scholarly journals What do we mean by sensitivity analysis? The need for comprehensive characterization of “global” sensitivity in Earth and Environmental systems models

2015 ◽  
Vol 51 (5) ◽  
pp. 3070-3092 ◽  
Author(s):  
Saman Razavi ◽  
Hoshin V. Gupta
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity ◽  
Environmental Systems ◽  
Systems Models ◽  
Comprehensive Characterization

Parameter Sensitivity Analysis of the Extended Bouc-Wen Model of Hysteresis

2002 ◽  
Author(s):  
Haochuan Zhang ◽  
Fai Ma
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Simplified Model ◽  
Model Parameters ◽  
Hysteretic Model ◽  
Structural Damping ◽  
Parameter Sensitivity Analysis ◽  
Differential Model ◽  
Global Sensitivity

The extended Bouc-Wen differential model is one of the most widely accepted phenomenological models of hysteresis in computational mechanics. It is routinely used in the characterization of structural damping and in system identification. In this paper, the differential model of hysteresis is carefully re-examined and two significant issues are uncovered. First, it is found that the unspecified parameters of the model are not independent. One of the model parameters can be eliminated through suitable transformations in the parameter space. Second, through local and global sensitivity analysis, it is found that some parameters of the hysteretic model are rather insensitive. If these insensitive parameters are set to constant values, a greatly simplified model is obtained.

scholarly journals What do we do with model simulation crashes? Recommendations for global sensitivity analysis of earth and environmental systems models

2019 ◽  
Author(s):  
Razi Sheikholeslami ◽  
Saman Razavi ◽  
Amin Haghnegahdar
Keyword(s):  
Sensitivity Analysis ◽  
Sample Size ◽  
Response Surface ◽  
Land Surface ◽  
Global Sensitivity Analysis ◽  
Model Simulation ◽  
Surface Modelling ◽  
Global Sensitivity ◽  
Environmental Systems ◽  
Response Surface Modelling

Abstract. Complex, software-intensive, technically advanced, and computationally demanding models, presumably with ever-growing realism and fidelity, have been widely used to simulate and predict the dynamics of the Earth and environmental systems. The parameter-induced simulation crash (failure) problem is typical across most of these models, despite considerable efforts that modellers have directed at model development and implementation over the last few decades. A simulation failure mainly occurs due to the violation of the numerical stability conditions, non-robust numerical implementations, or errors in programming. However, the existing sampling-based analysis techniques such as global sensitivity analysis (GSA) methods, which require running these models under many configurations of parameter values, are ill-equipped to effectively deal with model failures. To tackle this problem, we propose a novel approach that allows users to cope with failed designs (samples) during the GSA, without knowing where they took place and without re-running the entire experiment. This approach deems model crashes as missing data and uses strategies such as median substitution, single nearest neighbour, or response surface modelling to fill in for model crashes. We test the proposed approach on a 10-paramter HBV-SASK rainfall-runoff model and a 111-parameter MESH land surface-hydrology model. Our results show that response surface modelling is a superior strategy, out of the data filling strategies tested, and can scale well to the dimensionality of the model, sample size, and the ratio of number of failures to the sample size. Further, we conduct a "failure analysis" and discuss some possible causes of the MESH model failure.

scholarly journals What should we do when a model crashes? Recommendations for global sensitivity analysis of Earth and environmental systems models

2019 ◽  
Vol 12 (10) ◽  
pp. 4275-4296 ◽  
Author(s):  
Razi Sheikholeslami ◽  
Saman Razavi ◽  
Amin Haghnegahdar
Keyword(s):  
Sensitivity Analysis ◽  
Sample Size ◽  
Response Surface ◽  
Land Surface ◽  
Nearest Neighbor ◽  
Global Sensitivity Analysis ◽  
Surface Modeling ◽  
Response Surface Modeling ◽  
Global Sensitivity ◽  
Environmental Systems

Abstract. Complex, software-intensive, technically advanced, and computationally demanding models, presumably with ever-growing realism and fidelity, have been widely used to simulate and predict the dynamics of the Earth and environmental systems. The parameter-induced simulation crash (failure) problem is typical across most of these models despite considerable efforts that modellers have directed at model development and implementation over the last few decades. A simulation failure mainly occurs due to the violation of numerical stability conditions, non-robust numerical implementations, or errors in programming. However, the existing sampling-based analysis techniques such as global sensitivity analysis (GSA) methods, which require running these models under many configurations of parameter values, are ill equipped to effectively deal with model failures. To tackle this problem, we propose a new approach that allows users to cope with failed designs (samples) when performing GSA without rerunning the entire experiment. This approach deems model crashes as missing data and uses strategies such as median substitution, single nearest-neighbor, or response surface modeling to fill in for model crashes. We test the proposed approach on a 10-parameter HBV-SASK (Hydrologiska Byråns Vattenbalansavdelning modified by the second author for educational purposes) rainfall–runoff model and a 111-parameter Modélisation Environmentale–Surface et Hydrologie (MESH) land surface–hydrology model. Our results show that response surface modeling is a superior strategy, out of the data-filling strategies tested, and can comply with the dimensionality of the model, sample size, and the ratio of the number of failures to the sample size. Further, we conduct a “failure analysis” and discuss some possible causes of the MESH model failure that can be used for future model improvement.

Sensitivity Analysis of the Extended Bouc-Wen Model of Hysteresis

2005 ◽  
Author(s):  
F. Ma ◽  
A. Imam
Keyword(s):  
Sensitivity Analysis ◽  
Random Vibration ◽  
Relative Sensitivity ◽  
Nonlinear Damping ◽  
Sensitivity Analyses ◽  
Simplified Model ◽  
Hysteretic Model ◽  
Differential Model ◽  
Global Sensitivity

The extended Bouc-Wen differential model is one of the most widely accepted phenomenological models of hysteresis in random vibration. It is routinely used in the characterization of nonlinear damping and in system identification. In this paper, the differential model of hysteresis is carefully re-examined and two significant issues are uncovered. First, it is found that the unspecified parameters of the model are functionally redundant. One of the parameters can be eliminated through suitable transformations in the parameter space. Second, local and global sensitivity analyses are conducted to assess the relative sensitivity of each model parameter. Through extensive Monte Carlo simulations, it is found that some parameters of the hysteretic model are rather insensitive. If the values of these insensitive parameters are fixed, a greatly simplified model is obtained.

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