape or function to test the applicability of the new correlation

Flexibility evaluation of multiechelon supply chains.

Science.gov (United States)

Almeida, João Flávio de Freitas; Conceição, Samuel Vieira; Pinto, Luiz Ricardo; de Camargo, Ricardo Saraiva; Júnior, Gilberto de Miranda

2018-01-01

Multiechelon supply chains are complex logistics systems that require flexibility and coordination at a tactical level to cope with environmental uncertainties in an efficient and effective manner. To cope with these challenges, mathematical programming models are developed to evaluate supply chain flexibility. However, under uncertainty, supply chain models become complex and the scope of flexibility analysis is generally reduced. This paper presents a unified approach that can evaluate the flexibility of a four-echelon supply chain via a robust stochastic programming model. The model simultaneously considers the plans of multiple business divisions such as marketing, logistics, manufacturing, and procurement, whose goals are often conflicting. A numerical example with deterministic parameters is presented to introduce the analysis, and then, the model stochastic parameters are considered to evaluate flexibility. The results of the analysis on supply, manufacturing, and distribution flexibility are presented. Tradeoff analysis of demand variability and service levels is also carried out. The proposed approach facilitates the adoption of different management styles, thus improving supply chain resilience. The model can be extended to contexts pertaining to supply chain disruptions; for example, the model can be used to explore operation strategies when subtle events disrupt supply, manufacturing, or distribution.

Interactions between perceived uncertainty types in service dyads

DEFF Research Database (Denmark)

Kreye, Melanie

2018-01-01

to avoid business failure. A conceptual framework of four uncertainty types is investigated: environmental, technological, organisational, and relational uncertainty. We present insights from four empirical cases of service dyads collected via multiple sources of evidence including 54 semi-structured...... interviews, observations, and secondary data. The cases show seven interaction paths with direct knock-on effects between two uncertainty types and indirect knock-on effects between three or four uncertainty types. The findings suggest a causal chain from environmental, technological, organisational......, to relational uncertainty. This research contributes to the servitization literature by (i) con-firming the existence of uncertainty types, (ii) providing an in-depth characterisation of technological uncertainty, and (iii) showing the interaction paths between four uncertainty types in the form of a causal...

Information Uncertainty to Compare Qualitative Reasoning Security Risk Assessment Results

Energy Technology Data Exchange (ETDEWEB)

Chavez, Gregory M [Los Alamos National Laboratory; Key, Brian P [Los Alamos National Laboratory; Zerkle, David K [Los Alamos National Laboratory; Shevitz, Daniel W [Los Alamos National Laboratory

2009-01-01

The security risk associated with malevolent acts such as those of terrorism are often void of the historical data required for a traditional PRA. Most information available to conduct security risk assessments for these malevolent acts is obtained from subject matter experts as subjective judgements. Qualitative reasoning approaches such as approximate reasoning and evidential reasoning are useful for modeling the predicted risk from information provided by subject matter experts. Absent from these approaches is a consistent means to compare the security risk assessment results. Associated with each predicted risk reasoning result is a quantifiable amount of information uncertainty which can be measured and used to compare the results. This paper explores using entropy measures to quantify the information uncertainty associated with conflict and non-specificity in the predicted reasoning results. The measured quantities of conflict and non-specificity can ultimately be used to compare qualitative reasoning results which are important in triage studies and ultimately resource allocation. Straight forward extensions of previous entropy measures are presented here to quantify the non-specificity and conflict associated with security risk assessment results obtained from qualitative reasoning models.

An evaluation of uncertainties in radioecological models

International Nuclear Information System (INIS)

Hoffmann, F.O.; Little, C.A.; Miller, C.W.; Dunning, D.E. Jr.; Rupp, E.M.; Shor, R.W.; Schaeffer, D.L.; Baes, C.F. III

1978-01-01

The paper presents results of analyses for seven selected parameters commonly used in environmental radiological assessment models, assuming that the available data are representative of the true distribution of parameter values and that their respective distributions are lognormal. Estimates of the most probable, median, mean, and 99th percentile for each parameter are fiven and compared to U.S. NRC default values. The regulatory default values are generally greater than the median values for the selected parameters, but some are associated with percentiles significantly less than the 50th. The largest uncertainties appear to be associated with aquatic bioaccumulation factors for fresh water fish. Approximately one order of magnitude separates median values and values of the 99th percentile. The uncertainty is also estimated for the annual dose rate predicted by a multiplicative chain model for the transport of molecular iodine-131 via the air-pasture-cow-milk-child's thyroid pathway. The value for the 99th percentile is ten times larger than the median value of the predicted dose normalized for a given air concentration of 131 I 2 . About 72% of the uncertainty in this model is contributed by the dose conversion factor and the milk transfer coefficient. Considering the difficulties in obtaining a reliable quantification of the true uncertainties in model predictions, methods for taking these uncertainties into account when determining compliance with regulatory statutes are discussed. (orig./HP) [de

Ignoring correlation in uncertainty and sensitivity analysis in life cycle assessment: what is the risk?

Energy Technology Data Exchange (ETDEWEB)

Groen, E.A., E-mail: Evelyne.Groen@gmail.com [Wageningen University, P.O. Box 338, Wageningen 6700 AH (Netherlands); Heijungs, R. [Vrije Universiteit Amsterdam, De Boelelaan 1105, Amsterdam 1081 HV (Netherlands); Leiden University, Einsteinweg 2, Leiden 2333 CC (Netherlands)

2017-01-15

Life cycle assessment (LCA) is an established tool to quantify the environmental impact of a product. A good assessment of uncertainty is important for making well-informed decisions in comparative LCA, as well as for correctly prioritising data collection efforts. Under- or overestimation of output uncertainty (e.g. output variance) will lead to incorrect decisions in such matters. The presence of correlations between input parameters during uncertainty propagation, can increase or decrease the the output variance. However, most LCA studies that include uncertainty analysis, ignore correlations between input parameters during uncertainty propagation, which may lead to incorrect conclusions. Two approaches to include correlations between input parameters during uncertainty propagation and global sensitivity analysis were studied: an analytical approach and a sampling approach. The use of both approaches is illustrated for an artificial case study of electricity production. Results demonstrate that both approaches yield approximately the same output variance and sensitivity indices for this specific case study. Furthermore, we demonstrate that the analytical approach can be used to quantify the risk of ignoring correlations between input parameters during uncertainty propagation in LCA. We demonstrate that: (1) we can predict if including correlations among input parameters in uncertainty propagation will increase or decrease output variance; (2) we can quantify the risk of ignoring correlations on the output variance and the global sensitivity indices. Moreover, this procedure requires only little data. - Highlights: • Ignoring correlation leads to under- or overestimation of the output variance. • We demonstrated that the risk of ignoring correlation can be quantified. • The procedure proposed is generally applicable in life cycle assessment. • In some cases, ignoring correlation has a minimal effect on decision-making tools.

Assessment and visualization of uncertainty for countrywide soil organic matter map of Hungary using local entropy

Science.gov (United States)

Szatmári, Gábor; Pásztor, László

2016-04-01

Uncertainty is a general term expressing our imperfect knowledge in describing an environmental process and we are aware of it (Bárdossy and Fodor, 2004). Sampling, laboratory measurements, models and so on are subject to uncertainty. Effective quantification and visualization of uncertainty would be indispensable to stakeholders (e.g. policy makers, society). Soil related features and their spatial models should be stressfully targeted to uncertainty assessment because their inferences are further used in modelling and decision making process. The aim of our present study was to assess and effectively visualize the local uncertainty of the countrywide soil organic matter (SOM) spatial distribution model of Hungary using geostatistical tools and concepts. The Hungarian Soil Information and Monitoring System's SOM data (approximately 1,200 observations) and environmental related, spatially exhaustive secondary information (i.e. digital elevation model, climatic maps, MODIS satellite images and geological map) were used to model the countrywide SOM spatial distribution by regression kriging. It would be common to use the calculated estimation (or kriging) variance as a measure of uncertainty, however the normality and homoscedasticity hypotheses have to be refused according to our preliminary analysis on the data. Therefore, a normal score transformation and a sequential stochastic simulation approach was introduced to be able to model and assess the local uncertainty. Five hundred equally probable realizations (i.e. stochastic images) were generated. The number of the stochastic images is fairly enough to provide a model of uncertainty at each location, which is a complete description of uncertainty in geostatistics (Deutsch and Journel, 1998). Furthermore, these models can be applied e.g. to contour the probability of any events, which can be regarded as goal oriented digital soil maps and are of interest for agricultural management and decision making as well. A

Some concepts of model uncertainty for performance assessments of nuclear waste repositories

International Nuclear Information System (INIS)

Eisenberg, N.A.; Sagar, B.; Wittmeyer, G.W.

1994-01-01

Models of the performance of nuclear waste repositories will be central to making regulatory decisions regarding the safety of such facilities. The conceptual model of repository performance is represented by mathematical relationships, which are usually implemented as one or more computer codes. A geologic system may allow many conceptual models, which are consistent with the observations. These conceptual models may or may not have the same mathematical representation. Experiences in modeling the performance of a waste repository representation. Experiences in modeling the performance of a waste repository (which is, in part, a geologic system), show that this non-uniqueness of conceptual models is a significant source of model uncertainty. At the same time, each conceptual model has its own set of parameters and usually, it is not be possible to completely separate model uncertainty from parameter uncertainty for the repository system. Issues related to the origin of model uncertainty, its relation to parameter uncertainty, and its incorporation in safety assessments are discussed from a broad regulatory perspective. An extended example in which these issues are explored numerically is also provided

Consideration of vertical uncertainty in elevation-based sea-level rise assessments: Mobile Bay, Alabama case study

Science.gov (United States)

Gesch, Dean B.

2013-01-01

The accuracy with which coastal topography has been mapped directly affects the reliability and usefulness of elevationbased sea-level rise vulnerability assessments. Recent research has shown that the qualities of the elevation data must be well understood to properly model potential impacts. The cumulative vertical uncertainty has contributions from elevation data error, water level data uncertainties, and vertical datum and transformation uncertainties. The concepts of minimum sealevel rise increment and minimum planning timeline, important parameters for an elevation-based sea-level rise assessment, are used in recognition of the inherent vertical uncertainty of the underlying data. These concepts were applied to conduct a sea-level rise vulnerability assessment of the Mobile Bay, Alabama, region based on high-quality lidar-derived elevation data. The results that detail the area and associated resources (land cover, population, and infrastructure) vulnerable to a 1.18-m sea-level rise by the year 2100 are reported as a range of values (at the 95% confidence level) to account for the vertical uncertainty in the base data. Examination of the tabulated statistics about land cover, population, and infrastructure in the minimum and maximum vulnerable areas shows that these resources are not uniformly distributed throughout the overall vulnerable zone. The methods demonstrated in the Mobile Bay analysis provide an example of how to consider and properly account for vertical uncertainty in elevation-based sea-level rise vulnerability assessments, and the advantages of doing so.

Assessing Power System Stability Following Load Changes and Considering Uncertainty

Directory of Open Access Journals (Sweden)

D. V. Ngo

2018-04-01

Full Text Available An increase in load capacity during the operation of a power system usually causes voltage drop and leads to system instability, so it is necessary to monitor the effect of load changes. This article presents a method of assessing the power system stability according to the load node capacity considering uncertainty factors in the system. The proposed approach can be applied to large-scale power systems for voltage stability assessment in real-time.

Use of quantitative uncertainty analysis for human health risk assessment

International Nuclear Information System (INIS)

Duncan, F.L.W.; Gordon, J.W.; Kelly, M.

1994-01-01

Current human health risk assessment method for environmental risks typically use point estimates of risk accompanied by qualitative discussions of uncertainty. Alternatively, Monte Carlo simulations may be used with distributions for input parameters to estimate the resulting risk distribution and descriptive risk percentiles. These two techniques are applied for the ingestion of 1,1=dichloroethene in ground water. The results indicate that Monte Carlo simulations provide significantly more information for risk assessment and risk management than do point estimates

Probabilistic Assessment of Investment Options in Honey Value Chains in Lamu County, Kenya

Directory of Open Access Journals (Sweden)

Joshua Wafula

2018-03-01

Full Text Available Designing and implementing biodiversity-based value chains can be a complex undertaking, especially in places where outcomes are uncertain and risks of project failure and cost overruns are high. We used the Stochastic Impact Evaluation (SIE approach to guide the Intergovernmental Authority on Development (IGAD on viable investment options in honey value chains, which the agency considered implementing as an economic incentive for communities along the Kenya-Somalia border to conserve biodiversity. The SIE approach allows for holistic analysis of project cost, benefit, and risk variables, including those with uncertain and missing information. It also identifies areas that pose critical uncertainties in the project. We started by conducting a baseline survey in Witu and Awer in Lamu County, Kenya. The aim of the survey was to establish the current farm income from beekeeping as a baseline, against which the prospective impacts of intervention options could be measured. We then developed an intervention decision model that was populated with all cost, benefit and risk variables relevant to beekeeping. After receiving training in making quantitative estimates, four subject-matter experts expressed their uncertainty about the proposed variables in the model by specifying probability distributions for them. We then used Monte Carlo simulation to project decision outcomes. We also identified variables that projected decision outcomes were most sensitive to, and we determined the value of information for each variable. The variable with the highest information value to the decision-maker in Witu was the honey price. In Awer, no additional information on any of the variables would change the recommendation to invest in honey value chains in the region. The analysis demonstrates a novel and comprehensive approach to decision-making for different stakeholders in a project where decision outcomes are uncertain.

A Bayesian belief network approach for assessing uncertainty in conceptual site models at contaminated sites

DEFF Research Database (Denmark)

Thomsen, Nanna Isbak; Binning, Philip John; McKnight, Ursula S.

2016-01-01

the most important site-specific features and processes that may affect the contaminant transport behavior at the site. However, the development of a CSM will always be associated with uncertainties due to limited data and lack of understanding of the site conditions. CSM uncertainty is often found...... to be a major source of model error and it should therefore be accounted for when evaluating uncertainties in risk assessments. We present a Bayesian belief network (BBN) approach for constructing CSMs and assessing their uncertainty at contaminated sites. BBNs are graphical probabilistic models...... that are effective for integrating quantitative and qualitative information, and thus can strengthen decisions when empirical data are lacking. The proposed BBN approach facilitates a systematic construction of multiple CSMs, and then determines the belief in each CSM using a variety of data types and/or expert...

Variability and uncertainty in Swedish exposure factors for use in quantitative exposure assessments.

Science.gov (United States)

Filipsson, Monika; Öberg, Tomas; Bergbäck, Bo

2011-01-01

Information of exposure factors used in quantitative risk assessments has previously been compiled and reported for U.S. and European populations. However, due to the advancement of science and knowledge, these reports are in continuous need of updating with new data. Equally important is the change over time of many exposure factors related to both physiological characteristics and human behavior. Body weight, skin surface, time use, and dietary habits are some of the most obvious examples covered here. A wealth of data is available from literature not primarily gathered for the purpose of risk assessment. Here we review a number of key exposure factors and compare these factors between northern Europe--here represented by Sweden--and the United States. Many previous compilations of exposure factor data focus on interindividual variability and variability between sexes and age groups, while uncertainty is mainly dealt with in a qualitative way. In this article variability is assessed along with uncertainty. As estimates of central tendency and interindividual variability, mean, standard deviation, skewness, kurtosis, and multiple percentiles were calculated, while uncertainty was characterized using 95% confidence intervals for these parameters. The presented statistics are appropriate for use in deterministic analyses using point estimates for each input parameter as well as in probabilistic assessments. © 2010 Society for Risk Analysis.

On-orbit servicing system assessment and optimization methods based on lifecycle simulation under mixed aleatory and epistemic uncertainties

Science.gov (United States)

Yao, Wen; Chen, Xiaoqian; Huang, Yiyong; van Tooren, Michel

2013-06-01

To assess the on-orbit servicing (OOS) paradigm and optimize its utilities by taking advantage of its inherent flexibility and responsiveness, the OOS system assessment and optimization methods based on lifecycle simulation under uncertainties are studied. The uncertainty sources considered in this paper include both the aleatory (random launch/OOS operation failure and on-orbit component failure) and the epistemic (the unknown trend of the end-used market price) types. Firstly, the lifecycle simulation under uncertainties is discussed. The chronological flowchart is presented. The cost and benefit models are established, and the uncertainties thereof are modeled. The dynamic programming method to make optimal decision in face of the uncertain events is introduced. Secondly, the method to analyze the propagation effects of the uncertainties on the OOS utilities is studied. With combined probability and evidence theory, a Monte Carlo lifecycle Simulation based Unified Uncertainty Analysis (MCS-UUA) approach is proposed, based on which the OOS utility assessment tool under mixed uncertainties is developed. Thirdly, to further optimize the OOS system under mixed uncertainties, the reliability-based optimization (RBO) method is studied. To alleviate the computational burden of the traditional RBO method which involves nested optimum search and uncertainty analysis, the framework of Sequential Optimization and Mixed Uncertainty Analysis (SOMUA) is employed to integrate MCS-UUA, and the RBO algorithm SOMUA-MCS is developed. Fourthly, a case study on the OOS system for a hypothetical GEO commercial communication satellite is investigated with the proposed assessment tool. Furthermore, the OOS system is optimized with SOMUA-MCS. Lastly, some conclusions are given and future research prospects are highlighted.

Uncertainty Communication. Issues and good practice

International Nuclear Information System (INIS)

Kloprogge, P.; Van der Sluijs, J.; Wardekker, A.

2007-12-01

In 2003 the Netherlands Environmental Assessment Agency (MNP) published the RIVM/MNP Guidance for Uncertainty Assessment and Communication. The Guidance assists in dealing with uncertainty in environmental assessments. Dealing with uncertainty is essential because assessment results regarding complex environmental issues are of limited value if the uncertainties have not been taken into account adequately. A careful analysis of uncertainties in an environmental assessment is required, but even more important is the effective communication of these uncertainties in the presentation of assessment results. The Guidance yields rich and differentiated insights in uncertainty, but the relevance of this uncertainty information may vary across audiences and uses of assessment results. Therefore, the reporting of uncertainties is one of the six key issues that is addressed in the Guidance. In practice, users of the Guidance felt a need for more practical assistance in the reporting of uncertainty information. This report explores the issue of uncertainty communication in more detail, and contains more detailed guidance on the communication of uncertainty. In order to make this a 'stand alone' document several questions that are mentioned in the detailed Guidance have been repeated here. This document thus has some overlap with the detailed Guidance. Part 1 gives a general introduction to the issue of communicating uncertainty information. It offers guidelines for (fine)tuning the communication to the intended audiences and context of a report, discusses how readers of a report tend to handle uncertainty information, and ends with a list of criteria that uncertainty communication needs to meet to increase its effectiveness. Part 2 helps writers to analyze the context in which communication takes place, and helps to map the audiences, and their information needs. It further helps to reflect upon anticipated uses and possible impacts of the uncertainty information on the

An Assessment Tool to Integrate Sustainability Principles into the Global Supply Chain

Directory of Open Access Journals (Sweden)

María Jesús Muñoz-Torres

2018-02-01

Full Text Available The integration of sustainability principles into the assessment of companies along the supply chains is a growing research area. However, there is an absence of a generally accepted method to evaluate corporate sustainability performance (CSP, and the models and frameworks proposed by the literature present various important challenges to be addressed. A systematic literature review on the supply chain at the corporate level has been conducted, analyzing the main strengths and gaps in the sustainability assessment literature. Therefore, this paper aims to contribute to the development of this field by proposing an assessment framework a leading company can adopt to expand sustainability principles to the rest of the members of the supply chain. This proposal is based on best practices and integrates and shares efforts with key initiatives (for instance, the Organizational Environmental Footprint from the European Commission and United Nations Environment Programme and the Society of Environmental Toxicology and Chemistry UNEP/SETAC; moreover, it overcomes important limitations of the current sustainability tools in a supply chain context consistent with the circular economy, the Sustainable Development Goals (SDGs, planetary boundaries, and social foundation requirements. The results obtained create, on the one hand, new opportunities for academics; and, on the other hand, in further research, the use of this framework could be a means of actively engaging companies in their supply chains and of achieving the implementation of practical and comprehensive CSP assessment.

