A review of techniques for parameter sensitivity analysis of environmental models

Mathematical models are utilized to approximate various highly complex engineering, physical, environmental, social, and economic phenomena. Model parameters exerting the most influence on model results are identified through a ‘sensitivity analysis’. A comprehensive review is presented of more than a dozen sensitivity analysis methods. This review is intended for those not intimately familiar with statistics or the techniques utilized for sensitivity analysis of computer models. The most fundamental of sensitivity techniques utilizes partial differentiation whereas the simplest approach requires varying parameter values one-at-a-time. Correlation analysis is used to determine relationships between independent and dependent variables. Regression analysis provides the most comprehensive sensitivity measure and is commonly utilized to build response surfaces that approximate complex models.

[1]  R. Bellman,et al.  On structural identifiability , 1970 .

[2]  W. J. Conover,et al.  Practical Nonparametric Statistics , 1972 .

[3]  C. Fortuin,et al.  Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. I Theory , 1973 .

[4]  K. Shuler,et al.  Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. II Applications , 1973 .

[5]  S. Morisawa,et al.  On the selection of a ground disposal site by sensitivity analysis. , 1974, Health physics.

[6]  K. Shuler,et al.  Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. III. Analysis of the approximations , 1975 .

[7]  R. Schainker,et al.  On the statistical sensitivity analysis of models for chemical kinetics , 1975 .

[8]  Robert J. Gelinas,et al.  Sensitivity analysis of ordinary differential equation systems—A direct method , 1976 .

[9]  Claudio Cobelli,et al.  Controllability, Observability and Structural Identifiability of Multi Input and Multi Output Biological Compartmental Systems , 1976, IEEE Transactions on Biomedical Engineering.

[10]  N. D. Cox,et al.  Comparison of two uncertainty analysis methods , 1977 .

[11]  E. M. Oblow.  Sensitivity theory for general nonlinear algebraic equations with constraints , 1978 .

[12]  K. Shuler,et al.  Nonlinear sensitivity analysis of multiparameter model systems , 1977 .

[13]  Jon C. Helton,et al.  Risk methodology for geologic disposal of radioactive waste: sensitivity analysis techniques , 1978 .

[14]  J. S. Hunter,et al.  Statistics for experimenters : an introduction to design, data analysis, and model building , 1979 .

[15]  T. J. Krieger,et al.  Statistical determination of effective variables in sensitivity analysis , 1978 .

[16]  Ali H. Dogru,et al.  Sensitivity analysis of partial differential equations with application to reaction and diffusion processes , 1979 .

[17]  R. Iman,et al.  The Use of the Rank Transform in Regression , 1979 .

[18]  Robert V. O'Neill,et al.  Analysis of parameter error in a nonlinear model , 1980 .

[20]  C. Cobelli,et al.  Parameter and structural identifiability concepts and ambiguities: a critical review and analysis. , 1980, The American journal of physiology.

[21]  Robert V. O'Neill,et al.  Application of error analysis to a Marsh Hydrology Model , 1980 .

[22]  Ronald L. Iman,et al.  Risk methodology for geologic disposal of radioactive waste: small sample sensitivity analysis techniques for computer models, with an application to risk assessment , 1980 .

[23]  E. Oblow,et al.  Sensitivity Theory for General Systems of Nonlinear Equations , 1980 .

[24]  A. M. Dunker,et al.  Efficient calculation of sensitivity coefficients for complex atmospheric models , 1981 .

[25]  Jon C. Helton,et al.  An Approach to Sensitivity Analysis of Computer Models: Part II - Ranking of Input Variables, Response Surface Validation, Distribution Effect and Technique Synopsis , 1981 .

[26]  Jon C. Helton,et al.  Sensitivity analysis of the asymptotic behavior of a model for the environmental movement of radionuclides , 1985 .

[27]  Metin Demiralp,et al.  Chemical kinetic functional sensitivity analysis: Elementary sensitivities , 1981 .

[28]  J. Summers,et al.  A sensitivity analysis of an ecosystem model of estuarine carbon flow , 1981 .

[29]  James E. Campbell,et al.  An Approach to Sensitivity Analysis of Computer Models: Part I—Introduction, Input Variable Selection and Preliminary Variable Assessment , 1981 .

