TY - JOUR
T1 - Predictive heuristic control
T2 - Inferring risks from heterogeneous nowcast accuracy
AU - van der Werf, Job Augustijn
AU - Kapelan, Zoran
AU - Langeveld, Jeroen Gerardus
PY - 2023
Y1 - 2023
N2 - Urban Drainage Systems can cause ecological and public health issues by releasing untreated contaminated water into the environment. 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, whilst performing poorly in quantifying rainfall depths. No relation was found in the 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 predicting the end of the rainfall event, showed a significant risk of operative deterioration (performing worse than the baseline RB-RTC), linked to the relative performance of the nowcast algorithm. 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 from nowcast data.
AB - Urban Drainage Systems can cause ecological and public health issues by releasing untreated contaminated water into the environment. 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, whilst performing poorly in quantifying rainfall depths. No relation was found in the 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 predicting the end of the rainfall event, showed a significant risk of operative deterioration (performing worse than the baseline RB-RTC), linked to the relative performance of the nowcast algorithm. 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 from nowcast data.
KW - Combined sewer overflows
KW - rainfall forecast
KW - real-time control
KW - risk assessment
KW - urban drainage systems
UR - http://www.scopus.com/inward/record.url?scp=85149154085&partnerID=8YFLogxK
U2 - 10.2166/wst.2023.027
DO - 10.2166/wst.2023.027
M3 - Article
C2 - 36853777
AN - SCOPUS:85149154085
SN - 0273-1223
VL - 87
SP - 1009
EP - 1028
JO - Water science and technology : a journal of the International Association on Water Pollution Research
JF - Water science and technology : a journal of the International Association on Water Pollution Research
IS - 4
ER -