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Modular Conceptual Modelling Approach and Software for Sewer Hydraulic Computations

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  • Vincent Wolfs

    (KU Leuven)

  • Patrick Willems

    (KU Leuven
    Vrije Universiteit Brussel)

Abstract

A major challenge in urban water management is the identification of cost-effective and future-proof strategies that can cope with the rapid urbanization and changing environmental conditions. Water quantity modelling forms a key-element in the development of such strategies. Conventional detailed hydrodynamic models are not well suited for use in decision support systems due to several important drawbacks. Therefore, this paper presents a novel and computationally efficient conceptual modelling approach for sewer water quantity simulations. A modular framework is considered that combines well-established model structures with machine learning techniques. This flexible framework ensures that even complex flow dynamics can be emulated accurately. An accompanying software tool was developed to facilitate model configuration. As an example, a full hydrodynamic sewer model of a city in Belgium was transformed into a conceptual model. This model delivered precise results, while the calculation time was 106 times shorter than the detailed model.

Suggested Citation

  • Vincent Wolfs & Patrick Willems, 2017. "Modular Conceptual Modelling Approach and Software for Sewer Hydraulic Computations," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 283-298, January.
    • Handle: RePEc:spr:waterr:v:31:y:2017:i:1:d:10.1007_s11269-016-1524-2
    DOI: 10.1007/s11269-016-1524-2
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    References listed on IDEAS

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    1. D. Nagesh Kumar & K. Srinivasa Raju & T. Sathish, 2004. "River Flow Forecasting using Recurrent Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 18(2), pages 143-161, April.
    2. Dragan Savic & Godfrey Walters & James Davidson, 1999. "A Genetic Programming Approach to Rainfall-Runoff Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 13(3), pages 219-231, June.
    3. Po-Kuan Chiang & Patrick Willems, 2013. "Model Conceptualization Procedure for River (Flood) Hydraulic Computations: Case Study of the Demer River, Belgium," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(12), pages 4277-4289, September.
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    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. María Bermúdez & Victor Ntegeka & Vincent Wolfs & Patrick Willems, 2018. "Development and Comparison of Two Fast Surrogate Models for Urban Pluvial Flood Simulations," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(8), pages 2801-2815, June.

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