scholarly journals Characterizing Prediction Uncertainty in Agricultural Modeling via a Coupled Statistical–Physical Framework

Modelling ◽  
2021 ◽  
Vol 2 (4) ◽  
pp. 753-775
Author(s):  
John C. Chrispell ◽  
Eleanor W. Jenkins ◽  
Kathleen R. Kavanagh ◽  
Matthew D. Parno
Keyword(s):  
Sensitivity Analysis ◽  
Crop Yield ◽  
Global Sensitivity Analysis ◽  
Climate Adaptation ◽  
Historical Data ◽  
Process Models ◽  
Global Sensitivity ◽  
Prediction Uncertainty ◽  
Agricultural Yields ◽  
The Impact

Multiple factors, many of them environmental, coalesce to inform agricultural decisions. Farm planning is often done months in advance. These decisions have to be made with the information available at the time, including current trends, historical data, or predictions of what future weather patterns may be. The effort described in this work is geared towards a flexible mathematical and software framework for simulating the impact of meteorological variability on future crop yield. Our framework is data driven and can easily be applied to any location with suitable historical observations. This will enable site-specific studies that are needed for rigorous risk assessments and climate adaptation planning. The framework combines a physics-based model of crop yield with stochastic process models for meteorological inputs. Combined with techniques from uncertainty quantification, global sensitivity analysis, and machine learning, this hybrid statistical–physical framework allows studying the potential impacts of meteorological uncertainty on future agricultural yields and identify the environmental variables that contribute the most to prediction uncertainty. To highlight the utility of our general approach, we studied the predicted yields of multiple crops in multiple scenarios constructed from historical data. Using global sensitivity analysis, we then identified the key environmental factors contributing to uncertainty in these scenarios’ predictions.

Nonintrusive Global Sensitivity Analysis for Linear Systems With Process Noise

2019 ◽  
Vol 14 (2) ◽  
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.

Efficient and Accurate Global Sensitivity Analysis for Reservoir Simulations By Use of the Probabilistic Collocation Method

SPE Journal ◽  
2013 ◽  
Vol 19 (04) ◽  
pp. 621-635 ◽  
Author(s):  
Cheng Dai ◽  
Heng Li ◽  
Dongxiao Zhang
Keyword(s):  
Sensitivity Analysis ◽  
Collocation Method ◽  
History Matching ◽  
Global Sensitivity Analysis ◽  
Computational Cost ◽  
Global Sensitivity ◽  
Reservoir Simulations ◽  
Probabilistic Collocation Method ◽  
The Impact ◽  
Probabilistic Collocation

Summary Reservoir simulations involve a large number of formation and fluid parameters, many of which are subject to uncertainties owing to the combination of spatial heterogeneity and insufficient measurements. Accurately quantifying the impact of varying parameters on simulation models can reveal the importance of the parameters, which helps in designing field-characterization strategies and determining parameterization for history matching. Compared with the commonly used local sensitivity analysis (SA), global SA considers the whole variation range of the parameters and can thus provide more-complete information. However, the traditional global sensitivity analysis that is derived from Monte Carlo simulation (MCS) is computationally too demanding for reservoir simulations. In this study, we propose an alternative approach that is both accurate and efficient. In the proposed approach, the model outputs such as pressure and reservoir production quantities are expressed by polynomial chaos expansions (PCEs). The probabilistic collocation method is used to determine the coefficients of the polynomial expansions by solving outputs at different sets of collocation points by means of the original partial-differential equations. Then, a proxy is constructed with such coefficients. Accurate statistical sensitivity indices of the uncertainty parameters can be obtained by running the proxy. We validate the approach with 2D examples by comparing with the MCS-based global SA. It is found that with only a small fraction of the computational cost required by the MCS approach, the new approach gives accurate global sensitivity for each parameter. The proposed approach is also demonstrated on a large-scale 3D black-oil model, for which the MCS-based global SA is found to be computationally infeasible. It is found that the developed approach possesses the following key advantages: It requires a much smaller number of reservoir simulations for accurate global SA; it is nonintrusive and can be implemented with existing codes or simulators; and it can accommodate arbitrary distributions of parameters encountered in realistic geological situations.

