How to perform global sensitivity analysis of a catchment-scale, distributed pesticide transfer model? Application to the PESHMELBA model

Abstract. Pesticide transfers in agricultural catchments are responsible for diffuse but major risks to water quality. Spatialized pesticide transfer models are useful tools to assess the impact of the structure of the landscape on water quality. Before considering using these tools in operational contexts, quantifying their uncertainties is a preliminary necessary step. In this study, we explored how global sensitivity analysis can be applied to the recent PESHMELBA pesticide transfer model to quantify uncertainties on transfer simulations. We set up a virtual catchment based on a real one and we compared different approaches for sensitivity analysis that could handle the specificities of the model: high number of input parameters, limited size of sample due to computational cost and spatialized output. We compared Sobol' indices obtained from Polynomial Chaos Expansion, HSIC dependence measures and feature importance measures obtained from Random Forest surrogate model. Results showed the consistency of the different methods and they highlighted the relevance of Sobol' indices to capture interactions between parameters. Sensitivity indices were first computed for each landscape element (site sensitivity indices). Second, we proposed to aggregate them at the hillslope and the catchment scale in order to get a summary of the model sensitivity and a valuable insight into the model hydrodynamical behaviour. The methodology proposed in this paper may be extended to other modular and distributed hydrological models as there has been a growing interest in these methods in recent years.

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Nonintrusive Global Sensitivity Analysis for Linear Systems With Process Noise

Journal of Computational and Nonlinear Dynamics ◽

10.1115/1.4041622 ◽

2019 ◽

Vol 14 (2) ◽

Cited By ~ 1

Author(s):

Souransu Nandi ◽

Tarunraj Singh

Keyword(s):

Sensitivity Analysis ◽

Linear Systems ◽

Global Sensitivity Analysis ◽

Benchmark Problems ◽

Model Parameters ◽

Parameter Uncertainties ◽

Sobol Indices ◽

Global Sensitivity ◽

Time Invariant ◽

The Impact

The focus of this paper is on the global sensitivity analysis (GSA) of linear systems with time-invariant model parameter uncertainties and driven by stochastic inputs. The Sobol' indices of the evolving mean and variance estimates of states are used to assess the impact of the time-invariant uncertain model parameters and the statistics of the stochastic input on the uncertainty of the output. Numerical results on two benchmark problems help illustrate that it is conceivable that parameters, which are not so significant in contributing to the uncertainty of the mean, can be extremely significant in contributing to the uncertainty of the variances. The paper uses a polynomial chaos (PC) approach to synthesize a surrogate probabilistic model of the stochastic system after using Lagrange interpolation polynomials (LIPs) as PC bases. The Sobol' indices are then directly evaluated from the PC coefficients. Although this concept is not new, a novel interpretation of stochastic collocation-based PC and intrusive PC is presented where they are shown to represent identical probabilistic models when the system under consideration is linear. This result now permits treating linear models as black boxes to develop intrusive PC surrogates.

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Probabilistic Analysis of a Metamorphic Mechanism Based on a Global Sensitivity Analysis: A Preliminary Study

Volume 5A: 41st Mechanisms and Robotics Conference ◽

10.1115/detc2017-67322 ◽

2017 ◽

Author(s):

Sarah C. Baxter ◽

Philip A. Voglewede

Keyword(s):

Sensitivity Analysis ◽

Probabilistic Analysis ◽

Global Sensitivity Analysis ◽

Kinematic Model ◽

Mathematical Structure ◽

Motion Path ◽

Model Parameters ◽

Global Sensitivity ◽

Sensitivity Indices ◽

The Impact

Mathematical modeling is an important part of the engineering design cycle. Most models require application specific input parameters that are established by calculation or experiment. The accuracy of model predictions depends on underlying model assumptions as well as how uncertainty in knowledge of the parameters is transmitted through the mathematical structure of the model. Knowledge about the relative impact of individual parameters can help establish priorities in developing/choosing specific parameters and provide insight into a range of parameters that produce ‘equally good’ designs. In this work Global Sensitivity Analysis (GSA) is examined as a technique that can contribute to this insight by developing Sensitivity Indices, a measure of the relative importance, for each parameter. The approach is illustrated on a kinematic model of a metamorphic 4-bar mechanism. The model parameters are the lengths of the four links. The results of this probabilistic analysis highlight the synergy that must exist between all four link lengths to create a design that can follow the desired motion path. The impact of individual link lengths, however, rises and falls depending on where the mechanism is along its motion path.

