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Titolo:
Practical identifiability analysis of large environmental simulation models
Autore:
Brun, R; Reichert, P; Kunsch, HR;
Indirizzi:
Swiss Fed Inst Environm Sci & Technol, Dubendorf, Switzerland Swiss Fed Inst Environm Sci & Technol Dubendorf Switzerland Switzerland ETH Zentrum, ETH Zurich, Seminar Stat, CH-8092 Zurich, Switzerland ETH Zentrum Zurich Switzerland CH-8092 Stat, CH-8092 Zurich, Switzerland
Titolo Testata:
WATER RESOURCES RESEARCH
fascicolo: 4, volume: 37, anno: 2001,
pagine: 1015 - 1030
SICI:
0043-1397(200104)37:4<1015:PIAOLE>2.0.ZU;2-M
Fonte:
ISI
Lingua:
ENG
Soggetto:
SENSITIVITY ANALYSIS; PARAMETER UNCERTAINTY; SYSTEMS; CALIBRATION; UNIQUENESS; AQUASIM;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Agriculture,Biology & Environmental Sciences
Engineering, Computing & Technology
Citazioni:
44
Recensione:
Indirizzi per estratti:
Indirizzo: Brun, R Swiss Fed Inst Environm Sci & Technol, Dubendorf, Switzerland Swiss Fed Inst Environm Sci & Technol Dubendorf Switzerland and
Citazione:
R. Brun et al., "Practical identifiability analysis of large environmental simulation models", WATER RES R, 37(4), 2001, pp. 1015-1030

Abstract

Large environmental simulation models are usually overparameterized with respect to given sets of observations. This results in poorly identifiable or nonidentifiable model parameters. For small models, plots of sensitivity functions have proven to be useful for the analysis of parameter identifiability. For models with many parameters, however, near-linear dependence of sensitivity functions can no longer be assessed graphically. In this paper a systematic approach for tackling the parameter identifiability problem oflarge models based on local sensitivity analysis is presented. The calculation of two identifiability measures that are easy to handle and interpret is suggested. The first accounts for the sensitivity of model results to single parameters, and the second accounts for the degree of near-linear dependence of sensitivity functions of parameter subsets. It is shown how thesemeasures provide identifiability diagnosis for parameter subsets, how theyare able to guide the selection of identifiable parameter subsets for parameter estimation, and how they facilitate the interpretation of the correlation matrix of the parameter estimate with respect to parameter identifiability. In addition, we show how potential bias of the parameter estimates, due to a priori fixing of some of the parameters, can be analyzed. Finally, two case studies are presented in order to illustrate the suggested approach.

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Documento generato il 23/01/21 alle ore 02:56:19