uncertainty and sensitivity analysisclassification of risks is based on

Mathematics, TU Delft, The Netherlands J. M. van Noortwijk HKV Consultants, Lelystad, The Netherlands ABSTRACT: We discuss graphical methods which may be employed generically for uncertainty and sensitivity analysis. And then effects of a 20% decrease in project lifetime. variability in the models output (and ignore the other ones for your 1 minus 20%, multiply $80,000, which is going to be $64,000 of salvage. response surfaces). And I'm going to go into Data, What If Analysis, Data Table. Now this is sort of looking like a tornado diagram. Comparison between three alternative probabilistic approaches for the same exposure assessment. I'm going to decrease the gap width, maybe something around 60%. So if I go back up here, 58.78 is the net present value when we have 100% of all those values. Written in the scripting language Python 2.7, this tool is a collection of scripts written by researchers at the Center for Advanced Vechicular Systems . And the last thing we need to do is to change our legend. Global Sensitivity Analysis:analysis across the entire response surface. It ranges from (+1) to (-1). W. J. Marshall, J. Yang, U. Mertyurek, and M. A. Jessee, , Transactions of the AmericanNuclear Society, Majdi I. Radaideh, William A. Wieselquist, Tomasz Kozlowski, ", K. Zeng, J. Hou, K. Ivanov, and M.Jessee, . B. management module. Uncertainty and Sensitivity Analysis. Analysis of Uncertainty in Ozone Population Exposure Modeling. @article{osti_5623213, title = {Comparison of uncertainty and sensitivity analysis techniques for computer models}, author = {Iman, R L and Helton, J C}, abstractNote = {Uncertainty analysis and sensitivity analysis are important elements in the development and implementation of computer models for complex processes. Quantifying such uncertainty bounds in model outcomes would strengthen confidence in the interpretation of results and provide the extent of variations possible in changing policy decision. Uncertainty analyses are Applications of sensitivity analysis include: Risk communication: how much of my output uncertainty is In a qualitative uncertainty analysis, a description of the uncertainty in each of the major elements of the analysis is provided. That's how much we're going to request for working capital. We will use Matlab in this workshop to show the steps. 80,000. global sensitivity analysis methods that allow you to examine the Menu. A 'sensitivity analysis' of these parameters is not only critical to model validation but also serves to guide future research efforts. A. Rivas, J. Hou, G. Ilas, Preliminary Benchmark Calculations of Spent Nuclear Fuel Isotopic Compositions Using BWR Assay Data,Transactions of the American Nuclear Society, vol. Marshall, William A. Wieselquist, Assessment of Existing Transportation Packages for Use with HALEU, ORNL/TM-2020/1725, UT-Battelle, LLC, Oak Ridge National Laboratory (September 2020) [supporting files], Briana Hiscox, Benjamin Betzler, Vladimir Sobes, William J. Marshall, "Neutronic Benchmarking of Small Gas-Cooled Systems," Proceedings of PHYSOR 2020, March 2020. An official website of the United States government. However, as models become more complex by including additional physical, chemical, or biological processes, their performance can degrade because they require more input variables, leading to greater data uncertainty (EPA, 2009a). Uncertainty and sensitivity analysis are an integral part of the modeling process (Saltelli et al., 2000). The level of uncertainty should meet the criteria determined at the onset of the modeling activity. And rate of return is going to be 32.4%, which is 58.5% percent higher than the base case. That was our nominal value. Part of their work includes the application of mathematical and computer models to help assess chemical hazards and risks to human health and the environment. If annual income is increased by 20%, we are going to have $48,000 dollars per year and the rate of return is going to be increased to 26.5%, which is 29.5% percent higher than the base case. parameter values or data) on the model output. Sensitivity analysis (SA)is a method to determine which variables, parameters, or other inputs have the most influence on the model output. And rate of return is going to be 17.7%. We have two more years of project lifetime. That has a very big effect on net present value. And what this tornado plot shows us is that if we change, for example, sales, if that goes down by 20% of our baseline, then that has a huge effect on the net present value. A subset of the most influential uncertainties are then evaluated in a model which investigates the process of resolving uncertainty over time learning. The diagram to the right shows the different sources of uncertainty and variability in a cumulative risk assessment. And I'm going to change this to minus 20%. An NRC Committee (2007) recommended that models used in the regulatory process should be no more complicated than is necessary to inform regulatory decision and that it is often preferable to omit capabilities that do not substantially improve model performance. Mapping the model attributes to the problem statement, Confirming the degree of certainty needed from model outputs, Determining the amount of reliable data available or the resources available to collect more, The quality of the scientific foundations of the model, The technical competence of the model development / application team, Qualitatively evaluate the level of uncertainty of each specified uncertainty (model, data, stochastic, etc. B. T. Rearden, " Some Innovations of Dr. Mark Williams for the Practical Application of Sensitivity and Uncertainty Analysis to Reactor Analysis and Criticality Safet y," Transactions of the American Nuclear Society, 121, 1479-1483 (November 2019). The first row is the case that we have 40% decrease in salvage value. Expert elicitation may be used to characterize uncertainty and fill data gaps where traditional scientific research is not feasible or data are not yet available. 