Procedures for Adjusting Regional Regression Models of Urban-Runoff Quality Using Local Data
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, Pu, (termed MAP-lF-P), regression against Pu, (termed MAP-R-P), regression against P, and additional local variables (termed MAP-R-P+nV), and a weighted combination of Pu and a local-regression prediction (termed MAP-W).
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 1993
- Accession Number
- ADA446939
Entities
People
- Anne B. Hoos
- Joy K. Sisolak