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OPR-PPR, a Computer Program for Assessing Data Importance to Model Predictions Using Linear Statistics

Techniques and Methods 6-E2

Prepared in cooperation with the U.S. Department of Energy
By:
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Abstract

The OPR-PPR program calculates the Observation-Prediction (OPR) and Parameter-Prediction (PPR) statistics that can be used to evaluate the relative importance of various kinds of data to simulated predictions. The data considered fall into three categories: (1) existing observations, (2) potential observations, and (3) potential information about parameters. The first two are addressed by the OPR statistic; the third is addressed by the PPR statistic. The statistics are based on linear theory and measure the leverage of the data, which depends on the location, the type, and possibly the time of the data being considered. For example, in a ground-water system the type of data might be a head measurement at a particular location and time. As a measure of leverage, the statistics do not take into account the value of the measurement. As linear measures, the OPR and PPR statistics require minimal computational effort once sensitivities have been calculated. Sensitivities need to be calculated for only one set of parameter values; commonly these are the values estimated through model calibration. OPR-PPR can calculate the OPR and PPR statistics for any mathematical model that produces the necessary OPR-PPR input files. In this report, OPR-PPR capabilities are presented in the context of using the ground-water model MODFLOW-2000 and the universal inverse program UCODE_2005. The method used to calculate the OPR and PPR statistics is based on the linear equation for prediction standard deviation. Using sensitivities and other information, OPR-PPR calculates (a) the percent increase in the prediction standard deviation that results when one or more existing observations are omitted from the calibration data set; (b) the percent decrease in the prediction standard deviation that results when one or more potential observations are added to the calibration data set; or (c) the percent decrease in the prediction standard deviation that results when potential information on one or more parameters is added.

Additional Publication Details

Publication type:
Report
Publication Subtype:
USGS Numbered Series
Title:
OPR-PPR, a Computer Program for Assessing Data Importance to Model Predictions Using Linear Statistics
Series title:
Techniques and Methods
Series number:
6-E2
Edition:
-
Year Published:
2007
Language:
ENGLISH
Publisher:
Geological Survey (U.S.)
Contributing office(s):
U.S. Geological Survey
Description:
viii, 115 p.
Larger Work Type:
Report
Larger Work Subtype:
USGS Numbered Series
Larger Work Title:
Chapter 2 of Book 6. Modeling Techniques, Section E. Model Analysis