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

2021 ◽  
Author(s):  
Emilie Rouzies ◽  
Claire Lauvernet ◽  
Bruno Sudret ◽  
Arthur Vidard
Keyword(s):  
Water Quality ◽  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Transfer Model ◽  
Valuable Insight ◽  
Catchment Scale ◽  
Sobol Indices ◽  
Global Sensitivity ◽  
Sensitivity Indices ◽  
The Impact

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.

scholarly journals Global sensitivity-based modelling approach to identify suitable Eucalyptus traits for adaptation to climate variability and change

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Elvis Felipe Elli ◽  
Neil Huth ◽  
Paulo Cesar Sentelhas ◽  
Rafaela Lorenzato Carneiro ◽  
Clayton Alcarde Alvares
Keyword(s):  
Sensitivity Analysis ◽  
Climate Variability ◽  
Global Sensitivity Analysis ◽  
Greenhouse Gas Emission ◽  
Growth And Yield ◽  
Superior Performance ◽  
Climate Variability And Change ◽  
Genetic Traits ◽  
Global Sensitivity ◽  
The Impact

Abstract Eucalyptus-breeding efforts have been made to identify clones of superior performance for growth and yield and how they will interact with global climate changes. This study performs a global sensitivity analysis for assessing the impact of genetic traits on Eucalyptus yield across contrasting environments in Brazil under present and future climate scenarios. The APSIM Next Generation Eucalyptus model was used to perform the simulations of stemwood biomass (t ha−1) for 7-year rotations across 23 locations in Brazil. Projections for the period from 2020 to 2049 using three global circulation models under intermediate (RCP4.5) and high (RCP8.5) greenhouse gas emission scenarios were performed. The Morris sensitivity method was used to perform a global sensitivity analysis to identify the influence of plant traits on stemwood biomass. Traits for radiation use efficiency, leaf partitioning, canopy light capture and fine root partitioning were the most important, impacting the Eucalyptus yield substantially in all environments under the present climate. Some of the traits targeted now by breeders for current climate will remain important under future climates. However, breeding should place a greater emphasis on photosynthetic temperature response for Eucalyptus in some regions. Global sensitivity analysis was found to be a powerful tool for identifying suitable Eucalyptus traits for adaptation to climate variability and change. This approach can improve breeding strategies by better understanding the gene × environment interactions for forest productivity.

scholarly journals Moment-based metrics for global sensitivity analysis of hydrological systems

2017 ◽  
Vol 21 (12) ◽  
pp. 6219-6234 ◽  
Author(s):  
Aronne Dell'Oca ◽  
Monica Riva ◽  
Alberto Guadagnini
Keyword(s):  
Sensitivity Analysis ◽  
Surrogate Model ◽  
Global Sensitivity Analysis ◽  
Computational Cost ◽  
Original System ◽  
Model Complexity ◽  
Model Output ◽  
Global Sensitivity ◽  
Reduction Techniques ◽  
The Impact

Abstract. We propose new metrics to assist global sensitivity analysis, GSA, of hydrological and Earth systems. Our approach allows assessing the impact of uncertain parameters on main features of the probability density function, pdf, of a target model output, y. These include the expected value of y, the spread around the mean and the degree of symmetry and tailedness of the pdf of y. Since reliable assessment of higher-order statistical moments can be computationally demanding, we couple our GSA approach with a surrogate model, approximating the full model response at a reduced computational cost. Here, we consider the generalized polynomial chaos expansion (gPCE), other model reduction techniques being fully compatible with our theoretical framework. We demonstrate our approach through three test cases, including an analytical benchmark, a simplified scenario mimicking pumping in a coastal aquifer and a laboratory-scale conservative transport experiment. Our results allow ascertaining which parameters can impact some moments of the model output pdf while being uninfluential to others. We also investigate the error associated with the evaluation of our sensitivity metrics by replacing the original system model through a gPCE. Our results indicate that the construction of a surrogate model with increasing level of accuracy might be required depending on the statistical moment considered in the GSA. The approach is fully compatible with (and can assist the development of) analysis techniques employed in the context of reduction of model complexity, model calibration, design of experiment, uncertainty quantification and risk assessment.

Analytical Variance-Based Global Sensitivity Analysis in Simulation-Based Design Under Uncertainty

2004 ◽  
Author(s):  
Wei Chen ◽  
Ruichen Jin ◽  
Agus Sudjianto
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Simulation Models ◽  
Design Under Uncertainty ◽  
Global Sensitivity ◽  
Functional Relationships ◽  
Simulation Based Design ◽  
Simulation Based ◽  
Input Variables ◽  
The Impact

The importance of sensitivity analysis in engineering design cannot be over-emphasized. In design under uncertainty, sensitivity analysis is performed with respect to the probabilistic characteristics. Global sensitivity analysis (GSA), in particular, is used to study the impact of variations in input variables on the variation of a model output. One of the most challenging issues for GSA is the intensive computational demand for assessing the impact of probabilistic variations. Existing variance-based GSA methods are developed for general functional relationships but require a large number of samples. In this work, we develop an efficient and accurate approach to GSA that employs analytic formulations derived from metamodels of engineering simulation models. We examine the types of GSA needed for design under uncertainty and derive generalized analytical formulations of GSA based on a variety of metamodels commonly used in engineering applications. The benefits of our proposed techniques are demonstrated and verified through both illustrative mathematical examples and the robust design for improving vehicle handling performance.