Technology scale and supply chains in a secure, affordable and low carbon energy transition

International Nuclear Information System (INIS)

Hoggett, Richard

2014-01-01

Highlights: • Energy systems need to decarbonise, provide security and remain affordable. • There is uncertainty over which technologies will best enable this to happen. • A strategy to deal with uncertainty is to assess a technologies ability to show resilience, flexibility and adaptability. • Scale is important and smaller scale technologies are like to display the above characteristics. • Smaller scale technologies are therefore more likely to enable a sustainable, secure, and affordable energy transition. - Abstract: This research explores the relationship between technology scale, energy security and decarbonisation within the UK energy system. There is considerable uncertainty about how best to deliver on these goals for energy policy, but a focus on supply chains and their resilience can provide useful insights into the problems uncertainty causes. Technology scale is central to this, and through an analysis of the supply chains of nuclear power and solar photovoltaics, it is suggested that smaller scale technologies are more likely to support and enable a secure, low carbon energy transition. This is because their supply chains are less complex, show more flexibility and adaptability, and can quickly respond to changes within an energy system, and as such they are more resilient than large scale technologies. These characteristics are likely to become increasingly important in a rapidly changing energy system, and prioritising those technologies that demonstrate resilience, flexibility and adaptability will better enable a transition that is rapid, sustainable, secure and affordable

Environment and Human Health: The Challenge of Uncertainty in Risk Assessment

Directory of Open Access Journals (Sweden)

Alex G. Stewart

2018-01-01

Full Text Available High quality and accurate environmental investigations and analysis are essential to any assessment of contamination and to the decision-making process thereafter. Remediation decisions may be focused by health outcomes, whether already present or a predicted risk. The variability inherent in environmental media and analysis can be quantified statistically; uncertainty in models can be reduced by additional research; deep uncertainty exists when environmental or biomedical processes are not understood, or agreed upon, or remain uncharacterized. Deep uncertainty is common where health and environment interact. Determinants of health operate from the individual’s genes to the international level; often several levels act synergistically. We show this in detail for lead (Pb. Pathways, exposure, dose and response also vary, modifying certainty. Multi-disciplinary approaches, built on high-quality environmental investigations, enable the management of complex and uncertain situations. High quality, accurate environmental investigations into pollution issues remain the cornerstone of understanding attributable health outcomes and developing appropriate responses and remediation. However, they are not sufficient on their own, needing careful integration with the wider contexts and stakeholder agendas, without which any response to the environmental assessment may very well founder. Such approaches may benefit more people than any other strategy.

Uncertainty Estimation Cheat Sheet for Probabilistic Risk Assessment

Science.gov (United States)

Britton, Paul T.; Al Hassan, Mohammad; Ring, Robert W.

2017-01-01

"Uncertainty analysis itself is uncertain, therefore, you cannot evaluate it exactly," Source Uncertain Quantitative results for aerospace engineering problems are influenced by many sources of uncertainty. Uncertainty analysis aims to make a technical contribution to decision-making through the quantification of uncertainties in the relevant variables as well as through the propagation of these uncertainties up to the result. Uncertainty can be thought of as a measure of the 'goodness' of a result and is typically represented as statistical dispersion. This paper will explain common measures of centrality and dispersion; and-with examples-will provide guidelines for how they may be estimated to ensure effective technical contributions to decision-making.

Assessing spatial uncertainties of land allocation using a scenario approach and sensitivity analysis: A study for land use in Europe

NARCIS (Netherlands)

Verburg, P.H.; Tabeau, A.A.; Hatna, E.

2013-01-01

Land change model outcomes are vulnerable to multiple types of uncertainty, including uncertainty in input data, structural uncertainties in the model and uncertainties in model parameters. In coupled model systems the uncertainties propagate between the models. This paper assesses uncertainty of

Flexibility evaluation of multiechelon supply chains.

Directory of Open Access Journals (Sweden)

João Flávio de Freitas Almeida

Full Text Available Multiechelon supply chains are complex logistics systems that require flexibility and coordination at a tactical level to cope with environmental uncertainties in an efficient and effective manner. To cope with these challenges, mathematical programming models are developed to evaluate supply chain flexibility. However, under uncertainty, supply chain models become complex and the scope of flexibility analysis is generally reduced. This paper presents a unified approach that can evaluate the flexibility of a four-echelon supply chain via a robust stochastic programming model. The model simultaneously considers the plans of multiple business divisions such as marketing, logistics, manufacturing, and procurement, whose goals are often conflicting. A numerical example with deterministic parameters is presented to introduce the analysis, and then, the model stochastic parameters are considered to evaluate flexibility. The results of the analysis on supply, manufacturing, and distribution flexibility are presented. Tradeoff analysis of demand variability and service levels is also carried out. The proposed approach facilitates the adoption of different management styles, thus improving supply chain resilience. The model can be extended to contexts pertaining to supply chain disruptions; for example, the model can be used to explore operation strategies when subtle events disrupt supply, manufacturing, or distribution.

A Belief Rule Based Expert System to Assess Mental Disorder under Uncertainty

DEFF Research Database (Denmark)

Hossain, Mohammad Shahadat; Afif Monrat, Ahmed; Hasan, Mamun

2016-01-01

to ignorance, incompleteness, and randomness. So, a belief rule-based expert system (BRBES) has been designed and developed with the capability of handling the uncertainties mentioned. Evidential reasoning works as the inference engine and the belief rule base as the knowledge representation schema......Mental disorder is a change of mental or behavioral pattern that causes sufferings and impairs the ability to function in ordinary life. In psychopathology, the assessment methods of mental disorder contain various types of uncertainties associated with signs and symptoms. This study identifies...

Damage assessment of composite plate structures with material and measurement uncertainty

Science.gov (United States)

Chandrashekhar, M.; Ganguli, Ranjan

2016-06-01

Composite materials are very useful in structural engineering particularly in weight sensitive applications. Two different test models of the same structure made from composite materials can display very different dynamic behavior due to large uncertainties associated with composite material properties. Also, composite structures can suffer from pre-existing imperfections like delaminations, voids or cracks during fabrication. In this paper, we show that modeling and material uncertainties in composite structures can cause considerable problem in damage assessment. A recently developed C0 shear deformable locking free refined composite plate element is employed in the numerical simulations to alleviate modeling uncertainty. A qualitative estimate of the impact of modeling uncertainty on the damage detection problem is made. A robust Fuzzy Logic System (FLS) with sliding window defuzzifier is used for delamination damage detection in composite plate type structures. The FLS is designed using variations in modal frequencies due to randomness in material properties. Probabilistic analysis is performed using Monte Carlo Simulation (MCS) on a composite plate finite element model. It is demonstrated that the FLS shows excellent robustness in delamination detection at very high levels of randomness in input data.

Accounting for uncertainty factors in biodiversity impact assessment: lessons from a case study

International Nuclear Information System (INIS)

Geneletti, D.; Beinat, E.; Chung, C.F.; Fabbri, A.G.; Scholten, H.J.

2003-01-01

For an Environmental Impact Statement (EIS) to effectively contribute to decision-making, it must include one crucial step: the estimation of the uncertainty factors affecting the impact evaluation and of their effect on the evaluation results. Knowledge of the uncertainties better orients the strategy of the decision-makers and underlines the most critical data or methodological steps of the procedure. Accounting for uncertainty factors is particularly relevant when dealing with ecological impacts, whose forecasts are typically affected by a high degree of simplification. By means of a case study dealing with the evaluation of road alternatives, this paper explores and discusses the main uncertainties that are related to the typical stages of a biodiversity impact assessment: uncertainty in the data that are used, in the methodologies that are applied, and in the value judgments provided by the experts. Subsequently, the effects of such uncertainty factors are tracked back to the result of the evaluation, i.e., to the relative performance of the project alternatives under consideration. This allows to test the sensitivity of the results, and consequently to provide a more informative ranking of the alternatives. The papers concludes by discussing the added-value for decision-making provided by uncertainty analysis within EIA

Qualification and application of nuclear reactor accident analysis code with the capability of internal assessment of uncertainty

International Nuclear Information System (INIS)

Borges, Ronaldo Celem

2001-10-01

This thesis presents an independent qualification of the CIAU code ('Code with the capability of - Internal Assessment of Uncertainty') which is part of the internal uncertainty evaluation process with a thermal hydraulic system code on a realistic basis. This is done by combining the uncertainty methodology UMAE ('Uncertainty Methodology based on Accuracy Extrapolation') with the RELAP5/Mod3.2 code. This allows associating uncertainty band estimates with the results obtained by the realistic calculation of the code, meeting licensing requirements of safety analysis. The independent qualification is supported by simulations with RELAP5/Mod3.2 related to accident condition tests of LOBI experimental facility and to an event which has occurred in Angra 1 nuclear power plant, by comparison with measured results and by establishing uncertainty bands on safety parameter calculated time trends. These bands have indeed enveloped the measured trends. Results from this independent qualification of CIAU have allowed to ascertain the adequate application of a systematic realistic code procedure to analyse accidents with uncertainties incorporated in the results, although there is an evident need of extending the uncertainty data base. It has been verified that use of the code with this internal assessment of uncertainty is feasible in the design and license stages of a NPP. (author)

Markov Chains and Markov Processes

OpenAIRE

Ogunbayo, Segun

2016-01-01

Markov chain, which was named after Andrew Markov is a mathematical system that transfers a state to another state. Many real world systems contain uncertainty. This study helps us to understand the basic idea of a Markov chain and how is been useful in our daily lives. For some times there had been suspense on distinct predictions and future existences. Also in different games there had been different expectations or results involved. That is the reason why we need Markov chains to predict o...

Integrating uncertainty propagation in GNSS radio occultation retrieval: from excess phase to atmospheric bending angle profiles

Science.gov (United States)

Schwarz, Jakob; Kirchengast, Gottfried; Schwaerz, Marc

2018-05-01

Global Navigation Satellite System (GNSS) radio occultation (RO) observations are highly accurate, long-term stable data sets and are globally available as a continuous record from 2001. Essential climate variables for the thermodynamic state of the free atmosphere - such as pressure, temperature, and tropospheric water vapor profiles (involving background information) - can be derived from these records, which therefore have the potential to serve as climate benchmark data. However, to exploit this potential, atmospheric profile retrievals need to be very accurate and the remaining uncertainties quantified and traced throughout the retrieval chain from raw observations to essential climate variables. The new Reference Occultation Processing System (rOPS) at the Wegener Center aims to deliver such an accurate RO retrieval chain with integrated uncertainty propagation. Here we introduce and demonstrate the algorithms implemented in the rOPS for uncertainty propagation from excess phase to atmospheric bending angle profiles, for estimated systematic and random uncertainties, including vertical error correlations and resolution estimates. We estimated systematic uncertainty profiles with the same operators as used for the basic state profiles retrieval. The random uncertainty is traced through covariance propagation and validated using Monte Carlo ensemble methods. The algorithm performance is demonstrated using test day ensembles of simulated data as well as real RO event data from the satellite missions CHAllenging Minisatellite Payload (CHAMP); Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC); and Meteorological Operational Satellite A (MetOp). The results of the Monte Carlo validation show that our covariance propagation delivers correct uncertainty quantification from excess phase to bending angle profiles. The results from the real RO event ensembles demonstrate that the new uncertainty estimation chain performs robustly. Together

MODARIA WG5: Towards a practical guidance for including uncertainties in the results of dose assessment of routine releases

Energy Technology Data Exchange (ETDEWEB)

Mora, Juan C. [Centro de Investigaciones Energeticas, Medioambientales y Tecnologicas - CIEMAT (Spain); Telleria, Diego [International Atomic Energy Agency - IAEA (Austria); Al Neaimi, Ahmed [Emirates Nuclear Energy Corporation - ENEC (United Arab Emirates); Blixt Buhr, Anna Ma [Vattenfall AB (Sweden); Bonchuk, Iurii [Radiation Protection Institute - RPI (Ukraine); Chouhan, Sohan [Atomic Energy of Canada Limited - AECL (Canada); Chyly, Pavol [SE-VYZ (Slovakia); Curti, Adriana R. [Autoridad Regulatoria Nuclear - ARN (Argentina); Da Costa, Dejanira [Instituto de Radioprotecao e Dosimetria - IRD (Brazil); Duran, Juraj [VUJE Inc (Slovakia); Galeriu, Dan [Horia Hulubei National Institute of Physics and Nuclear Engineering - IFIN-HH (Romania); Haegg, Ann- Christin; Lager, Charlotte [Swedish Radiation Safety Authority - SSM (Sweden); Heling, Rudie [Nuclear Research and Consultancy Group - NRG (Netherlands); Ivanis, Goran; Shen, Jige [Ecometrix Incorporated (Canada); Iosjpe, Mikhail [Norwegian Radiation Protection Authority - NRPA (Norway); Krajewski, Pawel M. [Central Laboratory for Radiological Protection - CLOR (Poland); Marang, Laura; Vermorel, Fabien [Electricite de France - EdF (France); Mourlon, Christophe [Institut de Radioprotection et de Surete Nucleaire - IRSN (France); Perez, Fabricio F. [Belgian Nuclear Research Centre - SCK (Belgium); Woodruffe, Andrew [Federal Authority for Nuclear Regulation - FANR (United Arab Emirates); Zorko, Benjamin [Jozef Stefan Institute (Slovenia)

2014-07-01

MODARIA (Modelling and Data for Radiological Impact Assessments) project was launched in 2012 with the aim of improving the capabilities in radiation dose assessment by means of acquisition of improved data for model testing, model testing and comparison, reaching consensus on modelling philosophies, approaches and parameter values, development of improved methods and exchange of information. The project focuses on areas where uncertainties remain in the predictive capability of environmental models, emphasizing in reducing associated uncertainties or developing new approaches to strengthen the evaluation of the radiological impact. Within MODARIA, four main areas were defined, one of them devoted to Uncertainty and Variability. In this area four working groups were included, Working Group 5 dealing with the 'uncertainty and variability analysis for assessments of radiological impacts arising from routine discharges of radionuclides'. Whether doses are estimated by using measurement data, by applying models, or through a combination of measurements and calculations, the variability and uncertainty contribute to a distribution of possible values. The degree of variability and uncertainty is represented by the shape and extent of that distribution. The main objective of WG5 is to explore how to consider uncertainties and variabilities in the results of assessment of doses in planned situations for controlling the impact of routine releases from radioactive and nuclear installations to the environment. The final aim is to produce guidance for the calculation of uncertainties in these exposure situations and for the presentation of such results to the different stakeholders. To achieve that objective the main tasks identified were: to find tools and methods for uncertainty and variability analysis applicable to dose assessments in routine radioactive discharges, to define scenarios where information on uncertainty and variability of parameters is available

An Approach for Assessing the Benefits of IT Investments in Global Supply Chains

DEFF Research Database (Denmark)

Betz, Michaela; Henningsson, Stefan

2016-01-01

-duced by the technology as an isolated product. In contrast, research on global supply chains has shown that benefits generated from IT investments in this domain are typically generated by the coor-dinated use of many stakeholders and by technologies producing complimentary effects in systemic relationships......This paper develops and demonstrates a novel approach for ex-ante assessment of business benefits from IT investments in global supply chains. Extant IT assessment approaches are typically based on the assumption that benefit realization from IT investments involves a single stakeholder and are pro....... The assessment approach in this paper brings the contingent inter-organizational and technological dependencies of IT investments to the forefront of the assessment. It provides actors in industries relating to global supply chains the means to better apprehend the possible benefits from an IT investment...

Some considerations on the treatment of uncertainties in risk assessment for practical decision making

International Nuclear Information System (INIS)

Aven, Terje; Zio, Enrico

2011-01-01

This paper discusses the challenges involved in the representation and treatment of uncertainties in risk assessment, taking the point of view of its use in support to decision making. Two main issues are addressed: (1) how to faithfully represent and express the knowledge available to best support the decision making and (2) how to best inform the decision maker. A general risk-uncertainty framework is presented which provides definitions and interpretations of the key concepts introduced. The framework covers probability theory as well as alternative representations of uncertainty, including interval probability, possibility and evidence theory.

Quantification of uncertainty in flood risk assessment for flood protection planning: a Bayesian approach

Science.gov (United States)

Dittes, Beatrice; Špačková, Olga; Ebrahimian, Negin; Kaiser, Maria; Rieger, Wolfgang; Disse, Markus; Straub, Daniel

2017-04-01

Flood risk estimates are subject to significant uncertainties, e.g. due to limited records of historic flood events, uncertainty in flood modeling, uncertain impact of climate change or uncertainty in the exposure and loss estimates. In traditional design of flood protection systems, these uncertainties are typically just accounted for implicitly, based on engineering judgment. In the AdaptRisk project, we develop a fully quantitative framework for planning of flood protection systems under current and future uncertainties using quantitative pre-posterior Bayesian decision analysis. In this contribution, we focus on the quantification of the uncertainties and study their relative influence on the flood risk estimate and on the planning of flood protection systems. The following uncertainty components are included using a Bayesian approach: 1) inherent and statistical (i.e. limited record length) uncertainty; 2) climate uncertainty that can be learned from an ensemble of GCM-RCM models; 3) estimates of climate uncertainty components not covered in 2), such as bias correction, incomplete ensemble, local specifics not captured by the GCM-RCM models; 4) uncertainty in the inundation modelling; 5) uncertainty in damage estimation. We also investigate how these uncertainties are possibly reduced in the future when new evidence - such as new climate models, observed extreme events, and socio-economic data - becomes available. Finally, we look into how this new evidence influences the risk assessment and effectivity of flood protection systems. We demonstrate our methodology for a pre-alpine catchment in southern Germany: the Mangfall catchment in Bavaria that includes the city of Rosenheim, which suffered significant losses during the 2013 flood event.

Handling Diversity of Visions and Priorities in Food Chain Sustainability Assessment

Directory of Open Access Journals (Sweden)

Francesca Galli

2016-03-01

Full Text Available Food chain sustainability assessment is challenging on several grounds. Handling knowledge and information on sustainability performance and coping with the diversity of visions around “what counts as sustainable food” are two key issues addressed by this study. By developing a comparative case study on local, regional and global wheat-to-bread chains, and confronting the multidimensionality of sustainability, this work focuses on the differing visions and perspectives of stakeholders. We integrate qualitative and quantitative data, stakeholder consultation and multi-criteria analysis to align the visions and the multiple meanings of sustainability. Because of the complexity and the dynamicity of the food system, the multidimensionality of the sustainability concept and its pliability to stakeholders priorities, sustainability is an object of competition for firms in the agro-food sector and has major implications in the governance of food chains. Results identify key propositions in relation to: (i the value of combining science-led evidence with socio-cultural values; (ii multidimensional sustainability assessment as a self diagnosis tool; and (iii the need to identify shared assessment criteria by communities of reference.

Severe Accident Research Network (SARNET). Level 2 PSA work package: comparison of partners methods for uncertainties assessment

International Nuclear Information System (INIS)

Chaumont, B.; Haesendonck, M.; Vidal, S.; Eyink, J.; Loeffler, H.; Radu, G.; Kopustinskas, V.; Ming, A.; Guntay, S.; Gustavsson, V.; Ivanov, I.; Dienstbier, J.; Bareith, A.; Hollo, E.; Lajtha, G.

