Water 2013, 5(1), 13-28; doi:10.3390/w5010013
Article

A Data Driven Approach to Bioretention Cell Performance: Prediction and Design

1
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 and 3
Received: 24 November 2012; in revised form: 13 December 2012 / Accepted: 28 December 2012 / Published: 8 January 2013
(This article belongs to the Special Issue Sustainable Urban Water Management)
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Abstract: Bioretention 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.
Keywords: bioretention; multiple linear regression; urban runoff; hydrology; low impact development; design
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

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.

AMA Style

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.


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