Abstract
To reduce flood risk in urban regions, it is important to optimize the performance of operational elements such as gates and pumps. This paper compares the performances of two approaches of multi-period and single-period simulation-optimization that are used to derive real-time control policies for operating urban drainage systems. The EPA storm water management model (SWMM), converting real-time rainfall data to surface runoff at network control points, i.e. pump stations, is linked to the particle swarm optimization (PSO) algorithm, evaluating the system operation performance measure (objective function) for different sets of control policies. A prototype network in a portion of the Seoul urban drainage system is used to investigate the efficiency of the proposed approaches. Results justify the high efficiency of multi-period optimization, leading to 32 and 29% average reductions in peak water level violations from a pre-defined permissible threshold at target points and the number of pump switches, respectively, in comparison with the online single-period optimization. The myopic policies derived by single-period optimization are not reliable, and in some cases, they even perform worse than ad-hoc policies applied by system operators based on their past experiences.
This is a preview of subscription content, access via your institution.








References
Bagis A, Karaboga D (2004) Artificial neural networks and fuzzy logic based control of spillway gates of dams. Hydrol Process 18(13):2485–2501
Beeneken T, Erbe V, Messmer A, Reder C, Rohlfing R, Scheer M, Weyand M (2013) Real-time control (RTC) of urban drainage systems–a discussion of the additional efforts compared to conventionally operated systems. Urban Water J 10(5):293–299
Beielstein T, Parsopoulos KE, Vrahatis MN (2002) Tuning PSO parameters through sensitivity analysis. Technical Report, Reihe Computational Intelligence CI 124/02, Department of Computer Science, University of Dortmund
Che D, Mays LW (2015) Development of an optimization/simulation model for real-time flood-control operation of river-reservoirs systems. Water Resour Manag 29(11):3987–4005
Chiang YM, Chang LC, Tsai MJ, Wang YF, Chang FJ (2011) Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks. Hydrol Earth Syst Sci 15(1):185–196
Darsono S, Labadie JW (2007) Neural-optimal control algorithm for real-time regulation of in-line storage in combined sewer systems. Environ Model Softw 22(9):1349–1361
Delattre JM (1990) Real time control of combined sewer system from the user’s perspective. Urban Storm Water Quality Enhancement-Source Control, Retrofitting and Combine Sewer Technology, ASCE, New York: 366–389
GarcÃa L, Barreiro-Gomez J, Escobar E, Téllez D, Quijano N, Ocampo-MartÃnez C (2015) Modeling and real-time control of urban drainage systems: a review. Adv Water Resour 85:120–132
Garofalo G, Giordano A, Piro P, Spezzano G, Vinci A (2017) A distributed real-time approach for mitigating CSO and flooding in urban drainage systems. J Netw Comput Appl 78:30–42
Hsu NS, Huang CL, Wei CC (2013) Intelligent real-time operation of a pumping station for an urban drainage system. J Hydrol 489:85–97
Huber WC, Dickinson RE (1988) Storm water management model user’s manual, version 4. Rep. No. EPA/600/3-88/001a, U.S. Environmental Protection Agency, Athens, GA
Jia B, Simonovic SP, Zhong P, Yu Z (2016) A multi-objective best compromise decision model for real-time flood mitigation operations of multi-reservoir system. Water Resour Manag 30(10):3363–3387
Lansey KE, Awumah K (1994) Optimal pump operations considering pump switches. J Water Resour Plan Manag 120(1):17–35
Lee EH, Lee YS, Joo JG, Jung D, Kim JH (2017) Investigating the impact of proactive pump operation and capacity expansion on urban drainage system resilience. J Water Resour Plan Manag 143(7):04017024
Malekmohammadi B, Zahraie B, Kerachian R (2010) A real-time operation optimization model for flood management in river-reservoir systems. Nat Hazards 53(3):459–482
Mohammadi B, Marino MA (1984) Reservoir operation: choice of objective functions. J Water Resour Plan Manag 110(1):15–29
Pleau M, Colas H, Lavallée P, Pelletier G, Bonin R (2005) Global optimal real-time control of the Quebec urban drainage system. Environ Model Softw 20(4):401–413
Rao Z, Salomons E (2007) Development of a real-time, near-optimal control process for water-distribution networks. J Hydroinf 9(1):25–37
Rossman L A (2008) EPA SWMM users manual. Water supply and water resources division, US environmental protection agency, Cincinnati, OH Google Scholar
Schutze M, Campisano A, Colas H, Schilling W, Vanrolleghem PA (2004) Real-time control of urban wastewater systems—where do we stand today? J Hydrol 299(3):335–348
Wasimi SA, Kitanidis PK (1983) Real-time forecasting and daily operation of a multireservoir system during floods by linear quadratic Gaussian control. Water Resour Res 19(6):1511–1522
Wei CC, Hsu NS, Huang CL (2014) Two-stage pumping control model for flood mitigation in inundated urban drainage basins. Water Resour Manag 28(2):425–444
Weijs SV (2011) Information theory for risk-based water system operation. Dissertation, Delft Univ. of Technology, Delft, Netherlands
Yazdi J, Kim JH (2015) Intelligent pump operation and river diversion systems for urban storm management. J Hydrol Eng 20(11):04015031
Yazdi J, Choi HS, Kim JH (2016) A methodology for optimal operation of pumping stations in urban drainage systems. J Hydro Environ Res 11:101–112
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Jafari, F., Jamshid Mousavi, S., Yazdi, J. et al. Real-Time Operation of Pumping Systems for Urban Flood Mitigation: Single-Period vs. Multi-Period Optimization. Water Resour Manage 32, 4643–4660 (2018). https://doi.org/10.1007/s11269-018-2076-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11269-018-2076-4