IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v32y2018i15d10.1007_s11269-018-2157-4.html
   My bibliography  Save this article

A Hybrid Surrogate Modelling Strategy for Simplification of Detailed Urban Drainage Simulators

Author

Listed:
  • Mahmood Mahmoodian

    (Luxembourg Institute of Science and Technology
    Delft University of Technology)

  • Juan Pablo Carbajal

    (Swiss Federal Institute of Aquatic Science and Technology, Eawag)

  • Vasilis Bellos

    (CH2M
    National Technical University of Athens)

  • Ulrich Leopold

    (Luxembourg Institute of Science and Technology)

  • Georges Schutz

    (RTC4Water)

  • Francois Clemens

    (Delft University of Technology
    Deltares)

Abstract

Urban drainage modelling typically requires development of highly detailed simulators due to the nature of various underlying surface and drainage processes, which makes them computationally too expensive. Application of such simulators is still challenging in activities such as real-time control (RTC), uncertainty quantification analysis or model calibration in which numerous simulations are required. The focus of this paper is to present a rather simple hybrid surrogate modelling (or emulation) strategy to simplify and accelerate a detailed urban drainage simulator (UDS). The proposed surrogate modelling strategy includes: a) identification of the variables to be emulated; b) development of a simplified conceptual model in which every component contributing to the variables identified in step (a) is replaced by a function; c) definition of these functions, either based on knowledge about the mechanisms of the simulator, or based on the data produced by the simulator; and finally, d) validation of the results produced by the surrogate model in comparison with the original detailed simulator. Herein, a detailed InfoWorks ICM simulator was selected for surrogate modelling. The case study area was a small urban drainage network in Luxembourg. An emulator was developed to map the rainfall time series, as input, to a storage tank volume and combined sewer overflow (CSO) in the case study network. The results showed that the introduced strategy provides a reliable method to simplify the simulator and reduce its run time significantly. For the specific case study, the emulator was approximately 1300 times faster than the original detailed simulator. For quantification of the emulation error, an ensemble of 500 rainfall scenarios with 1 month duration was generated by application of a multivariate autoregressive model for conditional simulation of rainfall time series. The results produced by the emulator were compared to the ones produced by the simulator. Finally, as an indicator of the emulation error, distributions of Nash-Sutcliffe efficiency (NSE) between the emulator and simulator results for prediction of storage tank volume and CSO flow time series were presented.

