Performance assessment of stormwater GI practices using artificial neural networks

Shanshan Li; Hamidreza Kazemi; Thomas D Rockaway

doi:10.1016/j.scitotenv.2018.10.155

Performance assessment of stormwater GI practices using artificial neural networks

Sci Total Environ. 2019 Feb 15;651(Pt 2):2811-2819. doi: 10.1016/j.scitotenv.2018.10.155. Epub 2018 Oct 12.

Authors

Shanshan Li¹, Hamidreza Kazemi², Thomas D Rockaway³

Affiliations

¹ School of Environmental and Planning, Liaocheng University, Liaocheng, Shandong 252000, China; Center for Infrastructure Research, Department of Civil and Environmental Engineering, University of Louisville, Louisville, KY 40292, United States. Electronic address: lishanshan@lcu.edu.cn.
² Center for Infrastructure Research, Department of Civil and Environmental Engineering, University of Louisville, Louisville, KY 40292, United States. Electronic address: h.kazemi@louisville.edu.
³ Center for Infrastructure Research, Department of Civil and Environmental Engineering, University of Louisville, Louisville, KY 40292, United States. Electronic address: tom.rockaway@louisville.edu.

PMID: 30463134
DOI: 10.1016/j.scitotenv.2018.10.155

Abstract

This study evaluates the performance of a suite of stormwater green infrastructure (GI) practices at the Belknap Campus, University of Louisville. In lack of instrumentation within individual GIs, and detailed drainage and sewer information, data mining procedures and artificial neural networks (ANN) were used. Two separate Back Propagation Neural Network Models (BPNNMs) were developed to estimate the reductions of flow volume and peak flow rates within the combined sewer system. The results from developed BPNNMs showed that following the construction of stormwater GIs at the Belknap campus, downstream wet-weather related flow decreased. The developed BPNNMs showed that the flow volume reduction and the peak flow attenuation rates had averages of approximately 33% and 61% per storm event, respectively. The flow reduction rates generally were lower for larger storms. Similarly, the peak flow rates decreased by increase of maximum intensity values per storm. However, further analysis indicated that even for large storm events, with long durations, the GIs had a positive impact on mitigation of combined sewer flows. Additionally, using rainfall data and downstream sewer flow in conjunction with artificial neural network modeling, was determined to be an effective technique for evaluating the combined hydrological performance of a suite of stormwater GIs.

Keywords: Artificial neural networks; Green infrastructure; Peak flow; Runoff flow; Stormwater.