2007-01-01

The PSA2 work package (PSA2 WP) is a part of the Joined Programme Activity of the European Severe Accident Network (SARNET) related to level 2 PSA methodologies. The general objectives of this work package is to provide a comparison of the different methodologies used or under development for level 2 PSA application by the partners involved in the work package and to promote their harmonization. The PSA2 WP is organized into three main topics: methodologies in general, methodologies for uncertainties assessment, and dynamic reliability methods. The different tasks initially defined for these three topics are shortly described and the partners involved identified. Attention is then paid on the methodologies used so far by the different partners to assess the uncertainties in their level 2 PSA. A review of partners approaches to assess - as far as possible - the different sources of possible uncertainties is done for the different following topics: - uncertainties propagated from the level 1 PSA, - uncertainties (in sense of approximation) due to the binning of the level 1 sequences in Plant Damage, - uncertainties related to the structure of the Accident Progression Event Tree, - uncertainties related to the probabilities of stochastic events (system failure or recovery, human actions, some physical phenomena such as ignition of hydrogen combustion or triggering of steam explosion), - uncertainties elated to the modelling of the different physical phenomena, - uncertainties related to the cut-off frequency used in the probabilistic quantification of the Accident Progression Event Tree; - uncertainties related to the binning of level 2 sequences in Release Categories (variables not considered, values of eventual continuous variables). First conclusions of the comparison are given in terms of improvement needs and then of perspectives of the work for the following period of work. (authors)

Assessing the Uncertainty of Tropical Cyclone Simulations in NCAR's Community Atmosphere Model

Directory of Open Access Journals (Sweden)

Kevin A Reed

2011-08-01

Full Text Available The paper explores the impact of the initial-data, parameter and structural model uncertainty on the simulation of a tropical cyclone-like vortex in the National Center for Atmospheric Research's (NCAR Community Atmosphere Model (CAM. An analytic technique is used to initialize the model with an idealized weak vortex that develops into a tropical cyclone over ten simulation days. A total of 78 ensemble simulations are performed at horizontal grid spacings of 1.0°, 0.5° and 0.25° using two recently released versions of the model, CAM 4 and CAM 5. The ensemble members represent simulations with random small-amplitude perturbations of the initial conditions, small shifts in the longitudinal position of the initial vortex and runs with slightly altered model parameters. The main distinction between CAM 4 and CAM 5 lies within the physical parameterization suite, and the simulations with both CAM versions at the varying resolutions assess the structural model uncertainty. At all resolutions storms are produced with many tropical cyclone-like characteristics. The CAM 5 simulations exhibit more intense storms than CAM 4 by day 10 at the 0.5° and 0.25° grid spacings, while the CAM 4 storm at 1.0° is stronger. There are also distinct differences in the shapes and vertical profiles of the storms in the two variants of CAM. The ensemble members show no distinction between the initial-data and parameter uncertainty simulations. At day 10 they produce ensemble root-mean-square deviations from an unperturbed control simulation on the order of 1--5 m s^-1 for the maximum low-level wind speed and 2--10 hPa for the minimum surface pressure. However, there are large differences between the two CAM versions at identical horizontal resolutions. It suggests that the structural uncertainty is more dominant than the initial-data and parameter uncertainties in this study. The uncertainty among the ensemble members is assessed and quantified.

Uncertainties in assessing climate change impacts on the hydrology of Mediterranean basins

Science.gov (United States)

Ludwig, Ralf

2013-04-01

There is substantial evidence in historical and recent observations that the Mediterranean and neighboring regions are especially vulnerable to the impacts of climate change. Numerous climate projections, stemming from ensembles of global and regional climate models, agree on severe changes in the climate forcing which are likely to exacerbate subsequent ecological, economic and social impacts. Many of these causal connections are closely linked to the general expectation that water availability will decline in the already water-stressed basins of Africa, the Mediterranean region and the Near East, even though considerable regional variances must be expected. Consequently, climate change impacts on water resources are raising concerns regarding their possible management and security implications. Decreasing access to water resources and other related factors could be a cause or a 'multiplier' of tensions within and between countries. Whether security threats arise from climate impacts or options for cooperation evolve does not depend only on the severity of the impacts themselves, but on social, economic, and institutional vulnerabilities or resilience as well as factors that influence local, national and international relations. However, an assessment of vulnerability and risks hinges on natural, socio-economic, and political conditions and responses, all of which are uncertain. Multidisciplinary research is needed to tackle the multi-facet complexity of climate change impacts on water resources in the Mediterranean and neighboring countries. This is particularly true in a region of overall data scarcity and poor data management and exchange structures. The current potential to develop appropriate regional adaptation measures towards climate change impacts suffers heavily from large uncertainties. These spread along a long chain of components, starting from the definition of emission scenarios to global and regional climate modeling to impact models and a

A mixed biomass-based energy supply chain for enhancing economic and environmental sustainability benefits: A multi-criteria decision making framework

International Nuclear Information System (INIS)

Mirkouei, Amin; Haapala, Karl R.; Sessions, John; Murthy, Ganti S.

2017-01-01

Highlights: •A mixed supply chain is developed to enhance sustainability benefits of bioenergy. •A decision-making framework is constructed to balance sustainability dimensions. •A stochastic optimization model is developed to explore the effects of uncertainty. •This study provides insights on bio-oil production processes and system structure. -- Abstract: Bioenergy sources have been introduced as a means to address challenges of conventional energy sources. The uncertainties of supply-side (upstream) externalities (e.g., collection and logistics) represent the key challenges in bioenergy supply chains and lead to reduce cross-cutting sustainability benefits. We propose a mixed biomass-based energy supply chain (consisting of mixed-mode bio-refineries and mixed-pathway transportation) and a multi-criteria decision making framework to address the upstream challenges. Our developed framework supports decisions influencing the economic and environmental dimensions of sustainability. Economic analysis employs a support vector machine technique, to predict the pattern of uncertainty parameters, and a stochastic optimization model, to incorporate uncertainties into the model. The stochastic model minimizes the total annual cost of the proposed mixed supply chain network by using a genetic algorithm. Environmental impact analysis employs life cycle assessment to evaluate the global warming potential of the cost-effective supply chain network. Our presented approach is capable of enhancing sustainability benefits of bioenergy industry infrastructure. A case study for the Pacific Northwest is used to demonstrate the application of the methodology and to verify the models. The results indicate that mixed supply chains can improve sustainability performance over traditional supply infrastructures by reducing costs (up to 24%) and environmental impacts (up to 5%).

Measuring information security breach impact and uncertainties under various information sharing scenarios

OpenAIRE

Durowoju, Olatunde; Chan, Hing; Wang, Xiaojun

2013-01-01

This study draws on information theory and aims to provide simulated evidence using real historical and statistical data to demonstrate how various levels of integration moderate the impact and uncertainties of information security breach on supply chain performance. We find that the supply chain behaves differently under various levels of integration when a security breach occurs. The entropy analysis revealed that the wholesaler experience the most uncertainty under system failure and data ...

The uncertainty cascade in flood risk assessment under changing climatic conditions - the Biala Tarnowska case study

Science.gov (United States)

Doroszkiewicz, Joanna; Romanowicz, Renata

2016-04-01

Uncertainty in the results of the hydraulic model is not only associated with the limitations of that model and the shortcomings of data. An important factor that has a major impact on the uncertainty of the flood risk assessment in a changing climate conditions is associated with the uncertainty of future climate scenarios (IPCC WG I, 2013). Future climate projections provided by global climate models are used to generate future runoff required as an input to hydraulic models applied in the derivation of flood risk maps. Biala Tarnowska catchment, situated in southern Poland is used as a case study. Future discharges at the input to a hydraulic model are obtained using the HBV model and climate projections obtained from the EUROCORDEX project. The study describes a cascade of uncertainty related to different stages of the process of derivation of flood risk maps under changing climate conditions. In this context it takes into account the uncertainty of future climate projections, an uncertainty of flow routing model, the propagation of that uncertainty through the hydraulic model, and finally, the uncertainty related to the derivation of flood risk maps. One of the aims of this study is an assessment of a relative impact of different sources of uncertainty on the uncertainty of flood risk maps. Due to the complexity of the process, an assessment of total uncertainty of maps of inundation probability might be very computer time consuming. As a way forward we present an application of a hydraulic model simulator based on a nonlinear transfer function model for the chosen locations along the river reach. The transfer function model parameters are estimated based on the simulations of the hydraulic model at each of the model cross-section. The study shows that the application of the simulator substantially reduces the computer requirements related to the derivation of flood risk maps under future climatic conditions. Acknowledgements: This work was supported by the

Simulation and assessment of agricultural biomass supply chain systems

Directory of Open Access Journals (Sweden)

D. Pavlou

2017-05-01

Full Text Available Agricultural biomass supply chain consists of a number of interacted sequential operations affected by various variables, such as weather conditions, machinery systems, and biomass features. These facts make the process of biomass supply chain as a complex system that requires computational tools, e.g. simulation and mathematical models, for their assessment and analysis. A biomass supply chain simulation model developed on the ExtendSim 8 simulation environment is presented in this paper. A number of sequential operations are applied in order biomass to be mowed, harvested, and transported to a biorefinery facility. Different operational scenarios regarding the travel distance between field and biorefinery facility, number of machines, and capacity of machines are analyzed showing how different parameters affect the processes within biomass supply chain in terms of time and cost. The results shown that parameters such as area of the field, travel distance, number of available machines, capacity of the machines, etc. should be taken into account in order a less time and/ or cost consuming machinery combination to be selected.

Product carbon footprint assessment supporting the green supply chain construction in household appliance manufacturers

Science.gov (United States)

Chen, Jianhua; Sun, Liang; Guo, Huiting

2017-11-01

Supply chain carbon emission is one of the factors considered in the green supply chain management. A method was designed to support the green supply chain measures based on the carbon footprint assessment for products. A research for 3 typical household appliances carbon footprint assessment was conducted to explore using product carbon footprint assessment method to guide the green supply chain management of the manufacturers. The result could reflect the differences directions on green supply chain management of manufacturers of washing machine, air conditioner and microwave, respectively That is, the washing machine manufacturer should pay attention to the low carbon activities in upstream suppliers in highest priority, and also the promotion of product energy efficiency. The air conditioner manufacturer should pay attention to the product energy efficiency increasing in highest priority, and the improvement of refrigerant to decrease its GWP. And the microwave manufacture could only focus on the energy efficiency increasing because it contributes most of the carbon emission to its carbon footprint. Besides, the representativeness of product and the applicability of the method were also discussed. As the manufacturer could master the technical information on raw material and components of its products to conduct the product carbon footprint assessment, this method could help the manufacturer to identify the effective green supply chain measures in the preliminary stage.

A solution procedure for mixed-integer nonlinear programming formulation of supply chain planning with quantity discounts under demand uncertainty

Science.gov (United States)

Yin, Sisi; Nishi, Tatsushi

2014-11-01

Quantity discount policy is decision-making for trade-off prices between suppliers and manufacturers while production is changeable due to demand fluctuations in a real market. In this paper, quantity discount models which consider selection of contract suppliers, production quantity and inventory simultaneously are addressed. The supply chain planning problem with quantity discounts under demand uncertainty is formulated as a mixed-integer nonlinear programming problem (MINLP) with integral terms. We apply an outer-approximation method to solve MINLP problems. In order to improve the efficiency of the proposed method, the problem is reformulated as a stochastic model replacing the integral terms by using a normalisation technique. We present numerical examples to demonstrate the efficiency of the proposed method.

Information on Hydrologic Conceptual Models, Parameters, Uncertainty Analysis, and Data Sources for Dose Assessments at Decommissioning Sites

International Nuclear Information System (INIS)

Meyer, Philip D.; Gee, Glendon W.; Nicholson, Thomas J.

1999-01-01

This report addresses issues related to the analysis of uncertainty in dose assessments conducted as part of decommissioning analyses. The analysis is limited to the hydrologic aspects of the exposure pathway involving infiltration of water at the ground surface, leaching of contaminants, and transport of contaminants through the groundwater to a point of exposure. The basic conceptual models and mathematical implementations of three dose assessment codes are outlined along with the site-specific conditions under which the codes may provide inaccurate, potentially nonconservative results. In addition, the hydrologic parameters of the codes are identified and compared. A methodology for parameter uncertainty assessment is outlined that considers the potential data limitations and modeling needs of decommissioning analyses. This methodology uses generic parameter distributions based on national or regional databases, sensitivity analysis, probabilistic modeling, and Bayesian updating to incorporate site-specific information. Data sources for best-estimate parameter values and parameter uncertainty information are also reviewed. A follow-on report will illustrate the uncertainty assessment methodology using decommissioning test cases

Information on Hydrologic Conceptual Models, Parameters, Uncertainty Analysis, and Data Sources for Dose Assessments at Decommissioning Sites

International Nuclear Information System (INIS)

Meyer D, Philip; Gee W, Glendon

2000-01-01

This report addresses issues related to the analysis of uncertainty in dose assessments conducted as part of decommissioning analyses. The analysis is limited to the hydrologic aspects of the exposure pathway involving infiltration of water at the ground surface, leaching of contaminants, and transport of contaminants through the groundwater to a point of exposure. The basic conceptual models and mathematical implementations of three dose assessment codes are outlined along with the site-specific conditions under which the codes may provide inaccurate, potentially nonconservative results. In addition, the hydrologic parameters of the codes are identified and compared. A methodology for parameter uncertainty assessment is outlined that considers the potential data limitations and modeling needs of decommissioning analyses. This methodology uses generic parameter distributions based on national or regional databases, sensitivity analysis, probabilistic modeling, and Bayesian updating to incorporate site-specific information. Data sources for best-estimate parameter values and parameter uncertainty information are also reviewed. A follow-on report will illustrate the uncertainty assessment methodology using decommissioning test cases

Assessment and presentation of uncertainties in probabilistic risk assessment: how should this be done

International Nuclear Information System (INIS)

Garlick, A.R.; Holloway, N.J.

1987-01-01

Despite continuing improvements in probabilistic risk assessment (PRA) techniques, PRA results, particularly those including degraded core analysis, will have maximum uncertainties of several orders of magnitude. This makes the expression of results, a matter no less important than their estimation. We put forward some ideas on the assessment and expression of highly uncertain quantities, such as probabilities of outcomes of a severe accident. These do not form a consistent set, but rather a number of alternative approaches aimed at stimulating discussion. These include non-probability expressions, such as fuzzy logic or Schafer's support and plausibility which abandon the purely probabilistic expression of risk for a more flexible type of expression, in which other types of measure are possible. The 'risk equivalent plant' concepts represent the opposite approach. Since uncertainty in a risk measure is in itself a form of risk, an attempt is made to define a 'risk equivalent' which is a risk with perfectly defined parameters, regarded (by means of suitable methods of judgement) as 'equally undesirable' with the actual plant. Some guidelines are given on the use of Bayesian methods in data-free or limited data situations. (author)

ASSESSMENT OF GREEN SUPPLY CHAIN MANAGEMENT IN AN INDIAN INDUSTRY

OpenAIRE

Er. Bhura Singh*1, Er. Nikhlesh N. Singh2 & Dr. Prabhat Sinha3

2017-01-01

In the present scenario, there is a growing need for integrating environmentally sound choices into supply chain management research and practices among the organization. The main objective of this study is to assess the recent literatures of the Green Supply Chain management (GSCM) and also determine the environment concern of this emerging field. The study is focused on development of GSCM strategies which also includes that all the researchers from this review is focused on environmental a...

Characterization of subjective uncertainty in the 1996 performance assessment for the Waste Isolation Pilot Plant

International Nuclear Information System (INIS)

HELTON, JON CRAIG; MARTELL, MARY-ALENA; TIERNEY, MARTIN S.

2000-01-01

The 1996 performance assessment (PA) for the Waste Isolation Pilot Plant (WIPP) maintains a separation between stochastic (i.e., aleatory) and subjective (i.e., epistemic) uncertainty, with stochastic uncertainty arising from the possible disruptions that could occur at the WIPP over the 10,000 yr regulatory period specified by the US Environmental Protection Agency (40 CFR 191,40 CFR 194) and subjective uncertainty arising from an inability to uniquely characterize many of the inputs required in the 1996 WIPP PA. The characterization of subjective uncertainty is discussed, including assignment of distributions, uncertain variables selected for inclusion in analysis, correlation control, sample size, statistical confidence on mean complementary cumulative distribution functions, generation of Latin hypercube samples, sensitivity analysis techniques, and scenarios involving stochastic and subjective uncertainty

Characterization of subjective uncertainty in the 1996 performance assessment for the Waste Isolation Pilot Plant

Energy Technology Data Exchange (ETDEWEB)

HELTON,JON CRAIG; MARTELL,MARY-ALENA; TIERNEY,MARTIN S.

2000-05-18

The 1996 performance assessment (PA) for the Waste Isolation Pilot Plant (WIPP) maintains a separation between stochastic (i.e., aleatory) and subjective (i.e., epistemic) uncertainty, with stochastic uncertainty arising from the possible disruptions that could occur at the WIPP over the 10,000 yr regulatory period specified by the US Environmental Protection Agency (40 CFR 191,40 CFR 194) and subjective uncertainty arising from an inability to uniquely characterize many of the inputs required in the 1996 WIPP PA. The characterization of subjective uncertainty is discussed, including assignment of distributions, uncertain variables selected for inclusion in analysis, correlation control, sample size, statistical confidence on mean complementary cumulative distribution functions, generation of Latin hypercube samples, sensitivity analysis techniques, and scenarios involving stochastic and subjective uncertainty.

Climate change impact assessment and adaptation under uncertainty

NARCIS (Netherlands)

Wardekker, J.A.

2011-01-01

Expected impacts of climate change are associated with large uncertainties, particularly at the local level. Adaptation scientists, practitioners, and decision-makers will need to find ways to cope with these uncertainties. Several approaches have been suggested as ‘uncertainty-proof’ to some

Epistemic uncertainties and natural hazard risk assessment - Part 1: A review of the issues

Science.gov (United States)

Beven, K. J.; Aspinall, W. P.; Bates, P. D.; Borgomeo, E.; Goda, K.; Hall, J. W.; Page, T.; Phillips, J. C.; Rougier, J. T.; Simpson, M.; Stephenson, D. B.; Smith, P. J.; Wagener, T.; Watson, M.

2015-12-01

Uncertainties in natural hazard risk assessment are generally dominated by the sources arising from lack of knowledge or understanding of the processes involved. There is a lack of knowledge about frequencies, process representations, parameters, present and future boundary conditions, consequences and impacts, and the meaning of observations in evaluating simulation models. These are the epistemic uncertainties that can be difficult to constrain, especially in terms of event or scenario probabilities, even as elicited probabilities rationalized on the basis of expert judgements. This paper reviews the issues raised by trying to quantify the effects of epistemic uncertainties. Such scientific uncertainties might have significant influence on decisions that are made for risk management, so it is important to communicate the meaning of an uncertainty estimate and to provide an audit trail of the assumptions on which it is based. Some suggestions for good practice in doing so are made.

County-Level Climate Uncertainty for Risk Assessments: Volume 1.

Energy Technology Data Exchange (ETDEWEB)

Backus, George A.; Lowry, Thomas Stephen; Jones, Shannon M; Walker, La Tonya Nicole; Roberts, Barry L; Malczynski, Leonard A.

