Advertisement

Water Resources Management

, Volume 33, Issue 12, pp 4201–4214 | Cite as

A Surrogate-Based Optimization Design and Uncertainty Analysis for Urban Flood Mitigation

  • Wen Zhang
  • Jing Li
  • Yunhao ChenEmail author
  • Yang Li
Article
  • 91 Downloads

Abstract

This study proposes a surrogate-based optimization framework (SBO) to help analyze the tradeoff between flood damages and investment while considering uncertainty originating from surrogates. The surrogate models were constructed based on the relationship between drainage specifications and simulated flood information and used to replace the numerical model in optimization, thereby reducing the computational burden. The bootstrapping approach was employed to quantify the uncertainty originating from surrogate models, which were incorporated into the NSGA-II optimization algorithm to seek the interval of optimal solutions. Through a case study, the results showed that the uncertainties caused by surrogate models have a significant influence on the reliability of the optimal solutions, but require lower computational efforts. Moreover, the local design conditions (i.e., various designed rainfalls) had an impact on the design and performance of the detention tanks. The proposed framework will facilitate cost-effective planning of flood mitigation systems with an awareness of associated uncertainty in order to resolve tradeoffs, particularly for large-scale problems.

Keywords

Urban flood mitigation design Surrogate model Multi-objective optimization Uncertainty analysis Bootstrapping method Ensemble-based 

Notes

Acknowledgements

We would like to thank Dr. Jianjun Yu, Chunyue Niu, and Zhihua Xu for helpful discussions that improved the manuscript. This work was supported through projects of the Beijing Laboratory of Water Resources Security and Beijing Flash Flood Disaster Monitoring and Risk Assessment, and Natural Science Foundation of China (51579135), and Science and Technology Plans of Ministry of Housing and Urban-Rural Development of the People’s Republic of China, and Opening Projects of Beijing Advanced Innovation Center for Future Urban Design, Beijing University of Civil Engineering and Architecture (UDC2017030212, UDC201650100).

Compliance with Ethical Standards

Conflict of Interest

None.

Supplementary material

11269_2019_2355_MOESM1_ESM.docx (271 kb)
ESM 1 (DOCX 270 kb)

