Predictive Heuristic Control: Inferring Risks from Heterogeneous Nowcast Accuracy.

Urban drainage systems (UDS) can cause ecological and public health issues by releasing untreated contaminated water into the environ-ment. Real-time control (RTC), augmented with rainfall nowcast, can effectively reduce these pollution loads. This research aims to identify key dynamics in the nowcast accuracies and relate those to the performance of nowcast-informed rule-based (RB)-RTC procedures. The developed procedures are tested in the case study of Rotterdam, the Netherlands. Using perfect nowcast data, all developed procedures showed a reduction in combined sewer overflow volumes of up to 14.6%. Considering real nowcast data, it showed a strong ability to predict if no more rain was expected while performing poorly in quantifying rainfall depths. No relation was found between nowcast accuracy and the consistency of the predicted rainfall using a moving horizon. Using the real nowcast data, all procedures, with the exception of the one related to the end of the event, showed a significant risk of operative deterioration. These losses were directly linked to the risks related to nowcast-informed RB-RTC. Understanding the strengths of a nowcast algorithm can ensure the reliability of the RB-RTC procedure and can negate the need for detailed modelling studies by inferring risks direct from nowcast data.