Related Experiment Video
Updated: Oct 9, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
Statistical modelling of hydrological performance in a suite of green infrastructure practices
Shanshan Li1, Hamidreza Kazemi2, Thomas D Rockaway2
1School of Geography and Environment, Liaocheng University, Liaocheng, Shandong 252059, China E-mail: lishanshan@lcu.edu.cn; Center for Infrastructure Research, Department of Civil and Environmental Engineering, University of Louisville, Louisville, KY 40292, USA.
Abstract:
Statistical modelling procedures (feature selection in conjunction with multiple linear regressions) were applied to determine the performance of a suite of stormwater green infrastructures (GIs) installed at the Belknap Campus of the University of Louisville. Two separate multiple linear regression models (MLRMs) were developed and calibrated to estimate the reductions of the flow regime parameters (flow volume and peak flow rates) within the down-gradient combined sewer system (CSS). The developed MLRMs showed that wet-weather-related CSS flow was mitigated post implementation of the stormwater GIs. At the down-gradient combined sewer flow-monitoring site, the average reduction rates of flow volume and the peak flow were estimated to be 22 and 63% per rainfall event, respectively. Unlike the black-box nature of most machine-learning techniques, the MLRM has the advantage of showing the unique statistical relationship between the rainfall features and the investigated CSS flow parameters. The results from this study indicate that proper statistic modelling can be applied effectively to evaluate the hydrological performance of stormwater management practices when lacking instrumentation and having limited drainage or sewer information.
Related Concept Videos
Typical Model Studies
Design Example: Analyzing Capacity Contours for Flood Risk Assessment
Design Example: Creating a Hydraulic Model of a Dam Spillway
Design Example: Design of an Irrigation Channel
Modeling and Similitude
Gradually Varying Flow