2017-06-01

This report uses the CMIP5 series of climate model simulations to produce country- level uncertainty distributions for use in socioeconomic risk assessments of climate change impacts. It provides appropriate probability distributions, by month, for 169 countries and autonomous-areas on temperature, precipitation, maximum temperature, maximum wind speed, humidity, runoff, soil moisture and evaporation for the historical period (1976-2005), and for decadal time periods to 2100. It also provides historical and future distributions for the Arctic region on ice concentration, ice thickness, age of ice, and ice ridging in 15-degree longitude arc segments from the Arctic Circle to 80 degrees latitude, plus two polar semicircular regions from 80 to 90 degrees latitude. The uncertainty is meant to describe the lack of knowledge rather than imprecision in the physical simulation because the emphasis is on unfalsified risk and its use to determine potential socioeconomic impacts. The full report is contained in 27 volumes.

Sensitivity, uncertainty, and importance analysis of a risk assessment

International Nuclear Information System (INIS)

Andsten, R.S.; Vaurio, J.K.

1992-01-01

In this paper a number of supplementary studies and applications associated with probabilistic safety assessment (PSA) are described, including sensitivity and importance evaluations of failures, errors, systems, and groups of components. The main purpose is to illustrate the usefulness of a PSA for making decisions about safety improvements, training, allowed outage times, and test intervals. A useful measure of uncertainty importance is presented, and it points out areas needing development, such as reactor vessel aging phenomena, for reducing overall uncertainty. A time-dependent core damage frequency is also presented, illustrating the impact of testing scenarios and intervals. Tea methods and applications presented are based on the Level 1 PSA carried out for the internal initiating event of the Loviisa 1 nuclear power station. Steam generator leakages and associated operator actions are major contributors to the current core-damage frequency estimate of 2 x10 -4 /yr. The results are used to improve the plant and procedures and to guide future improvements

Uncertainty Assessment: Reservoir Inflow Forecasting with Ensemble Precipitation Forecasts and HEC-HMS

Directory of Open Access Journals (Sweden)

Sheng-Chi Yang

2014-01-01

Full Text Available During an extreme event, having accurate inflow forecasting with enough lead time helps reservoir operators decrease the impact of floods downstream. Furthermore, being able to efficiently operate reservoirs could help maximize flood protection while saving water for drier times of the year. This study combines ensemble quantitative precipitation forecasts and a hydrological model to provide a 3-day reservoir inflow in the Shihmen Reservoir, Taiwan. A total of six historical typhoons were used for model calibration, validation, and application. An understanding of cascaded uncertainties from the numerical weather model through the hydrological model is necessary for a better use for forecasting. This study thus conducted an assessment of forecast uncertainty on magnitude and timing of peak and cumulative inflows. It found that using the ensemble-mean had less uncertainty than randomly selecting individual member. The inflow forecasts with shorter length of cumulative time had a higher uncertainty. The results showed that using the ensemble precipitation forecasts with the hydrological model would have the advantage of extra lead time and serve as a valuable reference for operating reservoirs.

Assessment of food fraud vulnerability in the spices chain

NARCIS (Netherlands)

Silvis, I.C.J.; Ruth, van S.M.; Fels, van der Ine; Luning, P.A.

2017-01-01

Recent scandals have increased the need to strengthen companies’ ability to combat fraud within their own organizations and across their supply chain. Vulnerability assessments are a first step towards the inventory of fraud vulnerability and fraud mitigation plans. Spices are reported frequently

Assessing the relative importance of parameter and forcing uncertainty and their interactions in conceptual hydrological model simulations

Science.gov (United States)

Mockler, E. M.; Chun, K. P.; Sapriza-Azuri, G.; Bruen, M.; Wheater, H. S.

2016-11-01

Predictions of river flow dynamics provide vital information for many aspects of water management including water resource planning, climate adaptation, and flood and drought assessments. Many of the subjective choices that modellers make including model and criteria selection can have a significant impact on the magnitude and distribution of the output uncertainty. Hydrological modellers are tasked with understanding and minimising the uncertainty surrounding streamflow predictions before communicating the overall uncertainty to decision makers. Parameter uncertainty in conceptual rainfall-runoff models has been widely investigated, and model structural uncertainty and forcing data have been receiving increasing attention. This study aimed to assess uncertainties in streamflow predictions due to forcing data and the identification of behavioural parameter sets in 31 Irish catchments. By combining stochastic rainfall ensembles and multiple parameter sets for three conceptual rainfall-runoff models, an analysis of variance model was used to decompose the total uncertainty in streamflow simulations into contributions from (i) forcing data, (ii) identification of model parameters and (iii) interactions between the two. The analysis illustrates that, for our subjective choices, hydrological model selection had a greater contribution to overall uncertainty, while performance criteria selection influenced the relative intra-annual uncertainties in streamflow predictions. Uncertainties in streamflow predictions due to the method of determining parameters were relatively lower for wetter catchments, and more evenly distributed throughout the year when the Nash-Sutcliffe Efficiency of logarithmic values of flow (lnNSE) was the evaluation criterion.

Particle Swarm Optimization and Uncertainty Assessment in Inverse Problems

Directory of Open Access Journals (Sweden)

José L. G. Pallero

2018-01-01

Full Text Available Most inverse problems in the industry (and particularly in geophysical exploration are highly underdetermined because the number of model parameters too high to achieve accurate data predictions and because the sampling of the data space is scarce and incomplete; it is always affected by different kinds of noise. Additionally, the physics of the forward problem is a simplification of the reality. All these facts result in that the inverse problem solution is not unique; that is, there are different inverse solutions (called equivalent, compatible with the prior information that fits the observed data within similar error bounds. In the case of nonlinear inverse problems, these equivalent models are located in disconnected flat curvilinear valleys of the cost-function topography. The uncertainty analysis consists of obtaining a representation of this complex topography via different sampling methodologies. In this paper, we focus on the use of a particle swarm optimization (PSO algorithm to sample the region of equivalence in nonlinear inverse problems. Although this methodology has a general purpose, we show its application for the uncertainty assessment of the solution of a geophysical problem concerning gravity inversion in sedimentary basins, showing that it is possible to efficiently perform this task in a sampling-while-optimizing mode. Particularly, we explain how to use and analyze the geophysical models sampled by exploratory PSO family members to infer different descriptors of nonlinear uncertainty.

Climate change risks and adaptation options across Australian seafood supply chains – A preliminary assessment

Directory of Open Access Journals (Sweden)

A. Fleming

2014-01-01

Full Text Available Climate change is already impacting the biology of the oceans and some dependent industries are in turn responding to these impacts. The development of response options for users of marine resources, such as fishers, is important in guiding adaptation efforts. However, harvesting fish is only the first step in a supply chain that delivers seafood to consumers. Impacts higher up the chain have seldom been considered in fisheries-climate research yet an understanding of these impacts and how climate risks and adaptation information are interpreted and used by stakeholders across the chain is vital for developing viable and sustainable adaptation options. We examined stakeholder perceptions of points where climate change impacts and adaptations currently occur, or may occur in the future, across the supply chains of several Australian fisheries (southern rock lobster, tropical rock lobster, prawn and aquaculture sectors (oyster, aquaculture prawn. We found that climate change impacts are well understood at the harvest stage and there is evidence of potential impacts and disruption to supply chains. Yet, there currently is no strong driver for change higher up the chain. Holistic adaptation planning along the supply chain, underpinned by targeted information and policy for the catch, processing and distribution, and marketing phases is needed. This effort is needed now, as some adaptation options have long lead times, and a delay in adaptation planning may limit future options. Given potential lead times and associated uncertainty, a risk-based approach is recommended with regard to adaptation planning for Australia’s seafood sector.

Uncertainty Quantification in the Reliability and Risk Assessment of Generation IV Reactors: Final Scientific/Technical Report

International Nuclear Information System (INIS)

Vierow, Karen; Aldemir, Tunc

2009-01-01

The project entitled, 'Uncertainty Quantification in the Reliability and Risk Assessment of Generation IV Reactors', was conducted as a DOE NERI project collaboration between Texas A and M University and The Ohio State University between March 2006 and June 2009. The overall goal of the proposed project was to develop practical approaches and tools by which dynamic reliability and risk assessment techniques can be used to augment the uncertainty quantification process in probabilistic risk assessment (PRA) methods and PRA applications for Generation IV reactors. This report is the Final Scientific/Technical Report summarizing the project.

Uncertainty Quantification in the Reliability and Risk Assessment of Generation IV Reactors: Final Scientific/Technical Report

Energy Technology Data Exchange (ETDEWEB)

Vierow, Karen; Aldemir, Tunc

2009-09-10

The project entitled, “Uncertainty Quantification in the Reliability and Risk Assessment of Generation IV Reactors”, was conducted as a DOE NERI project collaboration between Texas A&M University and The Ohio State University between March 2006 and June 2009. The overall goal of the proposed project was to develop practical approaches and tools by which dynamic reliability and risk assessment techniques can be used to augment the uncertainty quantification process in probabilistic risk assessment (PRA) methods and PRA applications for Generation IV reactors. This report is the Final Scientific/Technical Report summarizing the project.

Measurement uncertainty: Friend or foe?

Science.gov (United States)

Infusino, Ilenia; Panteghini, Mauro

2018-02-02

The definition and enforcement of a reference measurement system, based on the implementation of metrological traceability of patients' results to higher order reference methods and materials, together with a clinically acceptable level of measurement uncertainty, are fundamental requirements to produce accurate and equivalent laboratory results. The uncertainty associated with each step of the traceability chain should be governed to obtain a final combined uncertainty on clinical samples fulfilling the requested performance specifications. It is important that end-users (i.e., clinical laboratory) may know and verify how in vitro diagnostics (IVD) manufacturers have implemented the traceability of their calibrators and estimated the corresponding uncertainty. However, full information about traceability and combined uncertainty of calibrators is currently very difficult to obtain. Laboratory professionals should investigate the need to reduce the uncertainty of the higher order metrological references and/or to increase the precision of commercial measuring systems. Accordingly, the measurement uncertainty should not be considered a parameter to be calculated by clinical laboratories just to fulfil the accreditation standards, but it must become a key quality indicator to describe both the performance of an IVD measuring system and the laboratory itself. Copyright © 2018 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

Addressing uncertainties in the ERICA Integrated Approach

International Nuclear Information System (INIS)

Oughton, D.H.; Agueero, A.; Avila, R.; Brown, J.E.; Copplestone, D.; Gilek, M.

2008-01-01

Like any complex environmental problem, ecological risk assessment of the impacts of ionising radiation is confounded by uncertainty. At all stages, from problem formulation through to risk characterisation, the assessment is dependent on models, scenarios, assumptions and extrapolations. These include technical uncertainties related to the data used, conceptual uncertainties associated with models and scenarios, as well as social uncertainties such as economic impacts, the interpretation of legislation, and the acceptability of the assessment results to stakeholders. The ERICA Integrated Approach has been developed to allow an assessment of the risks of ionising radiation, and includes a number of methods that are intended to make the uncertainties and assumptions inherent in the assessment more transparent to users and stakeholders. Throughout its development, ERICA has recommended that assessors deal openly with the deeper dimensions of uncertainty and acknowledge that uncertainty is intrinsic to complex systems. Since the tool is based on a tiered approach, the approaches to dealing with uncertainty vary between the tiers, ranging from a simple, but highly conservative screening to a full probabilistic risk assessment including sensitivity analysis. This paper gives on overview of types of uncertainty that are manifest in ecological risk assessment and the ERICA Integrated Approach to dealing with some of these uncertainties

Dispelling urban myths about default uncertainty factors in chemical risk assessment--sufficient protection against mixture effects?

Science.gov (United States)

Martin, Olwenn V; Martin, Scholze; Kortenkamp, Andreas

2013-07-01

Assessing the detrimental health effects of chemicals requires the extrapolation of experimental data in animals to human populations. This is achieved by applying a default uncertainty factor of 100 to doses not found to be associated with observable effects in laboratory animals. It is commonly assumed that the toxicokinetic and toxicodynamic sub-components of this default uncertainty factor represent worst-case scenarios and that the multiplication of those components yields conservative estimates of safe levels for humans. It is sometimes claimed that this conservatism also offers adequate protection from mixture effects. By analysing the evolution of uncertainty factors from a historical perspective, we expose that the default factor and its sub-components are intended to represent adequate rather than worst-case scenarios. The intention of using assessment factors for mixture effects was abandoned thirty years ago. It is also often ignored that the conservatism (or otherwise) of uncertainty factors can only be considered in relation to a defined level of protection. A protection equivalent to an effect magnitude of 0.001-0.0001% over background incidence is generally considered acceptable. However, it is impossible to say whether this level of protection is in fact realised with the tolerable doses that are derived by employing uncertainty factors. Accordingly, it is difficult to assess whether uncertainty factors overestimate or underestimate the sensitivity differences in human populations. It is also often not appreciated that the outcome of probabilistic approaches to the multiplication of sub-factors is dependent on the choice of probability distributions. Therefore, the idea that default uncertainty factors are overly conservative worst-case scenarios which can account both for the lack of statistical power in animal experiments and protect against potential mixture effects is ill-founded. We contend that precautionary regulation should provide an

Decentralized supply chain network design: monopoly, duopoly and oligopoly competitions under uncertainty

Science.gov (United States)

Seyedhosseini, Seyed Mohammad; Fahimi, Kaveh; Makui, Ahmad

2017-12-01

This paper presents the competitive supply chain network design problem in which n decentralized supply chains simultaneously enter the market with no existing rival chain, shape their networks and set wholesale and retail prices in competitive mode. The customer demand is elastic and price dependent, customer utility function is based on the Hoteling model and the chains produce identical or highly substitutable products. We construct a solution algorithm based on bi-level programming and possibility theory. In the proposed bi-level model, the inner part sets the prices based on simultaneous extra- and Stackleberg intra- chains competitions, and the outer part shapes the networks in cooperative competitions. Finally, we use a real-word study to discuss the effect of the different structures of the competitors on the equilibrium solution. Moreover, sensitivity analyses are conducted and managerial insights are offered.

Proposed standardized definitions for vertical resolution and uncertainty in the NDACC lidar ozone and temperature algorithms - Part 2: Ozone DIAL uncertainty budget

Science.gov (United States)

Leblanc, Thierry; Sica, Robert J.; van Gijsel, Joanna A. E.; Godin-Beekmann, Sophie; Haefele, Alexander; Trickl, Thomas; Payen, Guillaume; Liberti, Gianluigi

2016-08-01

A standardized approach for the definition, propagation, and reporting of uncertainty in the ozone differential absorption lidar data products contributing to the Network for the Detection for Atmospheric Composition Change (NDACC) database is proposed. One essential aspect of the proposed approach is the propagation in parallel of all independent uncertainty components through the data processing chain before they are combined together to form the ozone combined standard uncertainty. The independent uncertainty components contributing to the overall budget include random noise associated with signal detection, uncertainty due to saturation correction, background noise extraction, the absorption cross sections of O3, NO2, SO2, and O2, the molecular extinction cross sections, and the number densities of the air, NO2, and SO2. The expression of the individual uncertainty components and their step-by-step propagation through the ozone differential absorption lidar (DIAL) processing chain are thoroughly estimated. All sources of uncertainty except detection noise imply correlated terms in the vertical dimension, which requires knowledge of the covariance matrix when the lidar signal is vertically filtered. In addition, the covariance terms must be taken into account if the same detection hardware is shared by the lidar receiver channels at the absorbed and non-absorbed wavelengths. The ozone uncertainty budget is presented as much as possible in a generic form (i.e., as a function of instrument performance and wavelength) so that all NDACC ozone DIAL investigators across the network can estimate, for their own instrument and in a straightforward manner, the expected impact of each reviewed uncertainty component. In addition, two actual examples of full uncertainty budget are provided, using nighttime measurements from the tropospheric ozone DIAL located at the Jet Propulsion Laboratory (JPL) Table Mountain Facility, California, and nighttime measurements from the JPL

Scientific uncertainties associated with risk assessment of radiation

International Nuclear Information System (INIS)

Hubert, P.; Fagnani, F.

1989-05-01

The proper use and interpretation of data pertaining to biological effects of ionizing radiations is based on a continuous effort to discuss the various assumptions and uncertainties in the process of risk assessment. In this perspective, it has been considered useful by the Committee to review critically the general scientific foundations that constitute the basic framework of data for the evaluation of health effects of radiation. This review is an attempt to identify the main sources of uncertainties, to give, when possible, an order of magnitude for their relative importance, and to clarify the principal interactions between the different steps of the process of risk quantification. The discussion has been restricted to stochastic effects and especially to cancer induction in man: observations at the cellular levels and animal and in vitro experiments have not been considered. The consequences which might result from abandoning the hypothesis of linearity have not been directly examined in this draft, especially in respect to the concept of collective dose. Since another document dealing with 'Dose-response relationships for radiation-induced cancer' is in preparation, an effort has been made to avoid any overlap by making reference to that document whenever necessary

Scientific uncertainties associated with risk assessment of radiation

Energy Technology Data Exchange (ETDEWEB)

Hubert, P; Fagnani, F

1989-05-01

The proper use and interpretation of data pertaining to biological effects of ionizing radiations is based on a continuous effort to discuss the various assumptions and uncertainties in the process of risk assessment. In this perspective, it has been considered useful by the Committee to review critically the general scientific foundations that constitute the basic framework of data for the evaluation of health effects of radiation. This review is an attempt to identify the main sources of uncertainties, to give, when possible, an order of magnitude for their relative importance, and to clarify the principal interactions between the different steps of the process of risk quantification. The discussion has been restricted to stochastic effects and especially to cancer induction in man: observations at the cellular levels and animal and in vitro experiments have not been considered. The consequences which might result from abandoning the hypothesis of linearity have not been directly examined in this draft, especially in respect to the concept of collective dose. Since another document dealing with 'Dose-response relationships for radiation-induced cancer' is in preparation, an effort has been made to avoid any overlap by making reference to that document whenever necessary.

