An Online Data-Driven Evolutionary Algorithm–Based Optimal Design of Urban Stormwater-Drainage Systems
Publication: Journal of Irrigation and Drainage Engineering
Volume 148, Issue 11
Abstract
To reduce flood risk in urban areas, an optimal design of drainage networks for urban areas is essential for flooding control and drainage management of the urban stormwater drainage system (USDS). A conventional design is generally used for drainage networks, resulting in high computational costs and limited flooding reduction effect. In this study, based on an on-line data-driven evolutionary algorithm coupled with the storm water management model (SWMM), a novel approach for USDS drainage network optimization design was developed. A case in Xi’an City, China, was then selected for practical implementation, where the performances of the local planning scheme, the particle swarm optimization algorithm (PSO) and the proposed approach were compared. Results confirmed that our proposed methodological approach is feasibility and highly efficiency, leading to a 32% reduction in total flooding from that resulting from the local planning scheme. In addition, the average computational time was reduced by 57%, while the flooding control effect was better, compared to PSO algorithm optimization. These results suggest that our optimization design approach is reliable and applicable, and can benefit and assist designers in practice.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
The data and code that support this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was partly supported by the National Natural Science Foundation of China (Nos. 5160919 and 52009104), and the Sino-German Mobility Programme (Grant No. M-0427).
References
Alhumaid, M., A. R. Ghumman, H. Haider, I. S. Al-Salamah, and Y. M. Ghazaw. 2018. “Sustainability evaluation framework of urban stormwater drainage options for arid environments using hydraulic modeling and multicriteria decision-making.” Water 10 (5): 581. https://doi.org/10.3390/w10050581.
Azizipour, M., A. Sattari, M. H. Afshar, E. Goharian, and S. S. Solis. 2020. “Optimal hydropower operation of multi-reservoir systems: Hybrid cellular automata-simulated annealing approach.” J. Hydroinf. 22 (5): 1236–1257. https://doi.org/10.2166/hydro.2020.168.
Bi, X., C. Dang, L. Cheng, H. Luo, M. Shi, and R. Zhao. 2015. “Study on compiling rainstorm intensity formula in Xi’an urban area.” J. Anhui Agric. Sci. 43 (26): 223–225. https://doi.org/10.13989/j.cnki.0517-6611.2015.26.212.
Chen, G., J. Hou, N. Zhou, S. Yang, Y. Tong, F. Su, and X. Bi. 2020. “High-resolution urban flood forecasting by using a coupled atmospheric and hydrodynamic flood models.” Front. Earth Sci. 8 (Oct): 545612. https://doi.org/10.3389/feart.2020.545612.
Cimorelli, L., L. Cozzolino, C. Covelli, C. Mucherino, A. Palumbo, and D. Pianese. 2013. “Optimal design of rural drainage networks.” J. Irrig. Drain. Eng. 139 (2): 137–144. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000526.
Cimorelli, L., F. Morlando, L. Cozzolino, C. Covelli, R. Della Morte, and D. Pianese. 2016. “Optimal positioning and sizing of detention tanks within urban drainage networks.” J. Irrig. Drain. Eng. 142 (1): 04015028. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000927.
Datta, R., and R. G. Regis. 2016. “A surrogate-assisted evolution strategy for constrained multi-objective optimization.” Expert Syst. Appl. 57 (Sep): 270–284. https://doi.org/10.1016/j.eswa.2016.03.044.
Duan, H. F., F. Li, and H. X. Yan. 2016. “Multi-objective optimal design of detention tanks in the urban stormwater drainage system: LID implementation and analysis.” Water Resour. Manage. 30 (13): 4635–4648. https://doi.org/10.1007/s11269-016-1444-1.
Guo, Z., J. P. Leitao, N. E. Simoes, and V. Moosavi. 2020. “Data-driven flood emulation: Speeding up urban flood predictions by deep convolutional neural networks.” J. Flood Risk Manage. 14 (1): e12684. https://doi.org/10.1111/jfr3.12684.
Hou, J. M., H. Han, Z. B. Li, K. H. Guo, and Y. Qin. 2018. “Effects of morphological change on fluvial flood patterns evaluated by a hydro-geomorphological model.” J. Hydroinf. 20 (3): 633–644. https://doi.org/10.2166/hydro.2018.142.
Jafari, F., S. J. Mousavi, J. Yazdi, and J. H. Kim. 2018. “Real-time operation of pumping systems for urban flood mitigation: Single-period vs. multi-period optimization.” Water Resour. Manage. 32 (14): 4643–4660. https://doi.org/10.1007/s11269-018-2076-4.
Jehanzaib, M., M. B. Idrees, D. Kim, and T. W. Kim. 2021. “Comprehensive evaluation of machine learning techniques for hydrological drought forecasting.” J. Irrig. Drain. Eng. 147 (7): 04021022. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001575.
