TY - JOUR
T1 - Investigation of Rainfall Forecast System Characteristics in Real-Time Optimal Operation of Urban Drainage Systems
AU - Jafari, Fatemeh
AU - Mousavi, S. Jamshid
AU - Kim, Joong Hoon
N1 - Funding Information:
The authors would like to thank Jafar Yazdi for providing the data of the case study and his recommendations.?We also thank Vahid Aghaei for his help in English checking and editing of the manuscript.
PY - 2020/3/1
Y1 - 2020/3/1
N2 - This study investigates the role of rainfall forecast system characteristics in predictive real-time optimal operation (PRTOP) of urban drainage systems (UDSs). A simulation-optimization model is proposed integrating the stormwater management rainfall-runoff simulator, the harmony search optimization algorithm, and a rainfall forecasting module. This module generates different rainfall forecast scenarios depending on the forecast (time) horizon (FH) and the forecast type (perfect or imperfect). Five adaptive PRTOP models are compared to evaluate the relationships between the FH, forecast type, and the system’s relative regulating capacity (SRRC). The models are tested in a part of UDS of Tehran, capital of Iran. Results indicate that for the studied system, perfect knowledge of future rainfall is more beneficial for a specific range of the SRRC, equal to 80–90% of its upper bound. Besides, the performance of the PRTOP model improves with increasing the FH up to a certain point, and then having no further positive effect. Finally, the PRTOP model equipped with a hypothetical forecasting model where the forecast error is a nonlinear function of the forecast lead time still performs better than both a zero-FH reactive RTOP model and short forecast horizon PRTOP models.
AB - This study investigates the role of rainfall forecast system characteristics in predictive real-time optimal operation (PRTOP) of urban drainage systems (UDSs). A simulation-optimization model is proposed integrating the stormwater management rainfall-runoff simulator, the harmony search optimization algorithm, and a rainfall forecasting module. This module generates different rainfall forecast scenarios depending on the forecast (time) horizon (FH) and the forecast type (perfect or imperfect). Five adaptive PRTOP models are compared to evaluate the relationships between the FH, forecast type, and the system’s relative regulating capacity (SRRC). The models are tested in a part of UDS of Tehran, capital of Iran. Results indicate that for the studied system, perfect knowledge of future rainfall is more beneficial for a specific range of the SRRC, equal to 80–90% of its upper bound. Besides, the performance of the PRTOP model improves with increasing the FH up to a certain point, and then having no further positive effect. Finally, the PRTOP model equipped with a hypothetical forecasting model where the forecast error is a nonlinear function of the forecast lead time still performs better than both a zero-FH reactive RTOP model and short forecast horizon PRTOP models.
KW - Optimization
KW - Predictive real-time operation
KW - Rainfall forecasts
KW - Urban drainage systems
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U2 - 10.1007/s11269-020-02528-1
DO - 10.1007/s11269-020-02528-1
M3 - Article
AN - SCOPUS:85083255810
SN - 0920-4741
VL - 34
SP - 1773
EP - 1787
JO - Water Resources Management
JF - Water Resources Management
IS - 5
ER -