IAEA CRP on HTGR Uncertainties in Modeling: Assessment of Phase I Lattice to Core Model Uncertainties

Energy Technology Data Exchange (ETDEWEB)

Rouxelin, Pascal Nicolas [Idaho National Lab. (INL), Idaho Falls, ID (United States); Strydom, Gerhard [Idaho National Lab. (INL), Idaho Falls, ID (United States)

2016-09-01

Best-estimate plus uncertainty analysis of reactors is replacing the traditional conservative (stacked uncertainty) method for safety and licensing analysis. To facilitate uncertainty analysis applications, a comprehensive approach and methodology must be developed and applied. High temperature gas cooled reactors (HTGRs) have several features that require techniques not used in light-water reactor analysis (e.g., coated-particle design and large graphite quantities at high temperatures). The International Atomic Energy Agency has therefore launched the Coordinated Research Project on HTGR Uncertainty Analysis in Modeling to study uncertainty propagation in the HTGR analysis chain. The benchmark problem defined for the prismatic design is represented by the General Atomics Modular HTGR 350. The main focus of this report is the compilation and discussion of the results obtained for various permutations of Exercise I 2c and the use of the cross section data in Exercise II 1a of the prismatic benchmark, which is defined as the last and first steps of the lattice and core simulation phases, respectively. The report summarizes the Idaho National Laboratory (INL) best estimate results obtained for Exercise I 2a (fresh single-fuel block), Exercise I 2b (depleted single-fuel block), and Exercise I 2c (super cell) in addition to the first results of an investigation into the cross section generation effects for the super-cell problem. The two dimensional deterministic code known as the New ESC based Weighting Transport (NEWT) included in the Standardized Computer Analyses for Licensing Evaluation (SCALE) 6.1.2 package was used for the cross section evaluation, and the results obtained were compared to the three dimensional stochastic SCALE module KENO VI. The NEWT cross section libraries were generated for several permutations of the current benchmark super-cell geometry and were then provided as input to the Phase II core calculation of the stand alone neutronics Exercise

Risk assessment through drinking water pathway via uncertainty modeling of contaminant transport using soft computing

International Nuclear Information System (INIS)

Datta, D.; Ranade, A.K.; Pandey, M.; Sathyabama, N.; Kumar, Brij

2012-01-01

The basic objective of an environmental impact assessment (EIA) is to build guidelines to reduce the associated risk or mitigate the consequences of the reactor accident at its source to prevent deterministic health effects, to reduce the risk of stochastic health effects (eg. cancer and severe hereditary effects) as much as reasonable achievable by implementing protective actions in accordance with IAEA guidance (IAEA Safety Series No. 115, 1996). The measure of exposure being the basic tool to take any appropriate decisions related to risk reduction, EIA is traditionally expressed in terms of radiation exposure to the member of the public. However, models used to estimate the exposure received by the member of the public are governed by parameters some of which are deterministic with relative uncertainty and some of which are stochastic as well as imprecise (insufficient knowledge). In an admixture environment of this type, it is essential to assess the uncertainty of a model to estimate the bounds of the exposure to the public to invoke a decision during an event of nuclear or radiological emergency. With a view to this soft computing technique such as evidence theory based assessment of model parameters is addressed to compute the risk or exposure to the member of the public. The possible pathway of exposure to the member of the public in the aquatic food stream is the drinking of water. Accordingly, this paper presents the uncertainty analysis of exposure via uncertainty analysis of the contaminated water. Evidence theory finally addresses the uncertainty in terms of lower bound as belief measure and upper bound of exposure as plausibility measure. In this work EIA is presented using evidence theory. Data fusion technique is used to aggregate the knowledge on the uncertain information. Uncertainty of concentration and exposure is expressed as an interval of belief, plausibility

Measurement Uncertainty

Science.gov (United States)

Koch, Michael

Measurement uncertainty is one of the key issues in quality assurance. It became increasingly important for analytical chemistry laboratories with the accreditation to ISO/IEC 17025. The uncertainty of a measurement is the most important criterion for the decision whether a measurement result is fit for purpose. It also delivers help for the decision whether a specification limit is exceeded or not. Estimation of measurement uncertainty often is not trivial. Several strategies have been developed for this purpose that will shortly be described in this chapter. In addition the different possibilities to take into account the uncertainty in compliance assessment are explained.

Value chain and marketing margins of cassava: An assessment of ...

African Journals Online (AJOL)

Value chain and marketing margins of cassava: An assessment of cassava marketing in ... African Journal of Food, Agriculture, Nutrition and Development ... Cassava is one of the emerging market oriented agricultural commodities with ...

Data Quality Assessment of the Uncertainty Analysis Applied to the Greenhouse Gas Emissions of a Dairy Cow System

Directory of Open Access Journals (Sweden)

Chun-Youl Baek

2017-09-01

Full Text Available The results of an uncertainty analysis are achieved by the statistical information (standard error, type of probability distributions, and range of minimum and maximum of the selected input parameters. However, there are limitations in identifying sufficient data samples for the selected input parameters for statistical information in the field of life cycle assessment (LCA. Therefore, there is a strong need for a consistent screening procedure to identify the input parameters for use in uncertainty analysis in the area of LCA. The conventional procedure for identifying input parameters for the uncertainty analysis method includes assessing the data quality using the pedigree method and the contribution analysis of the LCA results. This paper proposes a simplified procedure for ameliorating the existing data quality assessment method, which can lead to an efficient uncertainly analysis of LCA results. The proposed method has two salient features: (i a simplified procedure based on contribution analysis followed by a data quality assessment for selecting the input parameters for the uncertainty analysis; and (ii a quantitative data quality assessment method is proposed, based on the pedigree method, that adopts the analytic hierarchy process (AHP method and quality function deployment (QFD. The effects of the uncertainty of the selected input parameters on the LCA results were assessed using the Monte Carlo simulation method. A case study of greenhouse gas (GHG emissions from a dairy cow system was used to demonstrate the applicability of the proposed procedure.

Addressing land use change and uncertainty in the life-cycle assessment of wheat-based bioethanol

International Nuclear Information System (INIS)

Malça, João; Freire, Fausto

2012-01-01

Despite the significant growth in the number of published life-cycle assessments of biofuels, important aspects have not captured sufficient attention, namely soil carbon emissions from land use change (LUC) and uncertainty analysis. The main goal of this article is to evaluate the implications of different LUC scenarios and uncertainty in the life-cycle energy renewability efficiency and GHG (greenhouse gases) intensity of wheat-based bioethanol replacing gasoline. A comprehensive assessment of different LUC scenarios (grassland or cropland converted to wheat cultivation) and agricultural practices is conducted, which results in different carbon stock change values. The types of uncertainty addressed include parameter uncertainty (propagated into LC (life-cycle) results using Monte-Carlo simulation) and uncertainty concerning how bioethanol co-product credits are accounted for. Results show that GHG emissions have considerably higher uncertainty than energy efficiency values, mainly due to soil carbon emissions from direct LUC and N 2 O release from cultivated soil. Moreover, LUC dominates the GHG intensity of bioethanol. Very different GHG emissions are calculated depending on the LUC scenario considered. Conversion of full- or low-tillage croplands to wheat cultivation results in bioethanol GHG emissions lower than gasoline emissions, whereas conversion of grassland does not contribute to bioethanol GHG savings over gasoline in the short- to mid-term. -- Highlights: ► We address different LUC scenarios and uncertainty in the LCA of wheat bioethanol. ► GHG emissions have considerably higher uncertainty than energy efficiency values. ► Bioethanol contributes to primary energy savings over gasoline. ► Very different life-cycle GHG emissions are calculated depending on the LUC scenario. ► GHG savings over gasoline are only achieved if cropland is the reference land use.

Application of a GIS-BIOLOCO tool for the design and assessment of biomass delivery chains

NARCIS (Netherlands)

Geijzendorffer, I.R.; Annevelink, E.; Elbersen, B.S.; Smidt, R.A.; Mol, de R.M.

2008-01-01

The spatial fragmentation of different biomass sources in one or more regions makes design and assessment of sustainable biomass delivery chains rather complicated. This paper presents a GIS tool that supports the design and facilitates a sustainability assessment of biomass delivery chains at a

Uncertainty on shallow landslide hazard assessment: from field data to hazard mapping

Science.gov (United States)

Trefolini, Emanuele; Tolo, Silvia; Patelli, Eduardo; Broggi, Matteo; Disperati, Leonardo; Le Tuan, Hai

2015-04-01

Shallow landsliding that involve Hillslope Deposits (HD), the surficial soil that cover the bedrock, is an important process of erosion, transport and deposition of sediment along hillslopes. Despite Shallow landslides generally mobilize relatively small volume of material, they represent the most hazardous factor in mountain regions due to their high velocity and the common absence of warning signs. Moreover, increasing urbanization and likely climate change make shallow landslides a source of widespread risk, therefore the interest of scientific community about this process grown in the last three decades. One of the main aims of research projects involved on this topic, is to perform robust shallow landslides hazard assessment for wide areas (regional assessment), in order to support sustainable spatial planning. Currently, three main methodologies may be implemented to assess regional shallow landslides hazard: expert evaluation, probabilistic (or data mining) methods and physical models based methods. The aim of this work is evaluate the uncertainty of shallow landslides hazard assessment based on physical models taking into account spatial variables such as: geotechnical and hydrogeologic parameters as well as hillslope morphometry. To achieve this goal a wide dataset of geotechnical properties (shear strength, permeability, depth and unit weight) of HD was gathered by integrating field survey, in situ and laboratory tests. This spatial database was collected from a study area of about 350 km2 including different bedrock lithotypes and geomorphological features. The uncertainty associated to each step of the hazard assessment process (e.g. field data collection, regionalization of site specific information and numerical modelling of hillslope stability) was carefully characterized. The most appropriate probability density function (PDF) was chosen for each numerical variable and we assessed the uncertainty propagation on HD strength parameters obtained by

Integrating uncertainty propagation in GNSS radio occultation retrieval: from excess phase to atmospheric bending angle profiles

Directory of Open Access Journals (Sweden)

J. Schwarz

2018-05-01

Full Text Available Global Navigation Satellite System (GNSS radio occultation (RO observations are highly accurate, long-term stable data sets and are globally available as a continuous record from 2001. Essential climate variables for the thermodynamic state of the free atmosphere – such as pressure, temperature, and tropospheric water vapor profiles (involving background information – can be derived from these records, which therefore have the potential to serve as climate benchmark data. However, to exploit this potential, atmospheric profile retrievals need to be very accurate and the remaining uncertainties quantified and traced throughout the retrieval chain from raw observations to essential climate variables. The new Reference Occultation Processing System (rOPS at the Wegener Center aims to deliver such an accurate RO retrieval chain with integrated uncertainty propagation. Here we introduce and demonstrate the algorithms implemented in the rOPS for uncertainty propagation from excess phase to atmospheric bending angle profiles, for estimated systematic and random uncertainties, including vertical error correlations and resolution estimates. We estimated systematic uncertainty profiles with the same operators as used for the basic state profiles retrieval. The random uncertainty is traced through covariance propagation and validated using Monte Carlo ensemble methods. The algorithm performance is demonstrated using test day ensembles of simulated data as well as real RO event data from the satellite missions CHAllenging Minisatellite Payload (CHAMP; Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC; and Meteorological Operational Satellite A (MetOp. The results of the Monte Carlo validation show that our covariance propagation delivers correct uncertainty quantification from excess phase to bending angle profiles. The results from the real RO event ensembles demonstrate that the new uncertainty estimation chain performs

Performance measurement of supply chain flexibility using witness

Directory of Open Access Journals (Sweden)

Rituraj Chandrakar

2012-10-01

Full Text Available In today’s global scenario of intense competition and environmental uncertainty flexibility in supply chain has an important role to play for the existence of any supply chain business. A need to be responsive to the constantly changing market scenario and cater to the customer needs, a certain degree of flexibility is required, which requires the coordination of many plants to produce and deliver goods to customers located in different places, and suppliers, which provide each plant with the required components. This paper intends to measure the degree of flexibility required for a two stage supply chain and assessing both the supplier flexibility and the assembler flexibility. In this paper, nine configurations of the SC are considered resulting from the combination of the three degrees of supplier and manufacturer flexibility, i.e. no flexibility, limited flexibility and total flexibility, respectively. Simulation model representing the different flexibility configurations are evaluated and the performance of each configuration analyzed to determine the flexibility configuration suitable to a supply chain. In particular the performance analysis of lead time, work-in-process, service level and cost are measured to determine the suitable flexibility.

Assessment of strategies for value chains using an extended Structure-Conduct-Performance (SCP) framework: an application to the honey business in Brazil

NARCIS (Netherlands)

Santana De Figueiredo Junior, H.

2015-01-01

Keywords: Strategy evaluation, global networks, supply chains, policy Delphi, conjoint analysis, economic development, competitiveness, beekeeping, interventions, uncertainty, upgrading.

Competition for the end-customer nowadays takes place more among networks of firms

U.S. Offshore Wind Manufacturing and Supply Chain Development

Energy Technology Data Exchange (ETDEWEB)

Hamilton, Bruce [Navigant Consulting, Inc., Burlington, MA (United States)

2013-02-22

This report seeks to provide an organized, analytical approach to identifying and bounding uncertainties around offshore wind manufacturing and supply chain capabilities; projecting potential component-level supply chain needs under three demand scenarios; and identifying key supply chain challenges and opportunities facing the future U.S. market and current suppliers of the nation’s landbased wind market.

Joint analysis of epistemic and aleatory uncertainty in stability analysis for geo-hazard assessments

Science.gov (United States)

Rohmer, Jeremy; Verdel, Thierry

2017-04-01

Uncertainty analysis is an unavoidable task of stability analysis of any geotechnical systems. Such analysis usually relies on the safety factor SF (if SF is below some specified threshold), the failure is possible). The objective of the stability analysis is then to estimate the failure probability P for SF to be below the specified threshold. When dealing with uncertainties, two facets should be considered as outlined by several authors in the domain of geotechnics, namely "aleatoric uncertainty" (also named "randomness" or "intrinsic variability") and "epistemic uncertainty" (i.e. when facing "vague, incomplete or imprecise information" such as limited databases and observations or "imperfect" modelling). The benefits of separating both facets of uncertainty can be seen from a risk management perspective because: - Aleatoric uncertainty, being a property of the system under study, cannot be reduced. However, practical actions can be taken to circumvent the potentially dangerous effects of such variability; - Epistemic uncertainty, being due to the incomplete/imprecise nature of available information, can be reduced by e.g., increasing the number of tests (lab or in site survey), improving the measurement methods or evaluating calculation procedure with model tests, confronting more information sources (expert opinions, data from literature, etc.). Uncertainty treatment in stability analysis usually restricts to the probabilistic framework to represent both facets of uncertainty. Yet, in the domain of geo-hazard assessments (like landslides, mine pillar collapse, rockfalls, etc.), the validity of this approach can be debatable. In the present communication, we propose to review the major criticisms available in the literature against the systematic use of probability in situations of high degree of uncertainty. On this basis, the feasibility of using a more flexible uncertainty representation tool is then investigated, namely Possibility distributions (e

Serum Immunoglobulin Free Light Chain Assessment in IgG4-Related Disease

Directory of Open Access Journals (Sweden)

Aurélie Grados

2013-01-01

Full Text Available Immunoglobulin free light chains are produced in excess during normal antibody synthesis. Their evaluation is commonly used in case of a monoclonal gammopathy. In polyclonal hypergammaglobulinemia related to the Sjögren syndrome or systemic lupus, erythematosus serum free light chain levels are increased and could correlate with disease activity. We show here that the κ ( and λ ( free light chains and the κ : λ ratio ( are increased in sixteen patients with IgG4-related disease when compared to healthy controls. The increase of κ and λ free light chains probably reflects the marked polyclonal B cell activation of the disease. We could not assess in this small cohort of patients a significative correlation of serum free light chain levels and disease activity or extension.

Network Security Risk Assessment System Based on Attack Graph and Markov Chain

Science.gov (United States)

Sun, Fuxiong; Pi, Juntao; Lv, Jin; Cao, Tian

2017-10-01

Network security risk assessment technology can be found in advance of the network problems and related vulnerabilities, it has become an important means to solve the problem of network security. Based on attack graph and Markov chain, this paper provides a Network Security Risk Assessment Model (NSRAM). Based on the network infiltration tests, NSRAM generates the attack graph by the breadth traversal algorithm. Combines with the international standard CVSS, the attack probability of atomic nodes are counted, and then the attack transition probabilities of ones are calculated by Markov chain. NSRAM selects the optimal attack path after comprehensive measurement to assessment network security risk. The simulation results show that NSRAM can reflect the actual situation of network security objectively.

Uncertainties in environmental impact assessments due to expert opinion. Case study. Radioactive waste in Slovenia

International Nuclear Information System (INIS)

Kontic, B.; Ravnik, M.

1998-01-01

A comprehensive study was done at the J. Stefan Institute in Ljubljana and the School of Environmental Sciences in Nova Gorica in relation to sources of uncertainties in long-term environmental impact assessment (EIA). Under the research two main components were examined: first, methodology of the preparation of an EIA, and second validity of an expert opinion. Following the findings of the research a survey was performed in relation to assessing acceptability of radioactive waste repository by the regulatory. The components of dose evaluation in different time frames were examined in terms of susceptibility to uncertainty. Uncertainty associated to human exposure in the far future is so large that dose and risk, as individual numerical indicators of safety, by our opinion, should not be used in compliance assessment for radioactive waste repository. On the other hand, results of the calculations on the amount and activity of low and intermediate level waste and the spent fuel from the Krsko NPP show that expert's understanding of the treated questions can be expressed in transparent way giving credible output of the models used.(author)

An integrated location inventory routing model in supply chain network designing under uncertainty

Directory of Open Access Journals (Sweden)

Hojat Angazi

2016-09-01

Full Text Available In this study an integrated model is proposed for the location inventory routing problem under uncertainty. This problem involves determining the location of distribution centers (DCs in a three echelon supply chain. The DCs receive orders from the customer and according to a continuous review inventory replenishment policy place orders to the supplier. The products are directly shipped from the supplier to the DCs. The vehicles start from the DCs to fulfill the demands of the customers. Determining the routing of the vehicles is one of the decisions involved in this problem. The demands of customers are stochastically distributed and the capacity of DCs are limited. If one of the DCs undergo a disruption and is unable to fulfill the demands of the customers, shortage may occur. Moreover in the proposed model the shortage is considered as partial backlogging. This means that if shortage occurs, some of the orders result in lost sales and other orders are fulfilled in the next period. In order to optimally solve the proposed model a nonlinear integer programming (INLP model is developed. However, since the problem is NP-hard, the mathematical formulation cannot be efficiently solved for large sized instances of the problem. Therefore an outer approximation method is developed to solve the problem more efficiently. The computational results show the efficiency of the proposed method.

Assessment of supply chain management and its impact on the ...

African Journals Online (AJOL)

GBK

This study assessed the status of supply chain management of laboratory ... Methods: The study was conducted in 39 health facilities (HFs) from eight districts in four ... avoid frequent stock-out of laboratory supplies, logistics management ...

Analyzing Bullwhip Effect in Supply Networks under Exogenous Uncertainty

Directory of Open Access Journals (Sweden)

Mitra Darvish

2014-05-01

Full Text Available This paper explains a model for analyzing and measuring the propagation of order amplifications (i.e. bullwhip effect for a single-product supply network topology considering exogenous uncertainty and linear and time-invariant inventory management policies for network entities. The stream of orders placed by each entity of the network is characterized assuming customer demand is ergodic. In fact, we propose an exact formula in order to measure the bullwhip effect in the addressed supply network topology considering the system in Markovian chain framework and presenting a matrix of network member relationships and relevant order sequences. The formula turns out using a mathematical method called frequency domain analysis. The major contribution of this paper is analyzing the bullwhip effect considering exogenous uncertainty in supply networks and using the Fourier transform in order to simplify the relevant calculations. We present a number of numerical examples to assess the analytical results accuracy in quantifying the bullwhip effect.

Developing a Model for Agile Supply: an Empirical Study from Iranian Pharmaceutical Supply Chain

Science.gov (United States)

Rajabzadeh Ghatari, Ali; Mehralian, Gholamhossein; Zarenezhad, Forouzandeh; Rasekh, Hamid Reza

2013-01-01

Agility is the fundamental characteristic of a supply chain needed for survival in turbulent markets, where environmental forces create additional uncertainty resulting in higher risk in the supply chain management. In addition, agility helps providing the right product, at the right time to the consumer. The main goal of this research is therefore to promote supplier selection in pharmaceutical industry according to the formative basic factors. Moreover, this paper can configure its supply network to achieve the agile supply chain. The present article analyzes the supply part of supply chain based on SCOR model, used to assess agile supply chains by highlighting their specific characteristics and applicability in providing the active pharmaceutical ingredient (API). This methodology provides an analytical modeling; the model enables potential suppliers to be assessed against the multiple criteria using both quantitative and qualitative measures. In addition, for making priority of critical factors, TOPSIS algorithm has been used as a common technique of MADM model. Finally, several factors such as delivery speed, planning and reorder segmentation, trust development and material quantity adjustment are identified and prioritized as critical factors for being agile in supply of API. PMID:24250689

Developing a model for agile supply: an empirical study from Iranian pharmaceutical supply chain.