2002. W. J. Marshall, J. Yang, U. Mertyurek, and M. A. Jessee, Preliminary TSUNAMI Assessment of the Impact of Accident Tolerant Fuel Concepts on Reactor Physics Validation, Transactions of the AmericanNuclear Society, 120, 500503 (June 2019). outcomes of predictive simulations, Unless otherwise specified, content published on risk-engineering.org is Monte Carlo simulations are based on repeated sampling and are a popular way to incorporate the variance of the input factors (e.g. 2.6. Finally, the analytic framework is applied to the uncertainty and sensitivity analysis of a deterministic HIV model. Three levels of SA include: Sensitivity analysis of the van Genuchten parameter (a) for four models (HYDRUS, FECTUZ, CHAIN 2D, AND MULTIMED-DP). Abstract. Sensitivity analysis will be demonstrated and the relative importance of parameters' contributions to uncertainty in model outcomes will be determined. the cost of gasoline hybrid-electric vehicles (Gasoline-HEVcost), the cost of hydrogen fuel cell vehicles (H-FCVcost), the efficiency of gasoline hybrid electric vehicles (Gasoline-HEV efficiency). So if our annual income is increased by 40% from the base case, we are going to have $46,000 per year. Let's work on a simple example. Initial investment is decreased by 20%, then the rate of return is going to be 29.6%, which, comparing to the base case, the rate of return is going to be 44.8% higher than the base case. Now effect of 20% decrease in the initial investment. We can further modify this. And the rate of return can be calculated as 22%. Analysis results provide the importance of ten associated factors in determining the basic reproduction number R 0. Figure was adapted from EPA (2009a). Variability can be better characterized, but hard to reduce, with further study. This investing project requires $150,000 of investment at the present time and it yields the annual income of $40,000 for five years from year one to year five and the salvage value of $80,000 in the end of the year five. C (2016) Uncertainty and sensitivity analysis of West Java Water Sustainability Index - A case study on Citarum This information can also help to identify areas that may need more research to reduce the associated uncertainty. Uncertainty and Variability Sources in a cumulative risk assessment. And the green here represents the baseline values. And the last part, we are on the sensitivity analysis for the salvage value. [pdf]. The purpose of this module is to provide extended guidance on the concepts of sensitivity and uncertainty analyses - not to provide thorough instruction on the available methods or practices. First we calculate the rate of return on this cash flow for this project. The DesignBuilder Uncertainty and Sensitivity Analysis (UA/SA) toolkit enables a systematic quantification of risk and an evaluation of the most influential . This approach is considered non-intensive, in that it can be automated in some instances. We'll repeat the calculations for 20% decrease in salvage. So I'd right-click on this, format that axis. So the project with initial investment of $150,000 and annual income of $40,000 for three years and the salvage value of $80,000. Uncertainty in outcomes can orginate from multiple sources such as measurement errors in data collection, model identification, parameter uncertainty, and process stochasticity. Evaluating uncertainty in integrated environmental models: A review of concepts and tools. F. Bostelmann, S. E. Skutnik, E. D. Walker, G. Ilas, W. A. Wieselquist, "Modeling of the Molten Salt Reactor Experiment with SCALE,"Nuclear Technology,vol 208, Issue 4, Pages 603-624 (October 2021). Evaluate the sensitivity of the project ROR to 20% and 40% increase and decrease in initial investment, annual income, project life, and salvage value. Science 263(5147): 641-646. Therefore, application niche uncertainty can be minimized when the model is applied as intended. Model Life-cycle: DOE & sensitivity analysis, reliability analysis, mathematical models for structural dynamic analysis, drillstring dynamics analysis, application of natural language processing and predictive maintenance. Global Sensitivity Analysis. SCALE provides a suite of computational tools for sensitivity and uncertainty analysis to (1) identify important processes in safety analysis and design, (2) provide a quantifiable basis for neutronics validation for criticality safety and reactor physics analysis based on similarity assessment, and (3) quantify the effects of uncertainties in nuclear data and physical parameters for safety . 2001b., EPA (U.S. Environmental Protection Agency). Different techniques can be used in each of the tiers: see the tiered process for probabilistic risk assessment (WHO, 2008; EPA, 2009b); or the tiered approach outlined in EPA (2001b, 2004) described below: A schematic of a tiered approach. So if the initial investment is increased by 40%, rate of return is going to be 8.6%. We calculate the rate of return for such projects. Python programming language. When you make a data table, we have a column of different inputs that we're going to do kind of a case study on, the cell one up and one over from our values. The closure parameters introduced in transition models can compromise the accuracy of prediction results. can be used to indicate the proportionate contribution of input uncertainties. And each of these working capital values is going to be placed into cell B4 up here. Among the techniques to answer these questions are statistical design of experiments (such as