[30]  Robert V. O'Neill,et al.  A comparison of sensitivity analysis and error analysis based on a stream ecosystem model , 1981 .

[31]  R. L. Iman,et al.  Sensitivity-analysis techniques: self-teaching curriculum , 1982 .

[32]  Masato Koda,et al.  Sensitivity analysis of the atmospheric diffusion equation , 1982 .

[33]  J C Helton,et al.  Sensitivity analysis of a model for the environmental movement of radionuclides. , 1982, Health physics.

[34]  Dan G. Cacuci,et al.  Sensitivity Analysis of a Radiative-Convective Model by the Adjoint Method , 1982 .

[35]  C. V. Parks,et al.  Adjoint Sensitivity Analysis of Extremum-Type Responses in Reactor Safety , 1983 .

[36]  K. Rose,et al.  A Simulation Comparison and Evaluation of Parameter Sensitivity Methods Applicable to Large Models , 1983 .

[37]  Kenneth A. Rose,et al.  Latin hypercube analysis of parameter sensitivity in a large model of outdoor recreation demand , 1984 .

[38]  Ronald L. Iman,et al.  A FORTRAN-77 PROGRAM AND USER'S GUIDE FOR THE GENERATION OF LATIN HYPERCUBE AND RANDOM SAMPLES FOR USE WITH COMPUTER MODELS , 1984 .

[39]  G. Stephanopoulos,et al.  Development and global sensitivity analysis of a closed ecosystem model , 1985 .

[40]  J. C. Helton,et al.  A COMPARISON OF UNCERTAINTY AND SENSITIVITY ANALYSIS TECHNIQUES FOR COMPUTER MODELS , 1985 .

[41]  F. O. Hoffman,et al.  An Examination of Response-Surface Methodologies for Uncertainty Analysis in Assessment Models , 1985 .

[42]  Jon C. Helton,et al.  Uncertainty and Sensitivity Analysis of a Model for Multicomponent Aerosol Dynamics , 1986 .

[43]  B. A. Worley,et al.  Waste package performance assessment code with automated sensitivity-calculation capability , 1986 .

[44]  M. Stein.  Large sample properties of simulations using latin hypercube sampling , 1987 .

[45]  Ronald L. Iman,et al.  A Matrix-Based Approach to Uncertainty and Sensitivity Analysis for Fault Trees1 , 1987 .

[46]  Whicker Fw,et al.  Pathway: a dynamic food-chain model to predict radionuclide ingestion after fallout deposition. , 1987 .

[47]  Byung Ho Lee,et al.  Uncertainty and Sensitivity Analyses in Evaluating Risk of High Level Waste Repository , 1988 .

[48]  J. C. Helton,et al.  An Investigation of Uncertainty and Sensitivity Analysis Techniques for Computer Models , 1988 .

[49]  F W Whicker,et al.  Estimation of radionuclide ingestion: the "PATHWAY" food-chain model. , 1990, Health physics.

[50]  Ronald L. Iman,et al.  The Repeatability of Uncertainty and Sensitivity Analyses for Complex Probabilistic Risk Assessments , 1991 .

[51]  D. M. Hamby,et al.  Relative sensitivities of existing and novel model parameters in atmospheric tritium dose estimates , 1991 .

[52]  Jon C. Helton,et al.  Sensitivity analysis techniques and results for performance assessment at the Waste Isolation Pilot Plant , 1991 .

[53]  C. Yu,et al.  Sensitivity analysis of the RESRAD, a dose assessment code , 1991 .

[54]  Jack P. C. Kleijnen,et al.  Techniques for sensitivity analysis of simulation models: A case study of the CO2 greenhouse effect , 1992, Simul..

[55]  D. Hamby.  A probabilistic estimation of atmospheric tritium dose. , 1993, Health physics.

[56]  J. W. Garner,et al.  Uncertainty and sensitivity analysis results obtained in a preliminary performance assessment for the Waste Isolation Pilot Plant , 1993 .

[57]  D. M. Hamby.  A numerical comparison of sensitivity analysis techniques , 1993 .

[58]  D. Hamby.  A comparison of sensitivity analysis techniques. , 1995, Health physics.