Advertisement

Water Resources Management

, Volume 33, Issue 12, pp 4351–4365 | Cite as

An Integrated Fuzzy Simulation-Optimization Model for Supporting Low Impact Development Design under Uncertainty

  • Wei Lu
  • Xiaosheng QinEmail author
Article
  • 61 Downloads

Abstract

Seeking cost-effective design of urban hydrological facilities and drainage systems is an important task for many city planners. However, such a process has always been complicated with intrinsic uncertainties. This work presented an integrated fuzzy simulation-optimization model (FSOM) for supporting Low Impact Development (LID) design under model uncertainties. Various LID implementation schemes involving green roof, bio-retention cell, and permeable pavement were simulated through an urban hydrological model. Three model parameters were assumed as fuzzy sets. In a case study, fuzzy simulation (FS) and genetic algorithm (GA) were employed to search the optimal schemes of LIDs under various confidence levels of satisfying flood control constraints. Comparison of FSOM to traditional deterministic and stochastic models were also carried out. It was shown that FSOM could offer a flexible way of defining and assessing uncertainties associated with hydrological modeling and generate solutions that were comparable to those from either deterministic or stochastic models. However, FSOM also showed limitation of high computational requirement.

Keywords

Urban flood Low impact development Optimization Chance-constrained programming Fuzzy simulation Hydrological modeling uncertainties 

Abbreviations

BC

Bio-retention cell

CA

Commercial areas

CCP

Chance-constrained programming

CN

Curve number

FS

Fuzzy simulation

FSOM

Fuzzy simulation-optimization model

GA

Genetic algorithm

GR

Green roof

IDF

Intensity-duration-frequency

LID

Low impact development

MCS

Monte-Carlo simulation

MF

Membership function

PP

Permeable pavement

RA

Residential areas

SWMM

Storm water management model

UDS

Urban drainage system

Notes

Acknowledgements

This project was supported by Research Grant (M4082254.030) from School of Civil and Environmental Engineering, Nanyang Technological University, Singapore.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Supplementary material

11269_2019_2377_MOESM1_ESM.docx (66 kb)
ESM 1 (DOCX 66 kb)