Science.gov (United States)

Rajabzadeh Ghatari, Ali; Mehralian, Gholamhossein; Zarenezhad, Forouzandeh; Rasekh, Hamid Reza

2013-01-01

Agility is the fundamental characteristic of a supply chain needed for survival in turbulent markets, where environmental forces create additional uncertainty resulting in higher risk in the supply chain management. In addition, agility helps providing the right product, at the right time to the consumer. The main goal of this research is therefore to promote supplier selection in pharmaceutical industry according to the formative basic factors. Moreover, this paper can configure its supply network to achieve the agile supply chain. The present article analyzes the supply part of supply chain based on SCOR model, used to assess agile supply chains by highlighting their specific characteristics and applicability in providing the active pharmaceutical ingredient (API). This methodology provides an analytical modeling; the model enables potential suppliers to be assessed against the multiple criteria using both quantitative and qualitative measures. In addition, for making priority of critical factors, TOPSIS algorithm has been used as a common technique of MADM model. Finally, several factors such as delivery speed, planning and reorder segmentation, trust development and material quantity adjustment are identified and prioritized as critical factors for being agile in supply of API.

Uncertainty and sensitivity analysis using probabilistic system assessment code. 1

International Nuclear Information System (INIS)

Honma, Toshimitsu; Sasahara, Takashi.

1993-10-01

This report presents the results obtained when applying the probabilistic system assessment code under development to the PSACOIN Level 0 intercomparison exercise organized by the Probabilistic System Assessment Code User Group in the Nuclear Energy Agency (NEA) of OECD. This exercise is one of a series designed to compare and verify probabilistic codes in the performance assessment of geological radioactive waste disposal facilities. The computations were performed using the Monte Carlo sampling code PREP and post-processor code USAMO. The submodels in the waste disposal system were described and coded with the specification of the exercise. Besides the results required for the exercise, further additional uncertainty and sensitivity analyses were performed and the details of these are also included. (author)

Ex-plant consequence assessment for NUREG-1150: models, typical results, uncertainties

International Nuclear Information System (INIS)

Sprung, J.L.

1988-01-01

The assessment of ex-plant consequences for NUREG-1150 source terms was performed using the MELCOR Accident Consequence Code System (MACCS). This paper briefly discusses the following elements of MACCS consequence calculations: input data, phenomena modeled, computational framework, typical results, controlling phenomena, and uncertainties. Wherever possible, NUREG-1150 results will be used to illustrate the discussion. 28 references

RECOVERY ACT - Methods for Decision under Technological Change Uncertainty and Risk Assessment for Integrated Assessment of Climate Change

Energy Technology Data Exchange (ETDEWEB)

Webster, Mort D. [Pennsylvania State Univ., University Park, PA (United States). Dept. of Energy and Mineral Engineering

2015-11-30

This report presents the final outcomes and products of the project as performed both at the Massachusetts Institute of Technology and subsequently at Pennsylvania State University. The research project can be divided into three main components: methodology development for decision-making under uncertainty, improving the resolution of the electricity sector to improve integrated assessment, and application of these methods to integrated assessment.

RECOVERY ACT - Methods for Decision under Technological Change Uncertainty and Risk Assessment for Integrated Assessment of Climate Change

Energy Technology Data Exchange (ETDEWEB)

Webster, Mort David [MIT

2015-03-10

This report presents the final outcomes and products of the project as performed at the Massachusetts Institute of Technology. The research project consists of three main components: methodology development for decision-making under uncertainty, improving the resolution of the electricity sector to improve integrated assessment, and application of these methods to integrated assessment. Results in each area is described in the report.

Advanced LOCA code uncertainty assessment

International Nuclear Information System (INIS)

Wickett, A.J.; Neill, A.P.

1990-11-01

This report describes a pilot study that identified, quantified and combined uncertainties for the LOBI BL-02 3% small break test. A ''dials'' version of TRAC-PF1/MOD1, called TRAC-F, was used. (author)

Simulation codes and the impact of validation/uncertainty requirements

International Nuclear Information System (INIS)

Sills, H.E.

1995-01-01

Several of the OECD/CSNI members have adapted a proposed methodology for code validation and uncertainty assessment. Although the validation process adapted by members has a high degree of commonality, the uncertainty assessment processes selected are more variable, ranaing from subjective to formal. This paper describes the validation and uncertainty assessment process, the sources of uncertainty, methods of reducing uncertainty, and methods of assessing uncertainty.Examples are presented from the Ontario Hydro application of the validation methodology and uncertainty assessment to the system thermal hydraulics discipline and the TUF (1) system thermal hydraulics code. (author)

FRACTURE MECHANICS UNCERTAINTY ANALYSIS IN THE RELIABILITY ASSESSMENT OF THE REACTOR PRESSURE VESSEL: (2D SUBJECTED TO INTERNAL PRESSURE

Directory of Open Access Journals (Sweden)

Entin Hartini

2016-06-01

Full Text Available ABSTRACT FRACTURE MECHANICS UNCERTAINTY ANALYSIS IN THE RELIABILITY ASSESSMENT OF THE REACTOR PRESSURE VESSEL: (2D SUBJECTED TO INTERNAL PRESSURE. The reactor pressure vessel (RPV is a pressure boundary in the PWR type reactor which serves to confine radioactive material during chain reaction process. The integrity of the RPV must be guaranteed either  in a normal operation or accident conditions. In analyzing the integrity of RPV, especially related to the crack behavior which can introduce break to the reactor pressure vessel, a fracture mechanic approach should be taken for this assessment. The uncertainty of input used in the assessment, such as mechanical properties and physical environment, becomes a reason that the assessment is not sufficient if it is perfomed only by deterministic approach. Therefore, the uncertainty approach should be applied. The aim of this study is to analize the uncertainty of fracture mechanics calculations in evaluating the reliability of PWR`s reactor pressure vessel. Random character of input quantity was generated using probabilistic principles and theories. Fracture mechanics analysis is solved by Finite Element Method (FEM with  MSC MARC software, while uncertainty input analysis is done based on probability density function with Latin Hypercube Sampling (LHS using python script. The output of MSC MARC is a J-integral value, which is converted into stress intensity factor for evaluating the reliability of RPV’s 2D. From the result of the calculation, it can be concluded that the SIF from  probabilistic method, reached the limit value of  fracture toughness earlier than SIF from  deterministic method.  The SIF generated by the probabilistic method is 105.240 MPa m0.5. Meanwhile, the SIF generated by deterministic method is 100.876 MPa m0.5. Keywords: Uncertainty analysis, fracture mechanics, LHS, FEM, reactor pressure vessels   ABSTRAK ANALISIS KETIDAKPASTIAN FRACTURE MECHANIC PADA EVALUASI KEANDALAN

How risk and uncertainty is used in Supply Chain Management: a literature study

OpenAIRE

Bøge Sørensen, Lars

2004-01-01

Keywords Supply Chain Management, Risk Management, Supply Chain Risk Management Abstract To comply with Supply Chain Management dogma companies have cut their inventories to a minimum, lead times have been shortened, new suppliers have been chosen and the customer portfolio has been reduced. All of these activities impose a great deal of risk on the firms, jeopardizing the survival of entire supply chains. In this article the author intends to investigate and document the use a...

Spatial variability and parametric uncertainty in performance assessment models

International Nuclear Information System (INIS)

Pensado, Osvaldo; Mancillas, James; Painter, Scott; Tomishima, Yasuo

2011-01-01

The problem of defining an appropriate treatment of distribution functions (which could represent spatial variability or parametric uncertainty) is examined based on a generic performance assessment model for a high-level waste repository. The generic model incorporated source term models available in GoldSim ® , the TDRW code for contaminant transport in sparse fracture networks with a complex fracture-matrix interaction process, and a biosphere dose model known as BDOSE TM . Using the GoldSim framework, several Monte Carlo sampling approaches and transport conceptualizations were evaluated to explore the effect of various treatments of spatial variability and parametric uncertainty on dose estimates. Results from a model employing a representative source and ensemble-averaged pathway properties were compared to results from a model allowing for stochastic variation of transport properties along streamline segments (i.e., explicit representation of spatial variability within a Monte Carlo realization). We concluded that the sampling approach and the definition of an ensemble representative do influence consequence estimates. In the examples analyzed in this paper, approaches considering limited variability of a transport resistance parameter along a streamline increased the frequency of fast pathways resulting in relatively high dose estimates, while those allowing for broad variability along streamlines increased the frequency of 'bottlenecks' reducing dose estimates. On this basis, simplified approaches with limited consideration of variability may suffice for intended uses of the performance assessment model, such as evaluation of site safety. (author)

Uncertainty management in radioactive waste repository site assessment

International Nuclear Information System (INIS)

Baldwin, J.f.; Martin, T.P.; Tocatlidou

1994-01-01

The problem of performance assessment of a site to serve as a repository for the final disposal of radioactive waste involves different types of uncertainties. Their main sources include the large temporal and spatial considerations over which safety of the system has to be ensured, our inability to completely understand and describe a very complex structure such as the repository system, lack of precision in the measured information etc. These issues underlie most of the problems faced when rigid probabilistic approaches are used. Nevertheless a framework is needed, that would allow for an optimal aggregation of the available knowledge and an efficient management of the various types of uncertainty involved. In this work a knowledge-based modelling of the repository selection process is proposed that through a consequence analysis, evaluates the potential impact that hypothetical scenarios will have on a candidate site. The model is organised around a hierarchical structure, relating the scenarios with the possible events and processes that characterise them, and the site parameters. The scheme provides for both crisp and fuzzy parameter values and uses fuzzy semantic unification and evidential support logic reference mechanisms. It is implemented using the artificial intelligence language FRIL and the interaction with the user is performed through a windows interface

Assessment of the uncertainty associated with systematic errors in digital instruments: an experimental study on offset errors

International Nuclear Information System (INIS)

Attivissimo, F; Giaquinto, N; Savino, M; Cataldo, A

2012-01-01

This paper deals with the assessment of the uncertainty due to systematic errors, particularly in A/D conversion-based instruments. The problem of defining and assessing systematic errors is briefly discussed, and the conceptual scheme of gauge repeatability and reproducibility is adopted. A practical example regarding the evaluation of the uncertainty caused by the systematic offset error is presented. The experimental results, obtained under various ambient conditions, show that modelling the variability of systematic errors is more problematic than suggested by the ISO 5725 norm. Additionally, the paper demonstrates the substantial difference between the type B uncertainty evaluation, obtained via the maximum entropy principle applied to manufacturer's specifications, and the type A (experimental) uncertainty evaluation, which reflects actually observable reality. Although it is reasonable to assume a uniform distribution of the offset error, experiments demonstrate that the distribution is not centred and that a correction must be applied. In such a context, this work motivates a more pragmatic and experimental approach to uncertainty, with respect to the directions of supplement 1 of GUM. (paper)

A probabilistic approach to quantify the uncertainties in internal dose assessment using response surface and neural network

International Nuclear Information System (INIS)

Baek, M.; Lee, S.K.; Lee, U.C.; Kang, C.S.

1996-01-01

A probabilistic approach is formulated to assess the internal radiation exposure following the intake of radioisotopes. This probabilistic approach consists of 4 steps as follows: (1) screening, (2) quantification of uncertainties, (3) propagation of uncertainties, and (4) analysis of output. The approach has been applied for Pu-induced internal dose assessment and a multi-compartment dosimetric model is used for internal transport. In this approach, surrogate models of original system are constructed using response and neural network. And the results of these surrogate models are compared with those of original model. Each surrogate model well approximates the original model. The uncertainty and sensitivity analysis of the model parameters are evaluated in this process. Dominant contributors to each organ are identified and the results show that this approach could serve a good tool of assessing the internal radiation exposure

Methodology for quantifying uncertainty in coal assessments with an application to a Texas lignite deposit

Energy Technology Data Exchange (ETDEWEB)

Olea, Ricardo A.; Luppens, James A.; Tewalt, Susan J. [U.S. Geological Survey, Reston, VA (United States)

2011-01-01

A common practice for characterizing uncertainty in coal resource assessments has been the itemization of tonnage at the mining unit level and the classification of such units according to distance to drilling holes. Distance criteria, such as those used in U.S. Geological Survey Circular 891, are still widely used for public disclosure. A major deficiency of distance methods is that they do not provide a quantitative measure of uncertainty. Additionally, relying on distance between data points alone does not take into consideration other factors known to have an influence on uncertainty, such as spatial correlation, type of probability distribution followed by the data, geological discontinuities, and boundary of the deposit. Several geostatistical methods have been combined to formulate a quantitative characterization for appraising uncertainty. Drill hole datasets ranging from widespread exploration drilling to detailed development drilling from a lignite deposit in Texas were used to illustrate the modeling. The results show that distance to the nearest drill hole is almost completely unrelated to uncertainty, which confirms the inadequacy of characterizing uncertainty based solely on a simple classification of resources by distance classes. The more complex statistical methods used in this study quantify uncertainty and show good agreement between confidence intervals in the uncertainty predictions and data from additional drilling. (author)

Methodology for quantifying uncertainty in coal assessments with an application to a Texas lignite deposit

Science.gov (United States)

Olea, R.A.; Luppens, J.A.; Tewalt, S.J.

2011-01-01

A common practice for characterizing uncertainty in coal resource assessments has been the itemization of tonnage at the mining unit level and the classification of such units according to distance to drilling holes. Distance criteria, such as those used in U.S. Geological Survey Circular 891, are still widely used for public disclosure. A major deficiency of distance methods is that they do not provide a quantitative measure of uncertainty. Additionally, relying on distance between data points alone does not take into consideration other factors known to have an influence on uncertainty, such as spatial correlation, type of probability distribution followed by the data, geological discontinuities, and boundary of the deposit. Several geostatistical methods have been combined to formulate a quantitative characterization for appraising uncertainty. Drill hole datasets ranging from widespread exploration drilling to detailed development drilling from a lignite deposit in Texas were used to illustrate the modeling. The results show that distance to the nearest drill hole is almost completely unrelated to uncertainty, which confirms the inadequacy of characterizing uncertainty based solely on a simple classification of resources by distance classes. The more complex statistical methods used in this study quantify uncertainty and show good agreement between confidence intervals in the uncertainty predictions and data from additional drilling. ?? 2010.

Dispelling urban myths about default uncertainty factors in chemical risk assessment – sufficient protection against mixture effects?

Science.gov (United States)

2013-01-01

Assessing the detrimental health effects of chemicals requires the extrapolation of experimental data in animals to human populations. This is achieved by applying a default uncertainty factor of 100 to doses not found to be associated with observable effects in laboratory animals. It is commonly assumed that the toxicokinetic and toxicodynamic sub-components of this default uncertainty factor represent worst-case scenarios and that the multiplication of those components yields conservative estimates of safe levels for humans. It is sometimes claimed that this conservatism also offers adequate protection from mixture effects. By analysing the evolution of uncertainty factors from a historical perspective, we expose that the default factor and its sub-components are intended to represent adequate rather than worst-case scenarios. The intention of using assessment factors for mixture effects was abandoned thirty years ago. It is also often ignored that the conservatism (or otherwise) of uncertainty factors can only be considered in relation to a defined level of protection. A protection equivalent to an effect magnitude of 0.001-0.0001% over background incidence is generally considered acceptable. However, it is impossible to say whether this level of protection is in fact realised with the tolerable doses that are derived by employing uncertainty factors. Accordingly, it is difficult to assess whether uncertainty factors overestimate or underestimate the sensitivity differences in human populations. It is also often not appreciated that the outcome of probabilistic approaches to the multiplication of sub-factors is dependent on the choice of probability distributions. Therefore, the idea that default uncertainty factors are overly conservative worst-case scenarios which can account both for the lack of statistical power in animal experiments and protect against potential mixture effects is ill-founded. We contend that precautionary regulation should provide an

Data related uncertainty in near-surface vulnerability assessments for agrochemicals in the San Joaquin Valley.

Science.gov (United States)

Loague, Keith; Blanke, James S; Mills, Melissa B; Diaz-Diaz, Ricardo; Corwin, Dennis L

2012-01-01

Precious groundwater resources across the United States have been contaminated due to decades-long nonpoint-source applications of agricultural chemicals. Assessing the impact of past, ongoing, and future chemical applications for large-scale agriculture operations is timely for designing best-management practices to prevent subsurface pollution. Presented here are the results from a series of regional-scale vulnerability assessments for the San Joaquin Valley (SJV). Two relatively simple indices, the retardation and attenuation factors, are used to estimate near-surface vulnerabilities based on the chemical properties of 32 pesticides and the variability of both soil characteristics and recharge rates across the SJV. The uncertainties inherit to these assessments, derived from the uncertainties within the chemical and soil data bases, are estimated using first-order analyses. The results are used to screen and rank the chemicals based on mobility and leaching potential, without and with consideration of data-related uncertainties. Chemicals of historic high visibility in the SJV (e.g., atrazine, DBCP [dibromochloropropane], ethylene dibromide, and simazine) are ranked in the top half of those considered. Vulnerability maps generated for atrazine and DBCP, featured for their legacy status in the study area, clearly illustrate variations within and across the assessments. For example, the leaching potential is greater for DBCP than for atrazine, the leaching potential for DBCP is greater for the spatially variable recharge values than for the average recharge rate, and the leaching potentials for both DBCP and atrazine are greater for the annual recharge estimates than for the monthly recharge estimates. The data-related uncertainties identified in this study can be significant, targeting opportunities for improving future vulnerability assessments. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America

A Bayesian belief network approach for assessing uncertainty in conceptual site models at contaminated sites

Science.gov (United States)

Thomsen, Nanna I.; Binning, Philip J.; McKnight, Ursula S.; Tuxen, Nina; Bjerg, Poul L.; Troldborg, Mads

2016-05-01

A key component in risk assessment of contaminated sites is in the formulation of a conceptual site model (CSM). A CSM is a simplified representation of reality and forms the basis for the mathematical modeling of contaminant fate and transport at the site. The CSM should therefore identify the most important site-specific features and processes that may affect the contaminant transport behavior at the site. However, the development of a CSM will always be associated with uncertainties due to limited data and lack of understanding of the site conditions. CSM uncertainty is often found to be a major source of model error and it should therefore be accounted for when evaluating uncertainties in risk assessments. We present a Bayesian belief network (BBN) approach for constructing CSMs and assessing their uncertainty at contaminated sites. BBNs are graphical probabilistic models that are effective for integrating quantitative and qualitative information, and thus can strengthen decisions when empirical data are lacking. The proposed BBN approach facilitates a systematic construction of multiple CSMs, and then determines the belief in each CSM using a variety of data types and/or expert opinion at different knowledge levels. The developed BBNs combine data from desktop studies and initial site investigations with expert opinion to assess which of the CSMs are more likely to reflect the actual site conditions. The method is demonstrated on a Danish field site, contaminated with chlorinated ethenes. Four different CSMs are developed by combining two contaminant source zone interpretations (presence or absence of a separate phase contamination) and two geological interpretations (fractured or unfractured clay till). The beliefs in each of the CSMs are assessed sequentially based on data from three investigation stages (a screening investigation, a more detailed investigation, and an expert consultation) to demonstrate that the belief can be updated as more information

Effect of Streamflow Forecast Uncertainty on Real-Time Reservoir Operation

Science.gov (United States)

Zhao, T.; Cai, X.; Yang, D.