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Global Sensitivity Analysis for Field Response Based on the Manifold of Feature Covariance Matrix

10.1115/detc2021-69086 ◽

2021 ◽

Author(s):

Zhouzhou Song ◽

Zhao Liu ◽

Can Xu ◽

Ping Zhu

Keyword(s):

Sensitivity Analysis ◽

Covariance Matrix ◽

Computational Models ◽

Global Sensitivity Analysis ◽

Feature Selection Method ◽

Global Sensitivity ◽

Sensitivity Indices ◽

Field Response ◽

Input Variables ◽

The Impact

Abstract In real-world applications, it is commonplace that the computational models have field responses, i.e., the temporal or spatial fields. It has become a critical task to develop global sensitivity analysis (GSA) methods to measure the effect of each input variable on the full-field. In this paper, a new sensitivity analysis method based on the manifold of feature covariance matrix (FCM) is developed for quantifying the impact of input variables on the field response. The method firstly performs feature extraction on the field response to obtain a low-dimensional FCM. An adaptive feature selection method is proposed to avoid the FCM from singularity. Thereby, the field response is represented by a FCM, which lies on a symmetric positive-definite matrix manifold. Then, the GSA technique based on the Cramér-von Mises distance for output valued on the Riemannian manifold is introduced for estimating the sensitivity indices for field response. An example of a temporal field and an example of a 2-D displacement field are introduced to demonstrate the applicability of the proposed method in estimating global sensitivity indices for field solution. Results show that the proposed method can distinguish the important input variables correctly and can yield robust index values. Besides, the proposed method can be implemented for GSA for field responses of different dimensionalities.

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Variance-based global sensitivity analysis and beyond in life cycle assessment: an application to geothermal heating networks

The International Journal of Life Cycle Assessment ◽

10.1007/s11367-021-01921-1 ◽

2021 ◽

Author(s):

Marc Jaxa-Rozen ◽

Astu Sam Pratiwi ◽

Evelina Trutnevyte

Keyword(s):

Sensitivity Analysis ◽

Life Cycle Assessment ◽

Environmental Impacts ◽

Spectral Clustering ◽

Global Sensitivity Analysis ◽

Sobol Indices ◽

Global Sensitivity ◽

Geothermal Heating ◽

Trade Offs ◽

Heating Networks

Abstract Purpose Global sensitivity analysis increasingly replaces manual sensitivity analysis in life cycle assessment (LCA). Variance-based global sensitivity analysis identifies influential uncertain model input parameters by estimating so-called Sobol indices that represent each parameter’s contribution to the variance in model output. However, this technique can potentially be unreliable when analyzing non-normal model outputs, and it does not inform analysts about specific values of the model input or output that may be decision-relevant. We demonstrate three emerging methods that build on variance-based global sensitivity analysis and that can provide new insights on uncertainty in typical LCA applications that present non-normal output distributions, trade-offs between environmental impacts, and interactions between model inputs. Methods To identify influential model inputs, trade-offs, and decision-relevant interactions, we implement techniques for distribution-based global sensitivity analysis (PAWN technique), spectral clustering, and scenario discovery (patient rule induction method: PRIM). We choose these techniques because they are applicable with generic Monte Carlo sampling and common LCA software. We compare these techniques with variance-based Sobol indices, using a previously published LCA case study of geothermal heating networks. We assess eight environmental impacts under uncertainty for three design alternatives, spanning different geothermal production temperatures and heating network configurations. Results In the application case on geothermal heating networks, PAWN distribution-based sensitivity indices generally identify influential model parameters consistently with Sobol indices. However, some discrepancies highlight the potentially misleading interpretation of Sobol indices on the non-normal distributions obtained in our analysis, where variance may not meaningfully describe uncertainty. Spectral clustering highlights groups of model results that present different trade-offs between environmental impacts. Compared to second-order Sobol interaction indices, PRIM then provides more precise information regarding the combinations of input values associated with these different groups of calculated impacts. PAWN indices, spectral clustering, and PRIM have a computational advantage because they yield stable results at relatively small sample sizes (n = 12,000), unlike Sobol indices (n = 100,000 for second-order indices). Conclusions We recommend adding these new techniques to global sensitivity analysis in LCA as they give more precise as well as additional insights on uncertainty regardless of the distribution of the model outputs. PAWN distribution-based global sensitivity analysis provides a computationally efficient assessment of input sensitivities as compared to variance-based global sensitivity analysis. The combination of clustering and scenario discovery enables analysts to precisely identify combinations of input parameters or uncertainties associated with different outcomes of environmental impacts.

