Probabilistic Forecast for Real-Time Control of Rainwater Pollutant Loads in Urban Environments

Advanced wastewater management systems are necessary to effectively direct severely contaminated initial rainwater runoff to the treatment facility only when pollutant concentrations are elevated during the initial flush event, thereby reducing the risk of water pollution caused by urban drainage systems. This necessitates the implementation of intelligent decision-making systems, forecasting, and monitoring. However, conventional “deterministic” forecasts are inadequate for making informed decisions in the presence of uncertainty regarding future values, despite the fact that a variety of modeling techniques have been employed to predict total suspended solids at specific locations. The literature contains a number of “probabilistic” forecasting approaches that take into account uncertainty. Among them, this paper proposes the Model Conditional Processor (MCP), which is well-known in hydrological, hydraulic, and climatological fields, to forecast the predictive probability density of total suspended solids based on one or more deterministic predictions. This is intended to address the issue. The decision to divert the first flush is subsequently guided by the predictive density and probabilistic thresholds. The effective implementation of the MCP approach is demonstrated in a real case study that is part of the USGS’s extensive and long-term stormwater monitoring initiative, based on observations of a real stormwater drainage system. The results obtained confirm that probabilistic approaches are suitable instruments for enhancing decision-making.