A Data Driven Approach to Bioretention Cell Performance: Prediction and Design
AbstractBioretention cells are an urban stormwater management technology used to address both water quality and quantity concerns. A lack of region-specific design guidelines has limited the widespread implementation of bioretention cells, particularly in cold climates. In this paper, experimental data are used to construct a multiple linear regression model to predict hydrological performance of bioretention cells. Nine different observed parameters are considered as candidates for regressors, of which inlet runoff volume and duration, and initial soil moisture were chosen. These three variables are used to construct six different regression models, which are tested against the observations. Statistical analysis showed that the amount of runoff captured by a bioretention cell can be successfully predicted by the inlet runoff volume and event duration. Historical data is then used to calculate runoff volume for a given duration, in different catchment types. This data is used in the regression model to predict bioretention cell performance. The results are then used to create a design tool which can assist in estimating bioretention cell size to meet different performance goals in southern Alberta. Examples on the functionality of the design tool are provided. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Khan, U.T.; Valeo, C.; Chu, A.; He, J. A Data Driven Approach to Bioretention Cell Performance: Prediction and Design. Water 2013, 5, 13-28.
Khan UT, Valeo C, Chu A, He J. A Data Driven Approach to Bioretention Cell Performance: Prediction and Design. Water. 2013; 5(1):13-28.Chicago/Turabian Style
Khan, Usman T.; Valeo, Caterina; Chu, Angus; He, Jianxun. 2013. "A Data Driven Approach to Bioretention Cell Performance: Prediction and Design." Water 5, no. 1: 13-28.