2010-12-01

Various hydrological forecast products have been applied to real-time reservoir operation, including deterministic streamflow forecast (DSF), DSF-based probabilistic streamflow forecast (DPSF), and ensemble streamflow forecast (ESF), which represent forecast uncertainty in the form of deterministic forecast error, deterministic forecast error-based uncertainty distribution, and ensemble forecast errors, respectively. Compared to previous studies that treat these forecast products as ad hoc inputs for reservoir operation models, this paper attempts to model the uncertainties involved in the various forecast products and explores their effect on real-time reservoir operation decisions. In hydrology, there are various indices reflecting the magnitude of streamflow forecast uncertainty; meanwhile, few models illustrate the forecast uncertainty evolution process. This research introduces Martingale Model of Forecast Evolution (MMFE) from supply chain management and justifies its assumptions for quantifying the evolution of uncertainty in streamflow forecast as time progresses. Based on MMFE, this research simulates the evolution of forecast uncertainty in DSF, DPSF, and ESF, and applies the reservoir operation models (dynamic programming, DP; stochastic dynamic programming, SDP; and standard operation policy, SOP) to assess the effect of different forms of forecast uncertainty on real-time reservoir operation. Through a hypothetical single-objective real-time reservoir operation model, the results illustrate that forecast uncertainty exerts significant effects. Reservoir operation efficiency, as measured by a utility function, decreases as the forecast uncertainty increases. Meanwhile, these effects also depend on the type of forecast product being used. In general, the utility of reservoir operation with ESF is nearly as high as the utility obtained with a perfect forecast; the utilities of DSF and DPSF are similar to each other but not as efficient as ESF. Moreover

Dependencies, human interactions and uncertainties in probabilistic safety assessment

International Nuclear Information System (INIS)

Hirschberg, S.

1990-01-01

In the context of Probabilistic Safety Assessment (PSA), three areas were investigated in a 4-year Nordic programme: dependencies with special emphasis on common cause failures, human interactions and uncertainty aspects. The approach was centered around comparative analyses in form of Benchmark/Reference Studies and retrospective reviews. Weak points in available PSAs were identified and recommendations were made aiming at improving consistency of the PSAs. The sensitivity of PSA-results to basic assumptions was demonstrated and the sensitivity to data assignment and to choices of methods for analysis of selected topics was investigated. (author)

Parameter-induced uncertainty quantification of soil N2O, NO and CO2 emission from Höglwald spruce forest (Germany using the LandscapeDNDC model

Directory of Open Access Journals (Sweden)

K. Butterbach-Bahl

2012-10-01

Full Text Available Assessing the uncertainties of simulation results of ecological models is becoming increasingly important, specifically if these models are used to estimate greenhouse gas emissions on site to regional/national levels. Four general sources of uncertainty effect the outcome of process-based models: (i uncertainty of information used to initialise and drive the model, (ii uncertainty of model parameters describing specific ecosystem processes, (iii uncertainty of the model structure, and (iv accurateness of measurements (e.g., soil-atmosphere greenhouse gas exchange which are used for model testing and development. The aim of our study was to assess the simulation uncertainty of the process-based biogeochemical model LandscapeDNDC. For this we set up a Bayesian framework using a Markov Chain Monte Carlo (MCMC method, to estimate the joint model parameter distribution. Data for model testing, parameter estimation and uncertainty assessment were taken from observations of soil fluxes of nitrous oxide (N2O, nitric oxide (NO and carbon dioxide (CO2 as observed over a 10 yr period at the spruce site of the Höglwald Forest, Germany. By running four independent Markov Chains in parallel with identical properties (except for the parameter start values, an objective criteria for chain convergence developed by Gelman et al. (2003 could be used. Our approach shows that by means of the joint parameter distribution, we were able not only to limit the parameter space and specify the probability of parameter values, but also to assess the complex dependencies among model parameters used for simulating soil C and N trace gas emissions. This helped to improve the understanding of the behaviour of the complex LandscapeDNDC model while simulating soil C and N turnover processes and associated C and N soil-atmosphere exchange. In a final step the parameter distribution of the most sensitive parameters determining soil-atmosphere C and N exchange were used to obtain

Identification and assessment of potential vulnerabilities in the poultry meat production chain to dangerous agents and substances

NARCIS (Netherlands)

Schwägele, F.C.; Andrée, S.; Beraquet, N.; Castrillon, M.; Winkel, C.; Garforth, D.; Cnossen, H.J.; Lucas Luijckx, N.B.; Ayalew, G.

2009-01-01

The specific targeted European research project ΣChain (2006) addresses existing as well as potential vulnerabilities within food chains. One of the food chains within the focus of ΣChain is dealing with poultry meat. Fundamental for the assessment of potential vulnerabilities in the chain is basic

Microbiological risk from minimally processed packaged salads in the Dutch food chain.

Science.gov (United States)

Pielaat, Annemarie; van Leusden, Frans M; Wijnands, Lucas M

2014-03-01

The objective of this study was to evaluate the microbial hazard associated with the consumption of mixed salads produced under standard conditions. The presence of Salmonella, Campylobacter spp., and Escherichia coli O157 in the Dutch production chain of mixed salads was determined. Microbial prevalence and concentration data from a microbiological surveillance study were used as inputs for the quantitative microbial risk assessment. Chain logistics, production figures, and consumption patterns were combined with the survey data for the risk assessment chain approach. The results of the sample analysis were used to track events from contamination through human illness. Wide 95% confidence intervals around the mean were found for estimated annual numbers of illnesses resulting from the consumption of mixed salads contaminated with Salmonella Typhimurium DT104 (0 to 10,300 cases), Campylobacter spp. (0 to 92,000 cases), or E. coli (0 to 800 cases). The main sources of uncertainty are the lack of decontamination data (i.e., produce washing during processing) and an appropriate dose-response relationship.

Assessment the impact of samplers change on the uncertainty related to geothermalwater sampling

Science.gov (United States)

Wątor, Katarzyna; Mika, Anna; Sekuła, Klaudia; Kmiecik, Ewa

2018-02-01

The aim of this study is to assess the impact of samplers change on the uncertainty associated with the process of the geothermal water sampling. The study was carried out on geothermal water exploited in Podhale region, southern Poland (Małopolska province). To estimate the uncertainty associated with sampling the results of determinations of metasilicic acid (H2SiO3) in normal and duplicate samples collected in two series were used (in each series the samples were collected by qualified sampler). Chemical analyses were performed using ICP-OES method in the certified Hydrogeochemical Laboratory of the Hydrogeology and Engineering Geology Department at the AGH University of Science and Technology in Krakow (Certificate of Polish Centre for Accreditation No. AB 1050). To evaluate the uncertainty arising from sampling the empirical approach was implemented, based on double analysis of normal and duplicate samples taken from the same well in the series of testing. The analyses of the results were done using ROBAN software based on technique of robust statistics analysis of variance (rANOVA). Conducted research proved that in the case of qualified and experienced samplers uncertainty connected with the sampling can be reduced what results in small measurement uncertainty.

Uncertainty quantification in wind farm flow models

DEFF Research Database (Denmark)

Murcia Leon, Juan Pablo

uncertainties through a model chain are presented and applied to several wind energy related problems such as: annual energy production estimation, wind turbine power curve estimation, wake model calibration and validation, and estimation of lifetime equivalent fatigue loads on a wind turbine. Statistical...

Quantifying and Reducing Curve-Fitting Uncertainty in Isc

Energy Technology Data Exchange (ETDEWEB)

Campanelli, Mark; Duck, Benjamin; Emery, Keith

2015-06-14

Current-voltage (I-V) curve measurements of photovoltaic (PV) devices are used to determine performance parameters and to establish traceable calibration chains. Measurement standards specify localized curve fitting methods, e.g., straight-line interpolation/extrapolation of the I-V curve points near short-circuit current, Isc. By considering such fits as statistical linear regressions, uncertainties in the performance parameters are readily quantified. However, the legitimacy of such a computed uncertainty requires that the model be a valid (local) representation of the I-V curve and that the noise be sufficiently well characterized. Using more data points often has the advantage of lowering the uncertainty. However, more data points can make the uncertainty in the fit arbitrarily small, and this fit uncertainty misses the dominant residual uncertainty due to so-called model discrepancy. Using objective Bayesian linear regression for straight-line fits for Isc, we investigate an evidence-based method to automatically choose data windows of I-V points with reduced model discrepancy. We also investigate noise effects. Uncertainties, aligned with the Guide to the Expression of Uncertainty in Measurement (GUM), are quantified throughout.

Assessment of measurement result uncertainty in determination of 210Pb with the focus on matrix composition effect in gamma-ray spectrometry

International Nuclear Information System (INIS)

Iurian, A.R.; Pitois, A.; Kis-Benedek, G.; Migliori, A.; Padilla-Alvarez, R.; Ceccatelli, A.

2016-01-01

Reference materials were used to assess measurement result uncertainty in determination of 210 Pb by gamma-ray spectrometry, liquid scintillation counting, or indirectly by alpha-particle spectrometry, using its daughter 210 Po in radioactive equilibrium. Combined standard uncertainties of 210 Pb massic activities obtained by liquid scintillation counting are in the range 2–12%, depending on matrices and massic activity values. They are in the range 1–3% for the measurement of its daughter 210 Po using alpha-particle spectrometry. Three approaches (direct computation of counting efficiency and efficiency transfer approaches based on the computation and, respectively, experimental determination of the efficiency transfer factors) were applied for the evaluation of 210 Pb using gamma-ray spectrometry. Combined standard uncertainties of gamma-ray spectrometry results were found in the range 2–17%. The effect of matrix composition on self-attenuation was investigated and a detailed assessment of uncertainty components was performed. - Highlights: • Confirmed 210 Pb certified values by LSC and alpha-particle spectrometry ( 210 Po). • Assessed 210 Po measurement result uncertainty by alpha-particle spectrometry. • Matrix composition effect on gamma-ray spectrometry measurement result uncertainty. • Assessment of 210 Pb measurement result uncertainty by gamma-ray spectrometry. • Comparison of techniques and approaches: ‘fit-for-purpose’ considerations.

International wind power development. The 2012 supply chain assessment. Forecast 2012-2015

Energy Technology Data Exchange (ETDEWEB)

2011-11-15

The entire wind power supply chain is under pressure. Fierce competition among turbine OEMs (Original Equipment Manufactures), particularly in China, has decreased turbine prices to the extent that turbine OEMs and sub-suppliers are no longer realizing a profit. This is the first time in Chinese wind power history that many sub-suppliers have had to reduce their production capacity; even a large component supplier recently went bankrupt. The wind industry has entered a stage where strategic decision making is needed. How can the suppliers of components and materials survive this new reality? What are the latest supply chain management strategies of the world's top 10 turbine OEMs as a response to slumping demand? This 200+ page supply chain assessment, with the updated status of supply chain activities as of November 2011, addresses these questions. The report assesses more than 300 suppliers of eight key components (blades, gearboxes, electric generators, bearings, power converters, transformers, towers, pitch systems and balance of plant - offshore) and more than 200 suppliers of five groups of key materials (castings, forgings, reinforcement fibers, resins and rare earth materials). (LN)

An end-to-end assessment of range uncertainty in proton therapy using animal tissues

Science.gov (United States)

Zheng, Yuanshui; Kang, Yixiu; Zeidan, Omar; Schreuder, Niek

2016-11-01

Accurate assessment of range uncertainty is critical in proton therapy. However, there is a lack of data and consensus on how to evaluate the appropriate amount of uncertainty. The purpose of this study is to quantify the range uncertainty in various treatment conditions in proton therapy, using transmission measurements through various animal tissues. Animal tissues, including a pig head, beef steak, and lamb leg, were used in this study. For each tissue, an end-to-end test closely imitating patient treatments was performed. This included CT scan simulation, treatment planning, image-guided alignment, and beam delivery. Radio-chromic films were placed at various depths in the distal dose falloff region to measure depth dose. Comparisons between measured and calculated doses were used to evaluate range differences. The dose difference at the distal falloff between measurement and calculation depends on tissue type and treatment conditions. The estimated range difference was up to 5, 6 and 4 mm for the pig head, beef steak, and lamb leg irradiation, respectively. Our study shows that the TPS was able to calculate proton range within about 1.5% plus 1.5 mm. Accurate assessment of range uncertainty in treatment planning would allow better optimization of proton beam treatment, thus fully achieving proton beams’ superior dose advantage over conventional photon-based radiation therapy.

Uncertainty of the Soil–Water Characteristic Curve and Its Effects on Slope Seepage and Stability Analysis under Conditions of Rainfall Using the Markov Chain Monte Carlo Method

Directory of Open Access Journals (Sweden)

Weiping Liu

2017-10-01

Full Text Available It is important to determine the soil–water characteristic curve (SWCC for analyzing slope seepage and stability under the conditions of rainfall. However, SWCCs exhibit high uncertainty because of complex influencing factors, which has not been previously considered in slope seepage and stability analysis under conditions of rainfall. This study aimed to evaluate the uncertainty of the SWCC and its effects on the seepage and stability analysis of an unsaturated soil slope under conditions of rainfall. The SWCC model parameters were treated as random variables. An uncertainty evaluation of the parameters was conducted based on the Bayesian approach and the Markov chain Monte Carlo (MCMC method. Observed data from granite residual soil were used to test the uncertainty of the SWCC. Then, different confidence intervals for the model parameters of the SWCC were constructed. The slope seepage and stability analysis under conditions of rainfall with the SWCC of different confidence intervals was investigated using finite element software (SEEP/W and SLOPE/W. The results demonstrated that SWCC uncertainty had significant effects on slope seepage and stability. In general, the larger the percentile value, the greater the reduction of negative pore-water pressure in the soil layer and the lower the safety factor of the slope. Uncertainties in the model parameters of the SWCC can lead to obvious errors in predicted pore-water pressure profiles and the estimated safety factor of the slope under conditions of rainfall.

The French biofuels mandates under cost uncertainty - an assessment based on robust optimization

International Nuclear Information System (INIS)

Lorne, Daphne; Tchung-Ming, Stephane

2012-01-01

This paper investigates the impact of primary energy and technology cost uncertainty on the achievement of renewable and especially biofuel policies - mandates and norms - in France by 2030. A robust optimization technique that allows to deal with uncertainty sets of high dimensionality is implemented in a TIMES-based long-term planning model of the French energy transport and electricity sectors. The energy system costs and potential benefits (GHG emissions abatements, diversification) of the French renewable mandates are assessed within this framework. The results of this systemic analysis highlight how setting norms and mandates allows to reduce the variability of CO 2 emissions reductions and supply mix diversification when the costs of technological progress and prices are uncertain. Beyond that, we discuss the usefulness of robust optimization in complement of other techniques to integrate uncertainty in large-scale energy models. (authors)

Assessing 5 years of GOSAT Proxy XCH4 data and associated uncertainties

Directory of Open Access Journals (Sweden)

R. J. Parker

2015-11-01

Full Text Available We present 5 years of GOSAT XCH4 retrieved using the "proxy" approach. The Proxy XCH4 data are validated against ground-based TCCON observations and are found to be of high quality with a small bias of 4.8 ppb (∼ 0.27 % and a single-sounding precision of 13.4 ppb (∼ 0.74 %. The station-to-station bias (ameasure of the relative accuracy is found to be 4.2 ppb. For the first time the XCH4 / XCO2 ratio component of the Proxy retrieval is validated (bias of 0.014 ppb ppm−1 (∼ 0.30 %, single-sounding precision of 0.033 ppb ppm−1 (∼ 0.72 %. The uncertainty relating to the model XCO2 component of the Proxy XCH4 is assessed through the use of an ensemble of XCO2 models. While each individual XCO2 model is found to agree well with the TCCON validation data (r = 0.94–0.97, it is not possible to select one model as the best from our comparisons. The median XCO2 value of the ensemble has a smaller scatter against TCCON (a standard deviation of 0.92 ppm than any of the individual models whilst maintaining a small bias (0.15 ppm. This model median XCO2 is used to calculate the Proxy XCH4 with the maximum deviation of the ensemble from the median used as an estimate of the uncertainty. We compare this uncertainty to the a posteriori retrieval error (which is assumed to reduce with sqrt(N and find typically that the model XCO2 uncertainty becomes significant during summer months when the a posteriori error is at its lowest due to the increase in signal related to increased summertime reflected sunlight. We assess the significance of these model and retrieval uncertainties on flux inversion by comparing the GOSAT XCH4 against modelled XCH4 from TM5-4DVAR constrained by NOAA surface observations (MACC reanalysis scenario S1-NOAA. We find that for the majority of regions the differences are much larger than the estimated uncertainties. Our findings show that useful information will be provided to the inversions for the majority of regions in addition

Living with uncertainty: from the precautionary principle to the methodology of ongoing normative assessment

International Nuclear Information System (INIS)

Dupuy, J.P.; Grinbaum, A.

2005-01-01

The analysis of our epistemic situation regarding singular events, such as abrupt climate change, shows essential limitations in the traditional modes of dealing with uncertainty. Typical cognitive barriers lead to the paralysis of action. What is needed is taking seriously the reality of the future. We argue for the application of the methodology of ongoing normative assessment. We show that it is, paradoxically, a matter of forming a project on the basis of a fixed future which one does not want, and this in a coordinated way at the level of social institutions. Ongoing assessment may be viewed as a prescription to live with uncertainty, in a particular sense of the term, in order for a future catastrophe not to occur. The assessment is necessarily normative in that it must include the anticipation of a retrospective ethical judgment on present choices (notion of moral luck). (authors)

Process modeling and supply chain design for advanced biofuel production based on bio-oil gasification

Science.gov (United States)

Li, Qi

As a potential substitute for petroleum-based fuel, second generation biofuels are playing an increasingly important role due to their economic, environmental, and social benefits. With the rapid development of biofuel industry, there has been an increasing literature on the techno-economic analysis and supply chain design for biofuel production based on a variety of production pathways. A recently proposed production pathway of advanced biofuel is to convert biomass to bio-oil at widely distributed small-scale fast pyrolysis plants, then gasify the bio-oil to syngas and upgrade the syngas to transportation fuels in centralized biorefinery. This thesis aims to investigate two types of assessments on this bio-oil gasification pathway: techno-economic analysis based on process modeling and literature data; supply chain design with a focus on optimal decisions for number of facilities to build, facility capacities and logistic decisions considering uncertainties. A detailed process modeling with corn stover as feedstock and liquid fuels as the final products is presented. Techno-economic analysis of the bio-oil gasification pathway is also discussed to assess the economic feasibility. Some preliminary results show a capital investment of 438 million dollar and minimum fuel selling price (MSP) of $5.6 per gallon of gasoline equivalent. The sensitivity analysis finds that MSP is most sensitive to internal rate of return (IRR), biomass feedstock cost, and fixed capital cost. A two-stage stochastic programming is formulated to solve the supply chain design problem considering uncertainties in biomass availability, technology advancement, and biofuel price. The first-stage makes the capital investment decisions including the locations and capacities of the decentralized fast pyrolysis plants and the centralized biorefinery while the second-stage determines the biomass and biofuel flows. The numerical results and case study illustrate that considering uncertainties can be

A new uncertainty importance measure

International Nuclear Information System (INIS)

Borgonovo, E.

2007-01-01

Uncertainty in parameters is present in many risk assessment problems and leads to uncertainty in model predictions. In this work, we introduce a global sensitivity indicator which looks at the influence of input uncertainty on the entire output distribution without reference to a specific moment of the output (moment independence) and which can be defined also in the presence of correlations among the parameters. We discuss its mathematical properties and highlight the differences between the present indicator, variance-based uncertainty importance measures and a moment independent sensitivity indicator previously introduced in the literature. Numerical results are discussed with application to the probabilistic risk assessment model on which Iman [A matrix-based approach to uncertainty and sensitivity analysis for fault trees. Risk Anal 1987;7(1):22-33] first introduced uncertainty importance measures

Coordinating a Supply Chain with Risk-Averse Agents under Demand and Consumer Returns Uncertainty

Directory of Open Access Journals (Sweden)

Jian Liu

2013-01-01

Full Text Available This paper examines the optimal order decision in a supply chain when it faces uncertain demand and uncertain consumer returns. We build consumer returns model with decision-makers’ risk preference under mean-variance objective framework and discuss supply chain coordination problem under wholesale-price-only policy and the manufacturer’s buyback policy, respectively. We find that, with wholesale price policy, the supply chain cannot be coordinated whether the supply chain agents are risk-neutral or risk-averse. However, with buyback policy, the supply chain can be coordinated and the profit of the supply chain can be arbitrarily allocated between the manufacturer and the retailer. Through numerical examples, we illustrate the impact of stochastic consumer returns and the supply chain agents’ risk attitude on the optimal order decision.

