A Global Parallel Model Based Design of Experiments Method to Minimize Model Output Uncertainty

2011 ◽  
Vol 74 (3) ◽  
pp. 688-716 ◽  
Author(s):  
Jason N. Bazil ◽  
Gregory T. Buzzard ◽  
Ann E. Rundell
Keyword(s):  
Design Of Experiments ◽  
Model Output ◽  
Parallel Model ◽  
Model Based ◽  
Output Uncertainty

Preliminary uncertainty analysis – a prerequisite for assessing the predictive uncertainty of hydrologic models

1996 ◽  
Vol 33 (2) ◽  
pp. 79-90 ◽  
Author(s):  
Jian Hua Lei ◽  
Wolfgang Schilling
Keyword(s):  
Uncertainty Analysis ◽  
Model Parameters ◽  
Observation Data ◽  
Calibration Data ◽  
Model Output ◽  
Predictive Uncertainty ◽  
Runoff Volume ◽  
Output Uncertainty ◽  
Sensitive Parameters ◽  
Calculated Model

Physically-based urban rainfall-runoff models are mostly applied without parameter calibration. Given some preliminary estimates of the uncertainty of the model parameters the associated model output uncertainty can be calculated. Monte-Carlo simulation followed by multi-linear regression is used for this analysis. The calculated model output uncertainty can be compared to the uncertainty estimated by comparing model output and observed data. Based on this comparison systematic or spurious errors can be detected in the observation data, the validity of the model structure can be confirmed, and the most sensitive parameters can be identified. If the calculated model output uncertainty is unacceptably large the most sensitive parameters should be calibrated to reduce the uncertainty. Observation data for which systematic and/or spurious errors have been detected should be discarded from the calibration data. This procedure is referred to as preliminary uncertainty analysis; it is illustrated with an example. The HYSTEM program is applied to predict the runoff volume from an experimental catchment with a total area of 68 ha and an impervious area of 20 ha. Based on the preliminary uncertainty analysis, for 7 of 10 events the measured runoff volume is within the calculated uncertainty range, i.e. less than or equal to the calculated model predictive uncertainty. The remaining 3 events include most likely systematic or spurious errors in the observation data (either in the rainfall or the runoff measurements). These events are then discarded from further analysis. After calibrating the model the predictive uncertainty of the model is estimated.

scholarly journals Constrained model-based design of experiments for the identification of approximated models

2018 ◽  
Vol 51 (15) ◽  
pp. 515-520 ◽  
Author(s):  
Marco Quaglio ◽  
Eric S. Fraga ◽  
Federico Galvanin
Keyword(s):  
Design Of Experiments ◽  
Model Based ◽  
Constrained Model

Implementation and Application of Model-Based Design of Experiments in a Flowsheet Simulator

2019 ◽  
Vol 65 (3) ◽  
pp. 1135-1145 ◽  
Author(s):  
Norbert Asprion ◽  
Roger Böttcher ◽  
Jonas Mairhofer ◽  
Maria Yliruka ◽  
Johannes Höller ◽  
...  
Keyword(s):  
Design Of Experiments ◽  
Model Based

A framework for model-based design of experiments in the presence of continuous measurement systems

2010 ◽  
Vol 43 (5) ◽  
pp. 571-576 ◽  
Author(s):  
Federico Galvanin ◽  
Massimiliano Barolo ◽  
Fabrizio Bezzo
Keyword(s):  
Design Of Experiments ◽  
Continuous Measurement ◽  
Measurement Systems ◽  
Model Based

Multiobjective Framework for Model-based Design of Experiments to Improve Parameter Precision and Minimize Parameter Correlation

2013 ◽  
Vol 52 (24) ◽  
pp. 8289-8304 ◽  
Author(s):  
Vaibhav Maheshwari ◽  
Gade Pandu Rangaiah ◽  
Lakshminarayanan Samavedham
Keyword(s):  
Design Of Experiments ◽  
Parameter Correlation ◽  
Model Based ◽  
Parameter Precision
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