fractional factorial designs) and regression analysis. Examples of criteria for evaluating the subjectivity of choices are adapted below from WHO (2008): An example of a qualitative summary of uncertainties in the Baseline Ecological Risk Assessment (EPA, 2005). Saltelli, A., K. Chan, and M. Scott, eds. In option 1, only variability is quantified (dotted blue line). Clarity, J. Yang, U. Mertyurek, M. A. Jessee, and B. T. Rearden, , F. Bostelmann, D. Wiarda, W. A. Wieselquist, and B. T. Rearden, , Proceedings of International Conference on Mathematics and Computational Methods applied to Nuclear Science and Engineering. The level of uncertainty can be the assessor's description of the degree of severity of the uncertainty. Risk Analysis 22(3): 553-578. Sensitivity analysis is the study of how the uncertainty in the output of a mathematical model or system (numerical or otherwise) can be divided and allocated to different sources of uncertainty in its inputs. Pearsons Correlation Coefficient ():Reflects the relationship between two variables. CHARLIE NUTTELMAN: This screencast is going to go over a sensitivity analysis, and we're going to generate a tornado plot. uncertainty forces decision-makers to judge how probable it is that risks will be over-estimated or under-estimated for every member of the exposed population, whereas variability forces them to cope with the certainty that different individuals will be subjected to risks both above and below any reference point one chooses. NRC (1994), Models can never fully specify the systems that they described, and therefore are always subject to uncertainties that we cannot fully specify Oreskes (2003). V. Sobes, W. J. Marshall, D. Wiarda, F. Bostelmann, A. M. Holcomb, and B. T. Rearden, ENDF/B-VIII.0 Covariance Data Development and Testing Report for Advanced Reactors. And we want to determine how sensitive the outputs of the process are to the inputs. We'll repeat the calculations for a 40% increase in annual income, which is going to be 1 plus 40%, multiply $40,000, which comes to $56,000 annual income. Geoderma 60(1-4): 15-40. And we calculate the rate of return for the new situation, for the case that we have 20% less initial investment. An example of a parametric sensitivity analysis is given on theExample subtabin this section. And I'm going to click on one of these series, Format Data Series. The magnitude of each input is increased and decreased parametrically along a range deemed realistic for real-world values. Differential analysestypically contain four steps. This module builds upon the fundamental concepts outlined in previous modules:Environmental Modeling 101andBest Modeling Practices: Model Evaluation. I multiplied that by 80%, all the way up to 120%. When risk estimates are used for decision-making, sensitivity Percent. These needs should have been identified during model development (EPA, 2009a).Examples of criteria for qualitatively evaluating the uncertainty of the knowledge base are adapted below from WHO (2008): The EPA recommends using the terms'precision'and'bias,'rather than'accuracy,'to convey the information usually associated with accuracy. Increasingly complex models have reduced model framework/theory uncertainty as more scientific understandings are incorporated into the model. Verification, Validation, and Confirmation of Numerical Models in the Earth Sciences. Expert Elicitation: I'm going to change the labels so they are low. Mubayi is also a co-director of Mathematical Theoretical Biology Institute (MTBI) and director of the B.S. We also assume all the input variables are independent and have no effect on each other. 1.1. Research interests include Error Resilient Processor design, Semi Conductor Functional Safety & integrated Vector Management. Typically, the necessary quantities are obtained through structured interviews and/or questionnaires. And the rate of return can be calculated as 23.4. Uncertainty & Sensitivity Analysis. [pdf]. F. Bostelmann, D. Wiarda, W. A. Wieselquist, and B. T. Rearden, SCALE/Sampler Sensitivity Indices, Proceedings of International Conference on Mathematics and Computational Methods applied to Nuclear Science and Engineering, pp. 2002b., EPA (US Environmental Protection Agency). A sensitivity analysis, also referred to as a what-if analysis, is a mathematical tool used in scientific and financial modeling to study how uncertainties in a model affect that model's overall uncertainty. Proceedings of ANS Best Estimate Plus Uncertainty International Conference (BEPU 2018), RealCollegio, Lucca, Italy, May 1319, 2019. This bar shows what would be the change in the rate of return of the project if initial investment changes from 40% positive to 40% negative, 40% increase to 40% decrease. [pdf], B. T. Rearden, Some Innovations of Dr. Mark Williams for the Practical Application of Sensitivity and Uncertainty Analysis to Reactor Analysis and Criticality Safety, Transactions of the American Nuclear Society, 121, 14791483 (November 2019). parameters, but (almost) identical results (metamodels or Can handle PC models with 10's to 1000's of variables; Solves intensive computing problems (e.g. The paper addresses two types of uncertainties: 1) structural (e.g., branch outages), which are modeled by contingency scenarios, and 2) perturbations of model parameters (e.g., branch impedance), which are described by sensitivity analysis. Response Surface/Plane: A theoretical multi-dimensional 'surface' that describes the response of a model to changes in input values. Mountford, . Image adapted from EPA (2002b). "the probability of a randomly chosen individual being exposed to any given level"). In these course materials, applications are presented using the NumPy, SciPy and statsmodels libraries for the

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