Global sensitivity analysis of crop yield and transpiration from the FAO-AquaCrop model for dryland environments

2021 ◽  
Vol 269 ◽  
pp. 108182
Author(s):  
Yang Lu ◽  
Tendai P. Chibarabada ◽  
Matthew F. McCabe ◽  
Gabriëlle J.M. De Lannoy ◽  
Justin Sheffield
Keyword(s):  
Sensitivity Analysis ◽  
Crop Yield ◽  
Global Sensitivity Analysis ◽  
Global Sensitivity ◽  
Aquacrop Model

scholarly journals Considerations and Caveats when Applying Global Sensitivity Analysis Methods to Physiologically Based Pharmacokinetic Models

2020 ◽  
Vol 22 (5) ◽  
Author(s):  
Dan Liu ◽  
Linzhong Li ◽  
Amin Rostami-Hodjegan ◽  
Frederic Y. Bois ◽  
Masoud Jamei
Keyword(s):  
Sensitivity Analysis ◽  
Plasma Concentration ◽  
Global Sensitivity Analysis ◽  
Model Parameters ◽  
Model Parameter ◽  
Global Sensitivity ◽  
Physiologically Based ◽  
The Impact ◽  
Influential Parameters

Abstract Three global sensitivity analysis (GSA) methods (Morris, Sobol and extended Sobol) are applied to a minimal physiologically based PK (mPBPK) model using three model drugs given orally, namely quinidine, alprazolam, and midazolam. We investigated how correlations among input parameters affect the determination of the key parameters influencing pharmacokinetic (PK) properties of general interest, i.e., the maximal plasma concentration (Cmax) time at which Cmax is reached (Tmax), and area under plasma concentration (AUC). The influential parameters determined by the Morris and Sobol methods (suitable for independent model parameters) were compared to those determined by the extended Sobol method (which considers model parameter correlations). For the three drugs investigated, the Morris method was as informative as the Sobol method. The extended Sobol method identified different sets of influential parameters to Morris and Sobol. These methods overestimated the influence of volume of distribution at steady state (Vss) on AUC24h for quinidine and alprazolam. They also underestimated the effect of volume of liver (Vliver) for all three drugs, the impact of enzyme intrinsic clearance of CYP2C9 and CYP2E1 for quinidine, and that of UGT1A4 abundance for midazolam. Our investigation showed that the interpretation of GSA results is not straightforward. Dismissing existing model parameter correlations, GSA methods such as Morris and Sobol can lead to biased determination of the key parameters for the selected outputs of interest. Decisions regarding parameters’ influence (or otherwise) should be made in light of available knowledge including the model assumptions, GSA method limitations, and inter-correlations between model parameters, particularly in complex models.

Application of 0D model of blood flow to study vessel abnormalities in the human systemic circulation: An in-silico study

2018 ◽  
Vol 11 (08) ◽  
pp. 1850106 ◽  
Author(s):  
R. Gul ◽  
A. Shahzad ◽  
M. Zubair
Keyword(s):  
Sensitivity Analysis ◽  
Blood Flow ◽  
Global Sensitivity Analysis ◽  
Carotid Bifurcation ◽  
Clinical Decision ◽  
Systemic Circulation ◽  
Wave Pattern ◽  
Global Sensitivity ◽  
The Impact ◽  
Different Levels

In this paper, a multi-compartment 0D model of the blood flow is considered to study the vessel abnormalities (stenoses and aneurysms) in the human systemic circulation (SC). In the complete SC, different levels of stenosis and aneurysms are artificially created by decreasing and increasing the vessel diameters respectively and their effects on pressure and flow are studied using sensitivity analysis (SA). Normalized local sensitivity analysis (LSA) is used to study the impact of stenosis and aneurysms on pressure and flow wave pattern. Furthermore, global sensitivity analysis (GSA), Sobol’s method is used to quantify the overall influence of stenoses and aneurysms in the complete SC. The results of global sensitivity analysis revealed that the impact of both stenoses and aneurysms is strong within the individual structures (arm, legs, carotid bifurcation, aorta), while, aortic stenoses and aneurysms have effect on almost all downstream nodes. Moreover, the study could be useful for medical doctors, teachers and students to observe the hemodynamical changes in the SC with respect to vessel abnormalities, which could further help in making any clinical decision for patients having different levels of vessel abnormalities in any part or structure of the SC.

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