References

  1. Cimorelli L, Morlando F, Cozzolino L, Covelli C, Della Morte R, Pianese D (2016) Optimal positioning and sizing of detention tanks within urban drainage networks. J Irrig Drain Eng 142(1):04015028.CrossRefGoogle Scholar
  2. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197CrossRefGoogle Scholar
  3. Danish Hydraulic Institute (2014a) MIKE 21/MIKE 3 flow model FM: hydrodynamic and transport module scientific documentation. http://manuals.mikepoweredbydhi.help/2017/Coast_and_Sea/MIKE_321_FM_Scientific_Doc.pdf
  4. Danish Hydraulic Institute (2014b) MIKE FLOOD: 1D-2D Modelling User Manual. http://manuals.mikepoweredbydhi.help/2017/Water_Resources/MIKE_FLOOD_UserManual.pdf Google Scholar
  5. Danish Hydraulic Institute (2014c) MOUSE: rainfall dependent inflow and infiltration reference manual. http://manuals.mikepoweredbydhi.help/2017/Cities/MOUSERDIIReference.pdf Google Scholar
  6. Efron B (1979) Bootstrap methods: another look at the jackknife. Ann Stat 7(1):1–26CrossRefGoogle Scholar
  7. El-Shafie A, El-Shafie AH, Mukhlisin M (2014) New approach: integrated risk-stochastic dynamic model for dam and reservoir optimization. Water Resour Manag 28(8):2093–2107CrossRefGoogle Scholar
  8. Hammond MJ, Chen AS, Djordjević S, Butler D, Mark O (2013) Urban flood impact assessment: A state-of-the-art review. Urban Water J 12(1):14–29CrossRefGoogle Scholar
  9. Han D, Kwong T, Li S (2007) Uncertainties in real-time flood forecasting with neural networks. Hydrol Process 21(2):223–228CrossRefGoogle Scholar
  10. Jones DR, Schonlau M, Welch W (1998) Efficient global optimization of expensive black-box functions. J Glob Optim 13(4):455–492CrossRefGoogle Scholar
  11. Kasiviswanathan KS, Cibin R, Sudheer KP, Chaubey I (2013) Constructing prediction interval for artificial neural network rainfall runoff models based on ensemble simulations. J Hydrol 499:275–288CrossRefGoogle Scholar
  12. Kingston GB, Lambert MF, Maier HR (2005) Bayesian training of artificial neural networks used for water resources modeling. Water Resour Res, 41(12)Google Scholar
  13. Khatavkar P, Mays LW (2017) Optimization models for the Design of Vegetative Filter Strips for Stormwater runoff and sediment control. Water Resour Manag 31(9):2545–2560CrossRefGoogle Scholar
  14. Lee JG, Selvakumar A, Alvi K, Riverson J, Zhen JX, Shoemaker L, Lai F (2012) A watershed-scale design optimization model for stormwater best management practices. Environ Model Softw 37:6–18CrossRefGoogle Scholar
  15. Li F, Duan HF, Yan HX, Tao T (2015) Multi-objective optimal design of detention tanks in the urban stormwater drainage system: framework development and case study. Water Resour Manag 29(7):2125–2137CrossRefGoogle Scholar
  16. Limbrunner JF, Vogel RM, Chapra SC, Kirshen PH (2013) Classic optimization techniques applied to stormwater and nonpoint source pollution management at the watershed scale. J Water Resour Plan Manag 139(5):486–491CrossRefGoogle Scholar
  17. Liu Y, Theller LO, Pijanowski BC, Engel BA (2016) Optimal selection and placement of green infrastructure to reduce impacts of land use change and climate change on hydrology and water quality: an application to the Trail Creek watershed, Indiana. Sci Total Environ 553:149–163CrossRefGoogle Scholar
  18. Loáiciga HA, Sadeghi KM, Shivers S, Kharaghani S (2015) Stormwater control measures: optimization methods for sizing and selection. J Water Resour Plan Manag 141(9):04015006CrossRefGoogle Scholar
  19. Lu W, Qin X, Yu J (2019) On comparison of two-level and global optimization schemes for layout design of storage ponds. J Hydrol 570:544–554CrossRefGoogle Scholar
  20. McKay MD, Beckman RJ, Conover WJ (1979) A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21(2):239–245Google Scholar
  21. Oxley RL, Mays LW (2014) Optimization–simulation model for detention basin system design. Water Resour Manag 28(4):1157–1171CrossRefGoogle Scholar
  22. Parasuraman K, Elshorbagy A (2008) Toward improving the reliability of hydrologic prediction: model structure uncertainty and its quantification using ensemble-based genetic programming framework. Water Resour Res 44(12)Google Scholar
  23. Razavi S, Tolson BA, Burn DH (2012) Review of surrogate modeling in water resources. Water Resour Res, 48(7)Google Scholar
  24. Reichstein M, Camps-Valls G, Stevens B, et al. (2019) Deep learning and process understanding for data-driven Earth system science. Nature 566(7743):195–204CrossRefGoogle Scholar
  25. Sreekanth J, Datta B (2011). Coupled simulation-optimization model for coastal aquifer management using genetic programming-based ensemble surrogate models and multiple-realization optimization. Water Resour Res, 47(4)Google Scholar
  26. Srivastav RK, Sudheer KP, Chaubey I (2007) A simplified approach to quantifying predictive and parametric uncertainty in artificial neural network hydrologic models. Water Resour Res, 43(10)Google Scholar
  27. Tsoukalas I, Makropoulos C (2015a) Multiobjective optimisation on a budget: exploring surrogate modelling for robust multi-reservoir rules generation under hydrological uncertainty. Environ Model Softw 69:396–413CrossRefGoogle Scholar
  28. Tsoukalas I, Makropoulos C (2015b) A surrogate based optimization approach for the development of uncertainty-aware reservoir operational rules: the case of nestos hydrosystem. Water Resour Manag 29(13):4719–4734CrossRefGoogle Scholar
  29. Yazdi J, Salehi Neyshabouri SAA (2012) A simulation-based optimization model for flood management on a watershed scale. Water Resour Manag 26(15):4569–4586CrossRefGoogle Scholar
  30. Yazdi J, Salehi Neyshabour SAA (2014) Adaptive surrogate modeling for optimization of flood control detention dams. Environ Model Softw 61:106–120CrossRefGoogle Scholar
  31. Yazdi J, Salehi Neyshabouri SAA (2015) An optimization model for floodplain systems considering inflow uncertainties. Water Resour Manag 29(4):1295–1313CrossRefGoogle Scholar
  32. Yu JJ, Qin XS, Larsen O, Chua Chua (2014) Comparison between Response Surface Models and Artificial Neural Networks in Hydrologic Forecasting. J Hydrol Eng 19(3):473–481CrossRefGoogle Scholar
  33. Yu JJ, Qin XS, Larsen O (2015) Applying ANN emulators in uncertainty assessment of flood inundation modelling: a comparison of two surrogate schemes. Hydrol Sci J 60(12):2117–2131CrossRefGoogle Scholar
  34. Yu JJ, Qin XS, Chiew YM, Min R, Shen XL (2017) Stochastic Optimization Model for Supporting Urban Drainage Design under Complexity. J Water Res Plan Man 143(9):05017008CrossRefGoogle Scholar
  35. Zhang XS, Srinivasan R, Liew MV (2009) Approximating SWAT model using artificial neural network and support vector machine. JAWRA J Am Water Resour Assoc 45(2):460–474CrossRefGoogle Scholar
  36. Zhang W, Li J, Chen YH (2018) Detention tanks size optimization for urban flood mitigation. Journal of Beijing Normal University(Natural Science) 54(06):745–754Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Remote Sensing Science, Faculty of Geographical ScienceBeijing Normal UniversityBeijingChina
  2. 2.Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Faculty of Geographical ScienceBeijing Normal UniversityBeijingChina

Personalised recommendations