Jin, Y., M. Olhofer, and B. Sendhoff. 2000. “On evolutionary optimization with approximate fitness functions.” In Proc., Genetic and Evolutionary Computation Conf., 786–793. Burlington, MA: Morgan Kaufmann.
Jin, Y. C., H. D. Wang, T. Chugh, D. Guo, and K. Miettinen. 2019. “Data-driven evolutionary optimization: An overview and case studies.” IEEE Trans. Evol. Comput. 23 (3): 442–458. https://doi.org/10.1109/TEVC.2018.2869001.
Li, F., W. M. Shen, X. W. Cai, L. Gao, and G. G. Wang. 2020. “A fast surrogate-assisted particle swarm optimization algorithm for computationally expensive problems.” Appl. Soft Comput. 92 (Jul): 106303. https://doi.org/10.1016/j.asoc.2020.106303.
Liang, J. J., A. K. Qin, P. N. Suganthan, and S. Baskar. 2006. “Comprehensive learning particle swarm optimizer for global optimization of multimodal functions.” IEEE Trans. Evol. Comput. 10 (3): 281–295. https://doi.org/10.1109/TEVC.2005.857610.
Liao, Z. L., X. Y. Gu, J. Q. Xie, X. Wang, and J. X. Chen. 2019. “An integrated assessment of drainage system reconstruction based on a drainage network model.” Environ. Sci. Pollut. Res. 26 (26): 26563–26576. https://doi.org/10.1007/s11356-019-05280-1.
Liu, B., Q. F. Zhang, and G. G. E. Gielen. 2014. “A Gaussian process surrogate model assisted evolutionary algorithm for medium scale expensive optimization problems.” IEEE Trans. Evol. Comput. 18 (2): 180–192. https://doi.org/10.1109/TEVC.2013.2248012.
Meierdiercks, K. L., J. A. Smith, M. L. Baeck, and A. J. Miller. 2010. “Analyses of urban drainage network structure and its impact on hydrologic response.” J. Am. Water Resour. Assoc. 46 (5): 932–943. https://doi.org/10.1111/j.1752-1688.2010.00465.x.
Mollerup, A. L., P. S. Mikkelsen, and G. Sin. 2016. “A methodological approach to the design of optimising control strategies for sewer systems.” Environ. Modell. Software 83 (Sep): 103–115. https://doi.org/10.1016/j.envsoft.2016.05.004.
Muis, S., B. Guneralp, B. Jongman, J. Aerts, and P. J. Ward. 2015. “Flood risk and adaptation strategies under climate change and urban expansion: A probabilistic analysis using global data.” Sci. Total Environ. 538 (Dec): 445–457. https://doi.org/10.1016/j.scitotenv.2015.08.068.
Nash, J. E., and J. V. Sutcliffe. 1970. “River flow forecasting through conceptual models part I—A discussion of principles.” J. Hydrol. 10 (3): 282–290. https://doi.org/10.1016/0022-1694(70)90255-6.
Palla, A., and I. Gnecco. 2015. “Hydrologic modeling of low impact development systems at the urban catchment scale.” J. Hydrol. 528 (Sep): 361–368. https://doi.org/10.1016/j.jhydrol.2015.06.050.
Pandhiani, S. M., P. Sihag, A. B. Shabri, B. Singh, and Q. B. Pham. 2020. “Time-series prediction of streamflows of Malaysian rivers using data-driven techniques.” J. Irrig. Drain. Eng. 146 (7): 04020013. https://doi.org/10.1061/(asce)ir.1943-4774.0001463.
Peng, H. Q., Y. Liu, H. W. Wang, X. L. Gao, Y. Chen, and L. M. Ma. 2016. “Urban stormwater forecasting model and drainage optimization based on water environmental capacity.” Environ. Earth Sci. 75 (14): 1094. https://doi.org/10.1007/s12665-016-5824-x.
Qin, H. P., Z. X. Li, and G. T. Fu. 2013. “The effects of low impact development on urban flooding under different rainfall characteristics.” J. Environ. Manage. 129 (Nov): 577–585. https://doi.org/10.1016/j.jenvman.2013.08.026.
Regis, R. G. 2014a. “Evolutionary programming for high-dimensional constrained expensive black-box optimization using radial basis functions.” IEEE Trans. Evol. Comput. 18 (3): 326–347. https://doi.org/10.1109/TEVC.2013.2262111.
Regis, R. G. 2014b. “Particle swarm with radial basis function surrogates for expensive black-box optimization.” J. Comput. Sci. 5 (1): 12–23. https://doi.org/10.1016/j.jocs.2013.07.004.