References

  1. Arnbjerg-Nielsen K (2012) Quantification of climate change effects on extreme precipitation used for high resolution hydrologic design. Urban Water J 9(2):57–65CrossRefGoogle Scholar
  2. Banihabib ME, Tabari MMR, Tabari MMR (2019) Development of a fuzzy multi-objective heuristic model for optimum water allocation. Water Resour Manag 33(11):3673–3689CrossRefGoogle Scholar
  3. Chang NB, Rivera BJ, Wanielista MP (2011) Optimal design for water conservation and energy savings using green roofs in a green building under mixed uncertainties. J Clean Prod 19(11):1180–1188CrossRefGoogle Scholar
  4. Charnes A, Cooper WW (1959) Chance-constrained programming. Manag Sci 6(1):73–79CrossRefGoogle Scholar
  5. Chui TFM, Liu X, Zhan W (2016) Assessing cost-effectiveness of specific LID practice designs in response to large storm events. J Hydro 533:353–364CrossRefGoogle Scholar
  6. Dai C, Qin XS, Tan Q, Guo HC (2018) Optimizing best management practices for nutrient pollution control in a lake watershed under uncertainty. Ecol Indic 92:288–300CrossRefGoogle Scholar
  7. Dhingra AK, Lee BH (1994) A genetic algorithm approach to single and multiobjective structural optimization with discrete-continuous variables. Int J Numer Methods Eng 37:4059–4080CrossRefGoogle Scholar
  8. Dotto CB, Mannina G, Kleidorfer M, Vezzaro L, Henrichs M, McCarthy DT et al (2012) Comparison of different uncertainty techniques in urban stormwater quantity and quality modelling. Water Res 46(8):2545–2558CrossRefGoogle Scholar
  9. Duan HF, Li F, Tao T (2016) Multi-objective optimal design of detention tanks in the urban stormwater drainage system: uncertainty and sensitivity analysis. Water Resour Manag 30(7):2213–2226CrossRefGoogle Scholar
  10. Fu G, Butler D, Khu S, Sun S (2011) Imprecise probabilistic evaluation of sewer flooding in urban drainage systems using random set theory. Water Resour Res 47(2):155–170CrossRefGoogle Scholar
  11. Fu G, Kapelan Z (2013) Flood analysis of urban drainage systems: probabilistic dependence structure of rainfall characteristics and fuzzy model parameters. J Hydroinf 15(3):687–699CrossRefGoogle Scholar
  12. Getter KL, Rowe DB (2006) The role of extensive green roofs in sustainable development. HortScience 41(5):1276–1285CrossRefGoogle Scholar
  13. He L, Huang GH, Lu HW (2008) A simulation-based fuzzy chance-constrained programming model for optimal groundwater remediation under uncertainty. Adv Water Resour 31(12):1622–1635CrossRefGoogle Scholar
  14. Hu M, Sayama T, Zhang X, Tanaka K, Takara K, Yang H (2017) Evaluation of low impact development approach for mitigating flood inundation at a watershed scale in China. J Environ Manag 193:430–438CrossRefGoogle Scholar
  15. Huang GH (1998) A hybrid inexact-stochastic water management model. Eur J Oper Res 107(1):137–158CrossRefGoogle Scholar
  16. Huff FA (1967) Time distribution of rainfall in heavy storms. Water Resour Res 3(4):1007–1019CrossRefGoogle Scholar
  17. Jato-Espino D, Rodriguez-Hernandez J, Andrés-Valeri VC, Ballester-Muñoz F (2014) A fuzzy stochastic multi-criteria model for the selection of urban pervious pavements. Expert Syst Appl 41(15):6807–6817CrossRefGoogle Scholar
  18. Jun C, Qin XS, Lu W (2019) Temporal pattern analysis of rainstorm events for supporting rainfall design in a tropical city. In international conference on urban drainage modelling (pp. 380-384). Springer, ChamGoogle Scholar
  19. Krebs G, Kokkonen T, Valtanen M et al (2013) A high resolution application of a stormwater management model (swmm) using genetic parameter optimization. Urban Water J 10(6):394–410CrossRefGoogle Scholar
  20. Lakesuperiorstreams (2009) LakeSuperiorStreams: community partnerships for understanding water quality and stormwater impacts at the Head of the Great Lakes (http://lakesuperiorstreams.org). University of Minnesota-Duluth, Duluth, MNGoogle Scholar
  21. Li F, Yan XF, Duan HF (2019) Sustainable Design of Urban Stormwater Drainage Systems by implementing detention tank and LID measures for flooding risk control and water quality management. Water Resour Manag 33(9):3271–3288CrossRefGoogle Scholar
  22. Liu BD, Iwamura K (1998) Chance constrained programming with fuzzy parameters. Fuzzy Sets Syst 94(2):227–237CrossRefGoogle Scholar
  23. Lu W, Qin XS, Changhyun J (2019) A parsimonious framework of evaluating WSUD features in urban flood mitigation. J Environ Inform 33(1)Google Scholar
  24. Lu W, Qin XS, Yu JJ (2017) Emulator-aided optimization of detention tanks for flood reduction. International Association for Hydro-Environment Engineering and ResearchGoogle Scholar
  25. MATLAB (2015) Global optimization toolbox. Use’s guide (R2015b)Google Scholar
  26. Meteorological Service Singapore (2019) Climate of Singapore. http://www.weather.gov.sg/climate-climate-of-singapore/ (accessed Feb. 27 2019)
  27. Mooselu MG, Nikoo MR, Rayani NB, Izady A (2019) Fuzzy multi-objective simulation-optimization of stepped spillways considering flood uncertainty. Water Resour Manag 33(7):2261–2275CrossRefGoogle Scholar
  28. Noordhoek R (2014) Using water-sensitive Urban Design to improve drainage capacity: examination of the impact of distributed and catchment scale water-sensitive Urban Design systems on flow frequency. Bachelor Theses, University of South AustraliaGoogle Scholar
  29. Qin HP, Li ZX, Fu G (2013) The effects of low impact development on urban flooding under different rainfall characteristics. J Environ Manag 129:577–585CrossRefGoogle Scholar
  30. Revelli R, Ridolfi L (2002) Fuzzy approach for analysis of pipe networks. J Hydraul Eng 128(1):93–101CrossRefGoogle Scholar
  31. Ronalds R, Zhang H (2019) Assessing the impact of urban development and on-site Stormwater detention on regional hydrology using Monte Carlo simulated rainfall. Water Resour Manag 33(7):2517–2536CrossRefGoogle Scholar
  32. Ross JL, Ozbek MM, Pinder GF (2009) Aleatoric and epistemic uncertainty in groundwater flow and transport simulation. Water Resour Res 45(12)Google Scholar
  33. Rossman LA (2010) Storm water management model–user manual version 5.0. revised July 2010. US Environmental Protection Agency, Cincinnati, USAGoogle Scholar
  34. Toronto Region Conservation Authority (2010) Low impact development stormwater management; plannign and desing guideGoogle Scholar
  35. United States of America (2007) Energy Independence and security act. United States Government, Washington, DCGoogle Scholar
  36. US Environmental Protection Agency (US EPA) (2019) Storm Water Management Model (SWMM) - Version 5.1.010 with Low Impact Development (LID) Controls. https://www.epa.gov/water-research/storm-water-management-model-swmm (accessed Feb. 27 2019)
  37. Vogel JR, Moore TL, Coffman RR, Rodie SN, Hutchinson SL, McDonough KR, McLemore AJ, McMaine JT (2015) Critical review of technical questions facing low impact development and green infrastructure: a perspective from the great plains. Water Environ Res 87(9):849–862CrossRefGoogle Scholar
  38. Xu TY, Qin XS (2013) Solving water quality management problem through combined genetic algorithm and fuzzy simulation. J Environ Inform 22(1):39–48CrossRefGoogle Scholar
  39. Yazdi J, Neyshabouri SS (2014) Identifying low impact development strategies for flood mitigation using a fuzzy-probabilistic approach. Environ Model Softw 60:31–44CrossRefGoogle Scholar
  40. Yazdi J, Lee EH, Kim JH (2014) Stochastic multiobjective optimization model for urban drainage network rehabilitation. J Water Res Plan Man 141(8):04014091CrossRefGoogle Scholar
  41. Yu JJ, Qin XS, Chiew YM, Min R, Shen X (2017) Stochastic optimization model for supporting urban drainage design under complexity. J Water Res Plan Man 143(9):05017008CrossRefGoogle Scholar
  42. Zadeh L (1965) Fuzzy sets. Inf Control 8(3):338–353CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.School of Civil and Environmental EngineeringNanyang Technological UniversitySingaporeSingapore

Personalised recommendations