An Approach to the Analysis of Behavior and Sensitivity in Environmental Systems

Hornberger, G. M.; Spear, Robert C.

doi:10.1007/978-3-642-82054-0_3

Cited by 223 publications

(276 citation statements)

References 2 publications

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“…To reduce the computation cost, an alternative adjoint method was developed. In the adjoint method, a Green's or kernel function matrix K(t, t) is constructed for (6), which satisfies…”

Section: Adjoint Sensitivity Analysismentioning

confidence: 99%

“…The sensitivity coefficients obtained by (10) are the same as those obtained with (6). The difference between the adjoint method and direct method is the numerical methods.…”

Section: Adjoint Sensitivity Analysismentioning

confidence: 99%

“…MPSA method (also called regionalised sensitivity analysis) was first proposed by Hornberger and Spear [6] in the field of hydrology. It is a sensitivity approach based on MonteCarlo filtering.…”

Section: Multi-parametric Sensitivity Analysis (Mpsa)mentioning

confidence: 99%

“…Sensitivity analysis has a long history and it has been widely applied in different fields such as environmental modelling study [6], economic modelling for decisions making [7], chemical kinetics [8][9][10] and biological modelling analysis [11,12]. In this review, we particularly focus on the sensitivity analysis approaches applied to systems biology models.…”

Section: Introductionmentioning

confidence: 99%

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Sensitivity analysis approaches applied to systems biology models

2011

IET Syst. Biol.

339

263

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With the rising application of systems biology, sensitivity analysis methods have been widely applied to study the biological systems, including metabolic networks, signalling pathways and genetic circuits. Sensitivity analysis can provide valuable insights about how robust the biological responses are with respect to the changes of biological parameters and which model inputs are the key factors that affect the model outputs. In addition, sensitivity analysis is valuable for guiding experimental analysis, model reduction and parameter estimation. Local and global sensitivity analysis approaches are the two types of sensitivity analysis that are commonly applied in systems biology. Local sensitivity analysis is a classic method that studies the impact of small perturbations on the model outputs. On the other hand, global sensitivity analysis approaches have been applied to understand how the model outputs are affected by large variations of the model input parameters. In this review, the author introduces the basic concepts of sensitivity analysis approaches applied to systems biology models. Moreover, the author discusses the advantages and disadvantages of different sensitivity analysis methods, how to choose a proper sensitivity analysis approach, the available sensitivity analysis tools for systems biology models and the caveats in the interpretation of sensitivity analysis results.

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“…To reduce the computation cost, an alternative adjoint method was developed. In the adjoint method, a Green's or kernel function matrix K(t, t) is constructed for (6), which satisfies…”

Section: Adjoint Sensitivity Analysismentioning

confidence: 99%

“…The sensitivity coefficients obtained by (10) are the same as those obtained with (6). The difference between the adjoint method and direct method is the numerical methods.…”

Section: Adjoint Sensitivity Analysismentioning

confidence: 99%

Section: Multi-parametric Sensitivity Analysis (Mpsa)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Sensitivity analysis approaches applied to systems biology models

2011

IET Syst. Biol.

339

263

View full text Add to dashboard Cite

show abstract

“…It deals with only one or two kinds of diagnosis, within one particular approach to the problem of model structure identification, for one of three categories of data (the intermediate category of "some adequate data", as explained in the introduction to Section 3), and applied to modestly sized, even humble models. Nevertheless, the approach is sufficiently complex that it loses the attractive simplicity of the Monte Carlo approaches adopted elsewhere for the analysis of "scarce data" situations (Horn berger and Spear, , 1981. And it is a sobering thought indeed, if one reflects upon the vast potential of telemetered, on-line water quality monitoring networks (Marsili-Libelli, 1980), that a lack of field data will not always be the critical constraint on water quality modeling.…”

Section: Design For Failure and Speculationmentioning

confidence: 99%

Uncertainty and Forecasting of Water Quality

1983

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Model Calibration and Uncertainty Estimation

Gupta

Beven

Wagener

2005

Encyclopedia of Hydrological Sciences

193

185

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All rainfall-runoff models are, by definition, simplifications of the real-world system under investigation. The model components are aggregated descriptions of real-world hydrologic processes. One consequence of this is that the model parameters often do not represent directly measurable entities, but must be estimated using measurements of the system response through a process known as model calibration. The objective of this calibration process is to obtain a model with the following characteristics: (i) the input-state-output behavior of the model is consistent with the measurements of catchment behavior, (ii) the model predictions are accurate (i.e. they have negligible bias) and precise (i.e. the prediction uncertainty is relatively small), and (iii) the model structure and behavior are consistent with current hydrologic understanding of reality. This article describes the historic development leading to current views on model calibration, and the algorithms and techniques that have been developed for estimating parameters, thereby enabling the model to mimic the behavior of the hydrologic system. Manual techniques as well as automatic algorithms are addressed. The automatic approaches range from purely random techniques, to local and global search algorithms. An overview of multiobjective and recursive algorithms is also presented. Although it would be desirable to reduce the total output prediction error to zero (i.e. the difference between observed and simulated system behavior) this is generally impossible owing to the unavoidable uncertainties inherent in any rainfall-runoff modeling procedure. These uncertainties stem mainly from the inability of calibration procedures to uniquely identify a single optimal parameter set, from measurement errors associated with the system input and output, and from model structural errors arising from the aggregation of real-world processes into a mathematical model. Some commonly used approaches to estimate these uncertainties and their impacts on the model predictions are discussed. The article ends with a brief discussion about the current status of calibration and how well we are able to represent the effects of uncertainty in the modeling process, and some potential directions. THE NATURE OF RAINFALL-RUNOFF MODELSThe hydrology of any catchment involves complex interactions driven by a number of spatially distributed and highly interrelated water, energy, and vegetation processes. Any computer-based model intended to represent the behavior of a catchment must, therefore, conceptualize this reality using relatively simple mathematical equations that involve parameters to be specified for any particular application. Two characteristics of the modeling process are relevant to our discussion. First, all rainfall-runoff (RR) models, regardless of how spatially explicit, are to some degree lumped, so that their equations and parameters describe the processes as aggregated in space and time. As a consequence, the model parameters are typically not directly measurable, and...

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An Approach to the Analysis of Behavior and Sensitivity in Environmental Systems

Cited by 223 publications

References 2 publications

Sensitivity analysis approaches applied to systems biology models

Sensitivity analysis approaches applied to systems biology models

Uncertainty and Forecasting of Water Quality

Model Calibration and Uncertainty Estimation

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