#Undo on pcswmm software
2009 Granato 2014).Īlthough hydrologic/hydraulic software has become very powerful, the simulation is challenging due to lack of instrumentation, or when there is limited watershed and sewer system information available. In addition, GIs improve water quality, decrease greenhouse gas emission and the energy footprint, reduce the risk of uncontrolled stormwater runoff, etc. GIs can be described as a network of natural and semi-natural areas, features, and green spaces designed to redirect surface runoff into the groundwater system, or to be used for stormwater retention ( Hunt et al. GIs mimic the natural flow regime through a decentralized design to control stormwater runoff at the source rather than at a centralized location within the watershed ( Dietz 2007 Damodaram et al. Green infrastructures (GIs), as opposed to grey infrastructures, which are designed to achieve a rapid capture and discharge of both stormwater and wastewater, are identified as an alternative nature-based and cost-effective solution for improving the sustainability of urban development ( Pakzad & Osmond 2016 Eckart et al. When overflow occurs, it changes the local and down-gradient hydrologic environment, results in a greater volume of stormwater runoff and flusher peaks, and causes localized flooding ( Heasom et al. However, during storm events, as the CSS reaches capacity, the excess volume of stormwater overflows, which is also referred to as combined sewer overflow (CSO) ( Semadeni-Davies et al. During mild, wet-weather events, the system would convey sanitary and stormwater flow to the treatment facility. During dry weather conditions, the sanitary flow would be conveyed to the wastewater treatment facility. Within most historic cities, sewer systems were designed to convey sanitary flow and stormwater flow within the same network as combined sewer systems (CSS). Extensive urbanization and frequent extreme wet-weather events are considered one of the leading reasons for this phenomenon. Inundation and water quality impairment due to stormwater overflow compromises the quality of life in many urban communities ( Strassler et al. 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. 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. 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. The developed MLRMs showed that wet-weather-related CSS flow was mitigated post implementation of the stormwater GIs. 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). 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.