Sensitivity analysis for decision-making using the MORE method—A Pareto approach

Management Option Rank Equivalence (MORE) – A new method of sensitivity analysis for decision-making

Performance assessment and uncertainty prediction of a daily time-step HBV-Light rainfall-runoff model for the Upper Benue River Basin, Northern Cameroon

PurposeThe objectives of this paper are the application of sensitivity analysis (SA) methods in atmospheric dispersion modeling to the emission dispersion model (EDM) to study the prediction of atmospheric dispersion of NO2 generated by an industrial fire, whose results are useful for fire safety applications. The EDM is used to predict the level concentration of nitrogen dioxide (NO2) emitted by an industrial fire in a plant located in an industrial region site in Algeria.Design/methodology/approachThe SA was defined for the following input parameters: wind speed, NO2 emission rate and viscosity and diffusivity coefficients by simulating the air quality impacts of fire on an industrial area. Two SA methods are used: a local SA by using a one at a time technique and a global SA, for which correlation analysis was conducted on the EDM using the standardized regression coefficient.FindingsThe study demonstrates that, under ordinary weather conditions and for the fields near to the fire, the NO2 initial concentration has the most influence on the predicted NO2 levels than any other model input. Whereas, for the far field, the initial concentration and the wind speed have the most impact on the NO2 concentration estimation.Originality/valueThe study shows that an effective decision-making process should not be only based on the mean values, but it should, in particular, consider the upper bound plume concentration.

ABSTRACTHydrological models demand large numbers of input parameters, which are to be optimally identified for better simulation of various hydrological processes. Identifying the most relevant parameters and their values using efficient sensitivity analysis methods helps to better understand model performance. In this study, the physically-based distributed model SHETRAN is used for hydrological simulation on the Netravathi River Basin in south India and the most important parameters are identified using the Morris screening method. Further, the influence of a particular model parameter on streamflow is quantified using local sensitivity analysis and optimal parameters are obtained for calibration of the SHETRAN model. The results demonstrate the capability of two-stage sensitivity analysis, combining qualitative and quantitative methods in the initial screening-out of insignificant model parameters, identifying parameter interactions and quantifying the contribution of each model parameter to the streamflow. The results of the sensitivity analysis simplified the calibration procedure of SHETRAN for the study area.

This paper is concerned with the shape sensitivity analysis of a class of boundary control, constrained, optimal control problems for parabolic systems. The notion of Euler and Lagrange derivatives of a boundary optimal control in the direction of a vector field is introduced. The derivatives are obtained in the form of optimal solutions of auxiliary optimal control problems. The method of sensitivity analysis used in this paper is based on related results on the differential stability of metric projections in Hilbert space onto a convex, closed subset, combined with the material derivative method of shape sensitivity analysis. Parabolic initial-boundary value problems with Dirichlet and Neumann boundary conditions are considered in this paper.

Urolithiasis complicating pregnancy presents a challenge for urology and obstetric teams. The management options of ureteral stenting, percutaneous nephrostomy and ureteroscopy vary significantly with regard to efficacy, complications, impact on quality of life and costs. This analytic model compares these factors to determine an optimal strategy per gestational age at presentation. A decision analytic model was built that compared stenting, percutaneous nephrostomy and ureteroscopy. Outcomes included treatment failure, need for re-treatment and complications stratified by severity. Probabilities and utilities were derived from the literature and costs were derived from institutional charge data. The time horizon was 1 year with disutilities limited to gestation length or recovery time for treatment or complication. Multivariate sensitivity analyses and Monte Carlo analysis were performed to evaluate model robustness. In a simulated cohort of 1,000 pregnant women with urolithiasis ureteroscopy would yield 960 quality adjusted life years, compared to 870 with stenting and 880 with percutaneous nephrostomy. Treatment via stenting or percutaneous nephrostomy would fail more than twice as often as ureteroscopy and more than half of patients would require subsequent definitive management. In this cohort ureteroscopy would save $38.8 million compared to stenting and $17.6 million compared to percutaneous nephrostomy. Given its lower costs and improved outcomes, ureteroscopy was the dominant strategy across all gestational ages and remained dominant in all sensitivity analyses and Monte Carlo simulations. Regardless of gestational age, ureteroscopy was a cost-effective strategy to manage urolithiasis compared to stenting or percutaneous nephrostomy. This result was robust against substantial changes in model parameters.

Local and global sensitivity analysis (SA) methods were compared to demonstrate how the choice of SA method leads to differences in the quantification of the relative importance of model parameters in driving canopy reflectance variability. The global SA method used was the Extended Fourier Amplitude Sensitivity Test (EFAST), while a series of basecase simulations were defined for the local SA, and model parameters were perturbed sequentially. Canopy reflectance was simulated using the ProGOSAIL model. Some pronounced differences were found between the two methods in terms of the magnitude of the importance, the rank importance, and the wavelengths at which model parameters are most important in explaining reflectance variability.

Application of sensitivity analysis to the optimization of petroleum drilling operations

Sensitivity of muscle force response of a two-state cross-bridge model to variations in model parameters.