Suggested Citation

  • Mahmood Mahmoodian & Juan Pablo Carbajal & Vasilis Bellos & Ulrich Leopold & Georges Schutz & Francois Clemens, 2018. "A Hybrid Surrogate Modelling Strategy for Simplification of Detailed Urban Drainage Simulators," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(15), pages 5241-5256, December.
  • Handle: RePEc:spr:waterr:v:32:y:2018:i:15:d:10.1007_s11269-018-2157-4
    DOI: 10.1007/s11269-018-2157-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-018-2157-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11269-018-2157-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chuan Li & Yun Bai & Bo Zeng, 2016. "Deep Feature Learning Architectures for Daily Reservoir Inflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(14), pages 5145-5161, November.
    2. Rommel Regis & Christine Shoemaker, 2005. "Constrained Global Optimization of Expensive Black Box Functions Using Radial Basis Functions," Journal of Global Optimization, Springer, vol. 31(1), pages 153-171, January.
    3. J. Sreekanth & Bithin Datta, 2011. "Comparative Evaluation of Genetic Programming and Neural Network as Potential Surrogate Models for Coastal Aquifer Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(13), pages 3201-3218, October.
    4. Vasileios Christelis & Aristotelis Mantoglou, 2016. "Pumping Optimization of Coastal Aquifers Assisted by Adaptive Metamodelling Methods and Radial Basis Functions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(15), pages 5845-5859, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhenliang Liao & Zhiyu Zhang & Wenchong Tian & Xianyong Gu & Jiaqiang Xie, 2022. "Comparison of Real-time Control Methods for CSO Reduction with Two Evaluation Indices: Computing Load Rate and Double Baseline Normalized Distance," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(12), pages 4469-4484, September.
    2. Vassilios A. Tsihrintzis & Harris Vangelis, 2018. "Water Resources and Environment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(15), pages 4813-4817, December.
    3. Shahin Zandmoghaddam & Ali Nazemi & Elmira Hassanzadeh & Shadi Hatami, 2019. "Representing Local Dynamics of Water Resource Systems through a Data-Driven Emulation Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(10), pages 3579-3594, August.
    4. Vasilis Bellos & Ino Papageorgaki & Ioannis Kourtis & Harris Vangelis & Ioannis Kalogiros & George Tsakiris, 2020. "Reconstruction of a flash flood event using a 2D hydrodynamic model under spatial and temporal variability of storm," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 101(3), pages 711-726, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Krityakierne, Tipaluck & Baowan, Duangkamon, 2020. "Aggregated GP-based Optimization for Contaminant Source Localization," Operations Research Perspectives, Elsevier, vol. 7(C).
    2. Vasileios Christelis & Aristotelis Mantoglou, 2016. "Pumping Optimization of Coastal Aquifers Assisted by Adaptive Metamodelling Methods and Radial Basis Functions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(15), pages 5845-5859, December.
    3. Vasileios Christelis & Aristotelis Mantoglou, 2016. "Coastal Aquifer Management Based on the Joint use of Density-Dependent and Sharp Interface Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(2), pages 861-876, January.
    4. Vasileios Christelis & Aristotelis Mantoglou, 2016. "Coastal Aquifer Management Based on the Joint use of Density-Dependent and Sharp Interface Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(2), pages 861-876, January.
    5. Alvin Lal & Bithin Datta, 2018. "Development and Implementation of Support Vector Machine Regression Surrogate Models for Predicting Groundwater Pumping-Induced Saltwater Intrusion into Coastal Aquifers," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(7), pages 2405-2419, May.
    6. Madan K. Jha & Richard C. Peralta & Sasmita Sahoo, 2020. "Simulation-Optimization for Conjunctive Water Resources Management and Optimal Crop Planning in Kushabhadra-Bhargavi River Delta of Eastern India," IJERPH, MDPI, vol. 17(10), pages 1-20, May.
    7. Zheng, Liang & Xue, Xinfeng & Xu, Chengcheng & Ran, Bin, 2019. "A stochastic simulation-based optimization method for equitable and efficient network-wide signal timing under uncertainties," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 287-308.
    8. Hoseinzade, Davood & Lakzian, Esmail & Hashemian, Ali, 2021. "A blackbox optimization of volumetric heating rate for reducing the wetness of the steam flow through turbine blades," Energy, Elsevier, vol. 220(C).
    9. Shishir Gaur & Apurve Dave & Anurag Gupta & Anurag Ohri & Didier Graillot & S. B. Dwivedi, 2018. "Application of Artificial Neural Networks for Identifying Optimal Groundwater Pumping and Piping Network Layout," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(15), pages 5067-5079, December.
    10. Xinxin He & Jungang Luo & Ganggang Zuo & Jiancang Xie, 2019. "Daily Runoff Forecasting Using a Hybrid Model Based on Variational Mode Decomposition and Deep Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(4), pages 1571-1590, March.
    11. Juliane Müller & Christine Shoemaker & Robert Piché, 2014. "SO-I: a surrogate model algorithm for expensive nonlinear integer programming problems including global optimization applications," Journal of Global Optimization, Springer, vol. 59(4), pages 865-889, August.
    12. Jesús Ferrero Bermejo & Juan Francisco Gómez Fernández & Rafael Pino & Adolfo Crespo Márquez & Antonio Jesús Guillén López, 2019. "Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants," Energies, MDPI, vol. 12(21), pages 1-18, October.
    13. H. Le Thi & A. Vaz & L. Vicente, 2012. "Optimizing radial basis functions by d.c. programming and its use in direct search for global derivative-free optimization," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(1), pages 190-214, April.
    14. Saleem Ramadan, 2016. "A Hybrid Global Optimization Method Based on Genetic Algorithm and Shrinking Box," Modern Applied Science, Canadian Center of Science and Education, vol. 10(2), pages 1-67, February.
    15. Subhajit Dey & Om Prakash, 2022. "Coupled Sharp-interface and Density-dependent Model for Simultaneous Optimization of Production Well Locations and Pumping in Coastal Aquifer," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(7), pages 2327-2341, May.
    16. Zhe Zhou & Fusheng Bai, 2018. "An adaptive framework for costly black-box global optimization based on radial basis function interpolation," Journal of Global Optimization, Springer, vol. 70(4), pages 757-781, April.
    17. Anthony Mouraud, 2017. "Innovative time series forecasting: auto regressive moving average vs deep networks," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 4(3), pages 282-293, March.
    18. Yu, Xiayang & Sreekanth, J. & Cui, Tao & Pickett, Trevor & Xin, Pei, 2021. "Adaptative DNN emulator-enabled multi-objective optimization to manage aquifer−sea flux interactions in a regional coastal aquifer," Agricultural Water Management, Elsevier, vol. 245(C).
    19. Taimoor Akhtar & Christine Shoemaker, 2016. "Multi objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selection," Journal of Global Optimization, Springer, vol. 64(1), pages 17-32, January.
    20. Rommel G. Regis & Christine A. Shoemaker, 2009. "Parallel Stochastic Global Optimization Using Radial Basis Functions," INFORMS Journal on Computing, INFORMS, vol. 21(3), pages 411-426, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:32:y:2018:i:15:d:10.1007_s11269-018-2157-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.