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Effective Global Sensitivity Analysis for High-Dimensional Hydrologic and Water Quality Models

Journal of Hydrologic Engineering ◽

10.1061/(asce)he.1943-5584.0001726 ◽

2019 ◽

Vol 24 (1) ◽

pp. 04018057 ◽

Cited By ~ 4

Author(s):

Yogesh Khare ◽

Christopher J. Martinez ◽

Rafael Muñoz-Carpena ◽

Adelbert “Del” Bottcher ◽

Andrew James

Keyword(s):

Water Quality ◽

Sensitivity Analysis ◽

Global Sensitivity Analysis ◽

High Dimensional ◽

Quality Models ◽

Global Sensitivity ◽

Water Quality Models

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A Global Sensitivity Analysis Framework for Hybrid Simulation with Stochastic Substructures

Frontiers in Built Environment ◽

10.3389/fbuil.2021.778716 ◽

2021 ◽

Vol 7 ◽

Author(s):

Nikolaos Tsokanas ◽

Xujia Zhu ◽

Giuseppe Abbiati ◽

Stefano Marelli ◽

Bruno Sudret ◽

...

Keyword(s):

Sensitivity Analysis ◽

Hybrid Model ◽

Surrogate Model ◽

Global Sensitivity Analysis ◽

Hybrid Simulation ◽

Hybrid Models ◽

Analysis Framework ◽

Global Sensitivity ◽

Sensitivity Indices ◽

Realistic Situation

Hybrid simulation is an experimental method used to investigate the dynamic response of a reference prototype structure by decomposing it to physically-tested and numerically-simulated substructures. The latter substructures interact with each other in a real-time feedback loop and their coupling forms the hybrid model. In this study, we extend our previous work on metamodel-based sensitivity analysis of deterministic hybrid models to the practically more relevant case of stochastic hybrid models. The aim is to cover a more realistic situation where the physical substructure response is not deterministic, as nominally identical specimens are, in practice, never actually identical. A generalized lambda surrogate model recently developed by some of the authors is proposed to surrogate the hybrid model response, and Sobol’ sensitivity indices are computed for substructure quantity of interest response quantiles. Normally, several repetitions of every single sample of the inputs parameters would be required to replicate the response of a stochastic hybrid model. In this regard, a great advantage of the proposed framework is that the generalized lambda surrogate model does not require repeated evaluations of the same sample. The effectiveness of the proposed hybrid simulation global sensitivity analysis framework is demonstrated using an experiment.

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A Global Sensitivity Analysis Framework for Hybrid Simulation

10.31224/osf.io/wnqgb ◽

2021 ◽

Author(s):

Giuseppe Abbiati ◽

Stefano Marelli ◽

Nikolaos Tsokanas ◽

Bruno Sudret ◽

Bozidar Stojadinovic

Keyword(s):