Riano-Briceno, G., J. Barreiro-Gomez, A. Ramirez-Jaime, N. Quijano, and C. Ocampo-Martinez. 2016. “MatSWMM—An open-source toolbox for designing real-time control of urban drainage systems.” Environ. Modell. Software 83 (Sep): 143–154. https://doi.org/10.1016/j.envsoft.2016.05.009.
Rossman, L. A. 2015. Storm water management model: User’s manual version 5.1. Cincinnati: National Risk Management Research Laboratory.
Sadler, J. M., J. L. Goodall, M. Behl, M. M. Morsy, T. B. Culver, and B. D. Bowes. 2019. “Leveraging open source software and parallel computing for model predictive control of urban drainage systems using EPA-SWMM5.” Environ. Modell. Software 120 (Oct): 104484. https://doi.org/10.1016/j.envsoft.2019.07.009.
Shanghai General Municipal Engineering Design and Research Institute. 2006. Code for design of outdoor wastewater engineering. [In Chinese.] Beijing: China Planning Press.
Shishegar, S., S. Duchesne, and G. Pelletier. 2018. “Optimization methods applied to stormwater management problems: A review.” Urban Water J. 15 (3): 276–286. https://doi.org/10.1080/1573062X.2018.1439976.
Singh, R. M. 2013. “Uncertainty characterization in the design of flow diversion structure profiles using genetic algorithm and fuzzy logic.” J. Irrig. Drain. Eng. 139 (2): 145–157. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000490.
Sun, C. L., Y. C. Jin, J. C. Zeng, and Y. Yu. 2015. “A two-layer surrogate-assisted particle swarm optimization algorithm.” Soft Comput. 19 (6): 1461–1475. https://doi.org/10.1007/s00500-014-1283-z.
Wang, H., X. H. Lei, S. T. Khu, and L. X. Song. 2019a. “Optimization of pump start-up depth in drainage pumping station based on SWMM and PSO.” Water 11 (5): 1002. https://doi.org/10.3390/w11051002.
Wang, H. D., Y. C. Jin, and J. O. Jansen. 2016. “Data-driven surrogate-assisted multiobjective evolutionary optimization of a trauma system.” IEEE Trans. Evol. Comput. 20 (6): 939–952. https://doi.org/10.1109/TEVC.2016.2555315.
Wang, H. D., Y. C. Jin, C. L. Sun, and J. Doherty. 2019b. “Offline data-driven evolutionary optimization using selective surrogate ensembles.” IEEE Trans. Evol. Comput. 23 (2): 203–216. https://doi.org/10.1109/TEVC.2018.2834881.
Wu, X. X., M. Y. Sun, X. Chen, J. Wang, and B. L. Guo. 2019. “Empirical study of particle swarm optimization inspired by Lotka-Volterra model in ecology.” Soft Comput. 23 (14): 5571–5582. https://doi.org/10.1007/s00500-018-3215-9.
Xu, W., and C. Chen. 2020. “Optimization of operation strategies for an interbasin water diversion system using an aggregation model and improved NSGA-II algorithm.” J. Irrig. Drain. Eng. 146 (5): 04020006. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001462.
Yazdi, J., H. S. Choi, and J. H. Kim. 2016. “A methodology for optimal operation of pumping stations in urban drainage systems.” J. Hydro-environ. Res. 11 (Jun): 101–112. https://doi.org/10.1016/j.jher.2015.09.001.
Yazdi, J., S. Mohammadiun, R. Sadiq, S. Neyshabouri, and A. A. Gharahbagh. 2018. “Assessment of different MOEAs for rehabilitation evaluation of urban stormwater drainage systems—Case study: Eastern catchment of Tehran.” J. Hydro-environ. Res. 21 (Oct): 76–85. https://doi.org/10.1016/j.jher.2018.08.002.
Zhan, Z. H., J. Zhang, Y. Li, and H. S. H. Chung. 2009. “Adaptive particle swarm optimization.” IEEE Trans. Syst. Man Cybern. Part B Cybern. 39 (6): 1362–1381. https://doi.org/10.1109/TSMCB.2009.2015956.
Information & Authors
Information
Published In
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Jul 15, 2021
Accepted: Apr 27, 2022
Published online: Sep 10, 2022
Published in print: Nov 1, 2022
Discussion open until: Feb 10, 2023
ASCE Technical Topics:
- Algorithms
- Design (by type)
- Drainage
- Drainage systems
- Engineering fundamentals
- Environmental engineering
- Floods
- Hydraulic design
- Hydraulic engineering
- Hydraulic structures
- Infrastructure
- Irrigation engineering
- Mathematics
- Stormwater management
- Urban and regional development
- Water and water resources
- Water treatment
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Zongliang Guo, Sikai Lin, Runze Suo, Xinming Zhang, An Offline Weighted-Bagging Data-Driven Evolutionary Algorithm with Data Generation Based on Clustering, Mathematics, 10.3390/math11020431, 11, 2, (431), (2023).