Risk assessment of supply chain for pharmaceutical excipients with AHP-fuzzy comprehensive evaluation.

Science.gov (United States)

Li, Maozhong; Du, Yunai; Wang, Qiyue; Sun, Chunmeng; Ling, Xiang; Yu, Boyang; Tu, Jiasheng; Xiong, Yerong

2016-04-01

As the essential components in formulations, pharmaceutical excipients directly affect the safety, efficacy, and stability of drugs. Recently, safety incidents of pharmaceutical excipients posing seriously threats to the patients highlight the necessity of controlling the potential risks. Hence, it is indispensable for the industry to establish an effective risk assessment system of supply chain. In this study, an AHP-fuzzy comprehensive evaluation model was developed based on the analytic hierarchy process and fuzzy mathematical theory, which quantitatively assessed the risks of supply chain. Taking polysorbate 80 as the example for model analysis, it was concluded that polysorbate 80 for injection use is a high-risk ingredient in the supply chain compared to that for oral use to achieve safety application in clinic, thus measures should be taken to control and minimize those risks.

Assessing the social sustainability contribution of an infrastructure project under conditions of uncertainty

International Nuclear Information System (INIS)

Sierra, Leonardo A.; Yepes, Víctor; Pellicer, Eugenio

2017-01-01

Assessing the viability of a public infrastructure includes economic, technical and environmental aspects; however, on many occasions, the social aspects are not always adequately considered. This article proposes a procedure to estimate the social sustainability of infrastructure projects under conditions of uncertainty, based on a multicriteria deterministic method. The variability of the method inputs is contributed by the decision-makers. Uncertain inputs are treated through uniform and beta PERT distributions. The Monte Carlo method is used to propagate uncertainty in the method. A case study of a road infrastructure improvement in El Salvador is used to illustrate this treatment. The main results determine the variability of the short and long-term social improvement indices by infrastructure and the probability of the position in the prioritization of the alternatives. The proposed mechanism improves the reliability of the decision making early in infrastructure projects, taking their social contribution into account. The results can complement environmental and economic sustainability assessments. - Highlights: •Estimate the social sustainability of infrastructure projects under conditions of uncertainty •The method uses multicriteria and Monte Carlo techniques and beta PERT distributions •Determines variability of the short and long term social improvement •Determines probability in the prioritization of alternatives •Improves reliability of decision making considering the social contribution

Analyzing the uncertainty of ensemble-based gridded observations in land surface simulations and drought assessment

Science.gov (United States)

Ahmadalipour, Ali; Moradkhani, Hamid

2017-12-01

Hydrologic modeling is one of the primary tools utilized for drought monitoring and drought early warning systems. Several sources of uncertainty in hydrologic modeling have been addressed in the literature. However, few studies have assessed the uncertainty of gridded observation datasets from a drought monitoring perspective. This study provides a hydrologic modeling oriented analysis of the gridded observation data uncertainties over the Pacific Northwest (PNW) and its implications on drought assessment. We utilized a recently developed 100-member ensemble-based observed forcing data to simulate hydrologic fluxes at 1/8° spatial resolution using Variable Infiltration Capacity (VIC) model, and compared the results with a deterministic observation. Meteorological and hydrological droughts are studied at multiple timescales over the basin, and seasonal long-term trends and variations of drought extent is investigated for each case. Results reveal large uncertainty of observed datasets at monthly timescale, with systematic differences for temperature records, mainly due to different lapse rates. The uncertainty eventuates in large disparities of drought characteristics. In general, an increasing trend is found for winter drought extent across the PNW. Furthermore, a ∼3% decrease per decade is detected for snow water equivalent (SWE) over the PNW, with the region being more susceptible to SWE variations of the northern Rockies than the western Cascades. The agricultural areas of southern Idaho demonstrate decreasing trend of natural soil moisture as a result of precipitation decline, which implies higher appeal for anthropogenic water storage and irrigation systems.

An ensemble approach to assess hydrological models' contribution to uncertainties in the analysis of climate change impact on water resources

Science.gov (United States)

Velázquez, J. A.; Schmid, J.; Ricard, S.; Muerth, M. J.; Gauvin St-Denis, B.; Minville, M.; Chaumont, D.; Caya, D.; Ludwig, R.; Turcotte, R.

2012-06-01

Over the recent years, several research efforts investigated the impact of climate change on water resources for different regions of the world. The projection of future river flows is affected by different sources of uncertainty in the hydro-climatic modelling chain. One of the aims of the QBic3 project (Québec-Bavarian International Collaboration on Climate Change) is to assess the contribution to uncertainty of hydrological models by using an ensemble of hydrological models presenting a diversity of structural complexity (i.e. lumped, semi distributed and distributed models). The study investigates two humid, mid-latitude catchments with natural flow conditions; one located in Southern Québec (Canada) and one in Southern Bavaria (Germany). Daily flow is simulated with four different hydrological models, forced by outputs from regional climate models driven by a given number of GCMs' members over a reference (1971-2000) and a future (2041-2070) periods. The results show that the choice of the hydrological model does strongly affect the climate change response of selected hydrological indicators, especially those related to low flows. Indicators related to high flows seem less sensitive on the choice of the hydrological model. Therefore, the computationally less demanding models (usually simple, lumped and conceptual) give a significant level of trust for high and overall mean flows.

Operational risk assessments by supply chain professionals : process and performance

NARCIS (Netherlands)

Tazelaar, F.; Snijders, C.C.P.

2013-01-01

We consider the "process-performance paradox" in the assessment of operational risks by professionals in the field of operations and supply chain management (OSCM). The paradox states that although professionals with more expertise tend to decide in different ways, they often do not make better

Ecosilient Index to assess the greenness and resilience of the upstream automotive supply chain

DEFF Research Database (Denmark)

Azevedo, S.G.; Govindan, Kannan; Carvalho, H.

2013-01-01

This paper suggests an Ecosilient Index to assess the greenness and resilience of automotive companies and the corresponding supply chain. An integrated assessment model is proposed based on green and resilient practices. The Delphi technique is used to obtain the weights of the supply chain para...... sector; this constitutes an initial effort to close the gap between theory and practice. Future research is needed to investigate the index applicability in different industry contexts........ to the greenness of this industry is to reduce energy consumption. The significant contributions to resilience are sourcing strategies that allow switching of suppliers, flexible supply base/flexible sourcing and creating total supply chain visibility. The proposed index was developed in the context of automotive...

Assessing the Expected Value of Research Studies in Reducing Uncertainty and Improving Implementation Dynamics.

Science.gov (United States)

Grimm, Sabine E; Dixon, Simon; Stevens, John W

2017-07-01

With low implementation of cost-effective health technologies being a problem in many health systems, it is worth considering the potential effects of research on implementation at the time of health technology assessment. Meaningful and realistic implementation estimates must be of dynamic nature. To extend existing methods for assessing the value of research studies in terms of both reduction of uncertainty and improvement in implementation by considering diffusion based on expert beliefs with and without further research conditional on the strength of evidence. We use expected value of sample information and expected value of specific implementation measure concepts accounting for the effects of specific research studies on implementation and the reduction of uncertainty. Diffusion theory and elicitation of expert beliefs about the shape of diffusion curves inform implementation dynamics. We illustrate use of the resulting dynamic expected value of research in a preterm birth screening technology and results are compared with those from a static analysis. Allowing for diffusion based on expert beliefs had a significant impact on the expected value of research in the case study, suggesting that mistakes are made where static implementation levels are assumed. Incorporating the effects of research on implementation resulted in an increase in the expected value of research compared to the expected value of sample information alone. Assessing the expected value of research in reducing uncertainty and improving implementation dynamics has the potential to complement currently used analyses in health technology assessments, especially in recommendations for further research. The combination of expected value of research, diffusion theory, and elicitation described in this article is an important addition to the existing methods of health technology assessment.

Area 2: Inexpensive Monitoring and Uncertainty Assessment of CO2 Plume Migration using Injection Data

Energy Technology Data Exchange (ETDEWEB)

Srinivasan, Sanjay [Univ. of Texas, Austin, TX (United States)

2014-09-30

In-depth understanding of the long-term fate of CO₂ in the subsurface requires study and analysis of the reservoir formation, the overlaying caprock formation, and adjacent faults. Because there is significant uncertainty in predicting the location and extent of geologic heterogeneity that can impact the future migration of CO₂ in the subsurface, there is a need to develop algorithms that can reliably quantify this uncertainty in plume migration. This project is focused on the development of a model selection algorithm that refines an initial suite of subsurface models representing the prior uncertainty to create a posterior set of subsurface models that reflect injection performance consistent with that observed. Such posterior models can be used to represent uncertainty in the future migration of the CO₂ plume. Because only injection data is required, the method provides a very inexpensive method to map the migration of the plume and the associated uncertainty in migration paths. The model selection method developed as part of this project mainly consists of assessing the connectivity/dynamic characteristics of a large prior ensemble of models, grouping the models on the basis of their expected dynamic response, selecting the subgroup of models that most closely yield dynamic response closest to the observed dynamic data, and finally quantifying the uncertainty in plume migration using the selected subset of models. The main accomplishment of the project is the development of a software module within the SGEMS earth modeling software package that implements the model selection methodology. This software module was subsequently applied to analyze CO₂ plume migration in two field projects – the In Salah CO₂ Injection project in Algeria and CO₂ injection into the Utsira formation in Norway. These applications of the software revealed that the proxies developed in this project for quickly assessing the dynamic characteristics of the reservoir were

Principles of Uncertainty

CERN Document Server

Kadane, Joseph B

2011-01-01

An intuitive and mathematical introduction to subjective probability and Bayesian statistics. An accessible, comprehensive guide to the theory of Bayesian statistics, Principles of Uncertainty presents the subjective Bayesian approach, which has played a pivotal role in game theory, economics, and the recent boom in Markov Chain Monte Carlo methods. Both rigorous and friendly, the book contains: Introductory chapters examining each new concept or assumption Just-in-time mathematics -- the presentation of ideas just before they are applied Summary and exercises at the end of each chapter Discus

Assessment of errors and uncertainty patterns in GIA modeling

DEFF Research Database (Denmark)

Barletta, Valentina Roberta; Spada, G.

2012-01-01

During the last decade many efforts have been devoted to the assessment of global sea level rise and to the determination of the mass balance of continental ice sheets. In this context, the important role of glacial-isostatic adjustment (GIA) has been clearly recognized. Yet, in many cases only one......, such as time-evolving shorelines and paleo-coastlines. In this study we quantify these uncertainties and their propagation in GIA response using a Monte Carlo approach to obtain spatio-temporal patterns of GIA errors. A direct application is the error estimates in ice mass balance in Antarctica and Greenland...

LCA and external costs in comparative assessment of electricity chains. Decision support for sustainable electricity provision?

International Nuclear Information System (INIS)

Voss, A.

2002-01-01

The provision of energy and electricity plays an important role in a country's economic and environmental performance and the sustainability of its development. Sustainable development of the energy and electricity sector depends on finding ways of meeting energy service demands of the present generation that are economically viable, environmentally sound, and socially acceptable and do not jeopardize the ability of future generations to meet their own energy needs. Life Cycle Assessment (LCA) and external cost valuation are considered to offer opportunities to assist energy policy in a comprehensive comparative evaluation of electricity supply options with regard to the different dimensions of sustainable energy provision as well as in the implementation of appropriate internalization strategies. The paper addresses life cycle assessment and external cost analysis carried out for selected electricity systems of interest under German conditions. Results from a comprehensive comparative assessment of various electricity supply options with regard to their environmental impacts, health risks, raw materials requirements as well as their resulting external cost will be summarised. The use of LCA based indicators for assessing the relative sustainability of electricity systems and the use of total (internal plus external) cost assessment as measure of economic and environmental efficiency of energy systems will be discussed. Open problems related to life cycle analysis of energy chains and the assessment of environmental damage costs are critically reviewed, to illustrate how in spite of existing uncertainties the state of the art results may provide helpful energy policy decision support. The paper starts with some remarks on what the concept of sustainability in terms of energy systems means. (author)

UpWind D1. Uncertainties in wind assessment with LIDAR

Energy Technology Data Exchange (ETDEWEB)

Lindeloew-Marsden, P.

2009-01-15

In this report sources influencing wind assessments with lidars are listed and discussed. Comparisons with mast mounted cup anemometers are presented and the magnitudes of the errors from the listed error sources are estimated. Finally an attempt to define uncertainty windows for the current state of the two commercial wind sensing lidars is presented. The results in this report give important feedback on system improvements to manufacturers and an estimation of the current ability for wind farm developers which are potential users. (author)

SUPPLY CHAIN RISKS: LITERATURE REVIEW AND A NEW CATEGORIZATION

OpenAIRE

Er Kara, Merve; Oktay Fırat, Seniye Ümit

2017-01-01

Identification of risks is the first step to build a resilient and sustainable supply chain and develop proactive risk management strategies. Supply chains contain numerous risks with different forms, probabilities and impacts. Supply chain risks have a multi-dimensional nature and can result from a wide variety of sources including demand and supply variability, poor performing suppliers, price fluctuations, dynamic consumer markets, global economic uncertainty, and even unexpected events su...

Roadmap toward addressing and communicating uncertainty in LCA

DEFF Research Database (Denmark)

Laurin, Lise; Vigon, Bruce; Fantke, Peter

2017-01-01

-characterized uncertainty. The group has investigated current best LCA practices, such as refinements to the pedigree matrix used to assess LCI data quality. In parallel, in the frame of UNEP-SETAC Life Cycle Initiative flagship project on providing Harmonization and Global Guidance for Environmental Life Cycle Impact...... uncertainty is further related to input data, model selection and choices, amongst other aspects. Currently, methods exist to assess and assign uncertainty and variability on LCI data as well as LCIA characterization results. However, often uncertainty is only assessed and reported qualitatively......, is not comparable across impact categories and not consistently assessed and reported across levels of detail. Furthermore, many existing methods and models do not report uncertainty at all or limit their uncertainty assessment to a sensitivity analysis of selected input parameters, while ignoring variability...

Measures of Model Uncertainty in the Assessment of Primary Stresses in Ship Structures

DEFF Research Database (Denmark)

Östergaard, Carsten; Dogliani, Mario; Guedes Soares, Carlos

1996-01-01

The paper considers various models and methods commonly used for linear elastic stress analysis and assesses the uncertainty involved in their application to the analysis of the distribution of primary stresses in the hull of a containership example, through statistical evaluations of the results...

CEC/USDOE workshop on uncertainty analysis

International Nuclear Information System (INIS)

Elderkin, C.E.; Kelly, G.N.

1990-07-01

Any measured or assessed quantity contains uncertainty. The quantitative estimation of such uncertainty is becoming increasingly important, especially in assuring that safety requirements are met in design, regulation, and operation of nuclear installations. The CEC/USDOE Workshop on Uncertainty Analysis, held in Santa Fe, New Mexico, on November 13 through 16, 1989, was organized jointly by the Commission of European Communities (CEC's) Radiation Protection Research program, dealing with uncertainties throughout the field of consequence assessment, and DOE's Atmospheric Studies in Complex Terrain (ASCOT) program, concerned with the particular uncertainties in time and space variant transport and dispersion. The workshop brought together US and European scientists who have been developing or applying uncertainty analysis methodologies, conducted in a variety of contexts, often with incomplete knowledge of the work of others in this area. Thus, it was timely to exchange views and experience, identify limitations of approaches to uncertainty and possible improvements, and enhance the interface between developers and users of uncertainty analysis methods. Furthermore, the workshop considered the extent to which consistent, rigorous methods could be used in various applications within consequence assessment. 3 refs

Probabilistic accident consequence uncertainty analysis: Dispersion and deposition uncertainty assessment, main report

International Nuclear Information System (INIS)

Harper, F.T.; Young, M.L.; Miller, L.A.; Hora, S.C.; Lui, C.H.; Goossens, L.H.J.; Cooke, R.M.; Paesler-Sauer, J.; Helton, J.C.

1995-01-01

The development of two new probabilistic accident consequence codes, MACCS and COSYMA, was completed in 1990. These codes estimate the risks presented by nuclear installations based on postulated frequencies and magnitudes of potential accidents. In 1991, the US Nuclear Regulatory Commission (NRC) and the Commission of the European Communities (CEC) began a joint uncertainty analysis of the two codes. The ultimate objective of the joint effort was to develop credible and traceable uncertainty distributions for the input variables of the codes. Expert elicitation was identified as the best technology available for developing a library of uncertainty distributions for the selected consequence parameters. The study was formulated jointly and was limited to the current code models and to physical quantities that could be measured in experiments. Experts developed their distributions independently. To validate the distributions generated for the wet deposition input variables, samples were taken from these distributions and propagated through the wet deposition code model. Resulting distributions closely replicated the aggregated elicited wet deposition distributions. To validate the distributions generated for the dispersion code input variables, samples from the distributions and propagated through the Gaussian plume model (GPM) implemented in the MACCS and COSYMA codes. Project teams from the NRC and CEC cooperated successfully to develop and implement a unified process for the elaboration of uncertainty distributions on consequence code input parameters. Formal expert judgment elicitation proved valuable for synthesizing the best available information. Distributions on measurable atmospheric dispersion and deposition parameters were successfully elicited from experts involved in the many phenomenological areas of consequence analysis. This volume is the first of a three-volume document describing the project

A coordination theoretic model for three level supply chains using ...

Indian Academy of Sciences (India)

Typically, supply chain members are dependent on each other to manage various resources and information. The conflicting objectives and lack of coordination between supply chain members may often cause uncertainties in supply and demand. The basic elements of coordination theory like interdependency, coherency ...

Bayesian data analysis of severe fatal accident risk in the oil chain.

Science.gov (United States)

Eckle, Petrissa; Burgherr, Peter

2013-01-01

We analyze the risk of severe fatal accidents causing five or more fatalities and for nine different activities covering the entire oil chain. Included are exploration and extraction, transport by different modes, refining and final end use in power plants, heating or gas stations. The risks are quantified separately for OECD and non-OECD countries and trends are calculated. Risk is analyzed by employing a Bayesian hierarchical model yielding analytical functions for both frequency (Poisson) and severity distributions (Generalized Pareto) as well as frequency trends. This approach addresses a key problem in risk estimation-namely the scarcity of data resulting in high uncertainties in particular for the risk of extreme events, where the risk is extrapolated beyond the historically most severe accidents. Bayesian data analysis allows the pooling of information from different data sets covering, for example, the different stages of the energy chains or different modes of transportation. In addition, it also inherently delivers a measure of uncertainty. This approach provides a framework, which comprehensively covers risk throughout the oil chain, allowing the allocation of risk in sustainability assessments. It also permits the progressive addition of new data to refine the risk estimates. Frequency, severity, and trends show substantial differences between the activities, emphasizing the need for detailed risk analysis. © 2012 Paul Scherrer Institut.

On the relationship between aerosol model uncertainty and radiative forcing uncertainty.