Sensitivity Analysis ◽

Hybrid Model ◽

Polynomial Chaos ◽

Global Sensitivity Analysis ◽

Hybrid Simulation ◽

Polynomial Chaos Expansion ◽

Chaos Expansion ◽

Analysis Framework ◽

Global Sensitivity ◽

Sensitivity Indices

Hybrid Simulation is a dynamic response simulation paradigm that merges physical experiments and computational models into a hybrid model. In earthquake engineering, it is used to investigate the response of structures to earthquake excitation. In the context of response to extreme loads, the structure, its boundary conditions, damping, and the ground motion excitation itself are all subjected to large parameter variability. However, in current seismic response testing practice, Hybrid Simulation campaigns rely on a few prototype structures with fixed parameters subjected to one or two ground motions of different intensity. While this approach effectively reveals structural weaknesses, it does not reveal the sensitivity of structure's response. This thus far missing information could support the planning of further experiments as well as drive modeling choices in subsequent analysis and evaluation phases of the structural design process.This paper describes a Global Sensitivity Analysis framework for Hybrid Simulation. This framework, based on Sobol' sensitivity indices, is used to quantify the sensitivity of the response of a structure tested using the Hybrid Simulation approach due to the variability of the prototype structure and the excitation parameters. Polynomial Chaos Expansion is used to surrogate the hybrid model response. Thereafter, Sobol' sensitivity indices are obtained as a by-product of polynomial coefficients, entailing a reduced number of Hybrid Simulations compared to a crude Monte Carlo approach. An experimental verification example highlights the excellent performance of Polynomial Chaos Expansion surrogates in terms of stable estimates of Sobol' sensitivity indices in the presence of noise caused by random experimental errors.

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Comprehensive global sensitivity analysis of a repository model using different types of transformations and metamodeling techniques

10.5194/egusphere-egu21-6131 ◽

2021 ◽

Author(s):

Sabine M. Spiessl ◽

Dirk-A. Becker ◽

Sergei Kucherenko

Keyword(s):

Sensitivity Analysis ◽

Global Sensitivity Analysis ◽

Higher Order ◽

Model Parameters ◽

High Dimensional Model Representation ◽

Model Output ◽

Global Sensitivity ◽

Sparse Polynomial ◽

Sensitivity Indices ◽

Highly Nonlinear

Due to their highly nonlinear, non-monotonic or even discontinuous behavior, sensitivity analysis of final repository models can be a demanding task. Most of the output of repository models is typically distributed over several orders of magnitude and highly skewed. Many values of a probabilistic investigation are very low or even zero. Although this is desirable in view of repository safety it can distort the evidence of sensitivity analysis. For the safety assessment of the system, the highest values of outputs are mainly essential and if those are only a few, their dependence on specific parameters may appear insignificant. By applying a transformation, different model output values are differently weighed, according to their magnitude, in sensitivity analysis. Probabilistic methods of higher-order sensitivity analysis, applied on appropriately transformed model output values, provide a possibility for more robust identification of relevant parameters and their interactions. This type of sensitivity analysis is typically done by decomposing the total unconditional variance of the model output into partial variances corresponding to different terms in the ANOVA decomposition. From this, sensitivity indices of increasing order can be computed. The key indices used most often are the first-order index (SI1) and the total-order index (SIT). SI1 refers to the individual impact of one parameter on the model and SIT represents the total effect of one parameter on the output in interactions with all other parameters. The second-order sensitivity indices (SI2) describe the interactions between two model parameters.In this work global sensitivity analysis has been performed with three different kinds of output transformations (log, shifted and Box-Cox transformation) and two metamodeling approaches, namely the Random-Sampling High Dimensional Model Representation (RS-HDMR) [1] and the Bayesian Sparse PCE (BSPCE) [2] approaches. Both approaches are implemented in the SobolGSA software [3, 4] which was used in this work. We analyzed the time-dependent output with two approaches for sensitivity analysis, i.e., the pointwise and generalized approaches. With the pointwise approach, the output at each time step is analyzed independently. The generalized approach considers averaged output contributions at all previous time steps in the analysis of the current step. Obtained results indicate that robustness can be improved by using appropriate transformations and choice of coefficients for the transformation and the metamodel.[1] M. Zuniga, S. Kucherenko, N. Shah (2013). Metamodelling with independent and dependent inputs. Computer Physics Communications, 184 (6): 1570-1580.[2] Q. Shao, A. Younes, M. Fahs, T.A. Mara (2017). Bayesian sparse polynomial chaos expansion for global sensitivity analysis. Computer Methods in Applied Mechanics and Engineering, 318: 474-496.[3] S. M. Spiessl, S. Kucherenko, D.-A. Becker, O. Zaccheus (2018). Higher-order sensitivity analysis of a final repository model with discontinuous behaviour. Reliability Engineering and System Safety, doi: https://doi.org/10.1016/j.ress.2018.12.004.[4] SobolGSA software (2021). User manual https://www.imperial.ac.uk/process-systems-engineering/research/free-software/sobolgsa-software/.

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