| Abstract: | Statistical operations termed model-adjustment procedures (MAP?s) can be used to incorporate local data into existing regression models to improve the prediction of urban-runoff quality. Each MAP is a form of regression analysis in which the local data base is used as a calibration data set. Regression coefficients are determined from the local data base, and the resulting `adjusted? regression models can then be used to predict storm-runoff quality at unmonitored sites. The response variable in the regression analyses is the observed load or mean concentration of a constituent in storm runoff for a single storm. The set of explanatory variables used in the regression analyses is different for each MAP, but always includes the predicted value of load or mean concentration from a regional regression model. The four MAP?s examined in this study were: single-factor regression against the regional model prediction, P, (termed MAP-lF-P), regression against P,, (termed MAP-R-P), regression against P, and additional local variables (termed MAP-R-P+nV), and a weighted combination of P, and a local-regression prediction (termed MAP-W).
The procedures were tested by means of split-sample analysis, using data from three cities included in the Nationwide Urban Runoff Program: Denver, Colorado; Bellevue, Washington; and Knoxville, Tennessee. The MAP that provided the greatest predictive accuracy for the verification data set differed among the three test data bases and among model types (MAP-W for Denver and Knoxville, MAP-lF-P and MAP-R-P for Bellevue load models, and MAP-R-P+nV for Bellevue concentration models) and, in many cases, was not clearly indicated by the values of standard error of estimate for the calibration data set. A scheme to guide MAP selection, based on exploratory data analysis of the calibration data set, is presented and tested.
The MAP?s were tested for sensitivity to the size of a calibration data set. As expected, predictive accuracy of all MAP?s for the verification data set decreased as the calibration data-set size decreased, but predictive accuracy was not as sensitive for the MAP?s as it was for the local regression models. |
| Genre: | USGS Numbered Series |
| ProdID: | 19441 |
| Citation Author: | Hoos, A. B.; Sisolak, J. K. |
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| Citation Language: | ENGLISH |
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| Citation Phsyical Description: | iv, 39 p. :ill. ;28 cm. |
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| Citation Publisher: | U.S. Geological Survey ;
Books and Open-File Reports Section [distributor], |
| Citation Series: | Open-File Report |
| Citation Series Code: | OFR |
| Citation Series Number: | 93-39 |
| Citation Search Results Text: | Procedures for adjusting regional regression models of urban-runoff quality using local data; 1993; OFR; 93-39; Hoos, A. B.; Sisolak, J. K. |
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| Citation Year: | 1993 |
| Type: | citation/reference |
| Text: | Procedures for adjusting regional regression models of urban-runoff quality using local data; 1993; OFR; 93-39; Hoos, A. B.; Sisolak, J. K. |
| URL (THUMBNAIL): | http://pubs.er.usgs.gov/thumbnails/usgs_thumb.jpg |
| URL (INDEX PAGE): | http://pubs.water.usgs.gov/ofr_93-39 |
| Date Other: | Sat, 1 Jan 1994 00:00 -0600 |
| Publisher: | U.S. Geological Survey ;
Books and Open-File Reports Section [distributor], |
| Superseded by: |
http://pubs.er.usgs.gov/publication/wsp2428
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