Sensitivity analysis of the penicillin fermentation process model based on LHS-EPRCC

The field of research of this dissertation concerns the bioengineering of exercise, in particular the relationship between biomechanical and metabolic knowledge. This relationship can allow to evaluate exercise in many different circumstances: optimizing athlete performance, understanding and helping compensation in prosthetic patients and prescribing exercise with high caloric consumption and minimal joint loading to obese subjects. Furthermore, it can have technical application in fitness and rehabilitation machine design, predicting energy consumption and joint loads for the subjects who will use the machine. The aim of this dissertation was to further understand how mechanical work and metabolic energy cost are related during movement using interpretative models. Musculoskeletal models, when including muscle energy expenditure description, can be useful to address this issue, allowing to evaluate human movement in terms of both mechanical and metabolic energy expenditure. A whole body muscle-skeletal model that could describe both biomechanical and metabolic aspects during movement was identified in literature and then was applied and validated using an EMG-driven approach. The advantage of using EMG driven approach was to avoid the use of arbitrary defined optimization functions to solve the indeterminate problem of muscle activations. A sensitivity analysis was conducted in order to know how much changes in model parameters could affect model outputs: the results showed that changing parameters in between physiological ranges did not influence model outputs largely. In order to evaluate its predicting capacity, the musculoskeletal model was applied to experimental data: first the model was applied in a simple exercise (unilateral leg press exercise) and then in a more complete exercise (elliptical exercise). In these studies, energy consumption predicted by the model resulted to be close to energy consumption estimated by indirect calorimetry for different intensity levels at low frequencies of movement. The use of muscle skeletal models for predicting energy consumption resulted to be promising and the use of EMG driven approach permitted to avoid the introduction of optimization functions. Even though many aspects of this approach have still to be investigated and these results are preliminary, the conclusions of this dissertation suggest that musculoskeletal modelling can be a useful tool for addressing issues about efficiency of movement in healthy and pathologic subjects.

Design sensitivity analysis of structures deals with the calculation of the response derivatives with respect to the design variables. These derivatives, called the sensitivity coefficients, are used in the solution of various problems. In design optimization, the sensitivity coefficients are often required to select a search direction. These coefficients are used also in generating approximations for the response of a modified system. In addition, the sensitivities are required for assessing the effects of uncertainties in the structural properties on the system response. Calculation of the sensitivities involves much computational effort, particularly in large structural systems with many design variables. As a result, there has been much interest in efficient procedures for calculating the sensitivity coefficients. Developments in methods for sensitivity analysis are discussed in many studies (e.g. Haug et. al. 1986; Haftka and Adelman, 1989; Haftka and Gurdal, 1993; van Keulen et. al. in press). Methods of sensitivity analysis for discretized systems can be divided into the following classes: a. Finite-difference methods, which are easy to implement but might involve numerous repeated analyses and high computational cost, particularly in problems with many design or response variables. In addition, finite-difference approximations might have accuracy problems. The efficiency can be improved by using fast reanalysis techniques. b. Analytical methods, which provide exact solutions but might not be easy to implement in some problems such as shape optimization. c. "Semi-analytical" methods, which are based on a compromise between finite-difference methods and analytical methods. These methods use finite-difference evaluation of the right-hand-side vector. They are easy to implement but might provide inaccurate results. In general, the following factors are considered in choosing a suitable sensitivity analysis method for a specific application: the accuracy of the calculations, the computational effort involved and the ease-of-implementation. The implementation effort is weighted against the performance of the algorithms as reflected in their computational efficiency and accuracy. The quality of the results and efficiency of the calculations are usually two conflicting factors. That is, higher accuracy is often achieved at the expense of more computational effort. Dynamic sensitivity analysis has been demonstrated by several authors. Using the mode superposition approach and assuming harmonic loading, the response sensitivities were evaluated by direct differentiation of the equations of motion in the generalized coordinates (Kramer and Grierson, 1989). In cases of earthquake loading the ground acceleration is usually given in discrete time steps, thus the loading is not given analytically. In several studies (Kim and Choi, 2000; van Keulen et. al, in press) the unconditionally stable implicit numerical equation was directly derived. It was found that the analysis equations and the sensitivity equations have the same left-hand side expression. Thus, it was possible to use the available factorized coefficient matrix. A numerical procedure was applied for calculation of the sensitivity of the response.

Statistical and experimental methods in sensitivity analysis

Assessing the sensitivity of land-atmosphere coupling strength to boundary and surface layer parameters in the WRF model over Amazon

It is essential to control accurately the critical variables in patients whose normal control systems have been faced with trouble. Vital parameters were different for different people; also with respect to similar changes in the parameters, the responses were different. This issue led to changes in the model parameters. It should be noted that even a small change in some of the model parameters can lead to death of patients. Therefore, the purpose of this study was to design a controller which was robust with respect to model parameter changes and disturbances entered to the system; this controller must be able to control their blood sugar levels in a suitable settling time. One of the issues discussed in this paper is type-2 fuzzy method, which is to regulate blood glucose levels. The use of type-2 fuzzy systems allows us to model the effects of uncertainty in the systems which are based on special rules. It also gives us an opportunity to minimize the uncertainty effects, but unfortunately, because of the complexity in use and comprehension of type-2 fuzzy sets than type-1, they were less used. These complexities are due to its three-dimensional nature and the direct dependence of its relations to the development principles; this is how its computational complexity is made.