IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v24y2010i15p4469-4489.html
   My bibliography  Save this article

Selecting Model Parameter Sets from a Trade-off Surface Generated from the Non-Dominated Sorting Genetic Algorithm-II

Author

Listed:
  • Gift Dumedah
  • Aaron Berg
  • Mark Wineberg
  • Robert Collier

Abstract

There is increasing trend in the use of multi-objective genetic algorithms (GAs) to estimate parameter sets in the calibration of hydrological models. Multi-objective GAs facilitate the evaluation of several model evaluation objectives, and the examination of massive combinations of parameter sets. Typically, the outcome is a set of several equally-accurate parameter sets which make-up a trade-off surface between the objective functions, usually referred to as Pareto set. The Pareto set is a set of incomparable parameter sets as each solution has unique parameter values in parameter space with competing accuracy in the objective function space. As would be required for decision making purposes, a single parameter set is usually chosen to represent the model calibration procedure. An automated framework for choosing a single solution from such a trade-off surface has not been thoroughly investigated in the model calibration literature. As a result, this study has outlined an automated framework using the distribution of solutions in objective space and parameter space to select solutions with unique properties from an incomparable set of solutions. Our Pareto set was generated from the application of Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to calibrate the Soil and Water Assessment Tool (SWAT) for simulations of streamflow in the Fairchild Creek watershed in southern Ontario. Using cluster analysis to evaluate the distribution of solutions in both objective space and parameter space, we developed four auto-selection methods for choosing parameter sets from the trade-off surface to support decision making. Our method generates solutions with unique properties including a representative pathway in parameter space, a basin of attraction (or the center of mass) in objective space, a proximity to the origin in objective space, and a balanced compromise between objective space and parameter space (denoted BCOP). The BCOP method is appealing as it is an equally-weighted compromise for the distribution of solutions in objective space and parameter space. That is, the BCOP solution emphasizes stability in model parameter values and in objective function values—in a way that similarity in parameter space implies similarity in objective space. Copyright Springer Science+Business Media B.V. 2010

Suggested Citation

  • Gift Dumedah & Aaron Berg & Mark Wineberg & Robert Collier, 2010. "Selecting Model Parameter Sets from a Trade-off Surface Generated from the Non-Dominated Sorting Genetic Algorithm-II," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(15), pages 4469-4489, December.
  • Handle: RePEc:spr:waterr:v:24:y:2010:i:15:p:4469-4489
    DOI: 10.1007/s11269-010-9668-y
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11269-010-9668-y
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s11269-010-9668-y?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. Robert Thorndike, 1953. "Who belongs in the family?," Psychometrika, Springer;The Psychometric Society, vol. 18(4), pages 267-276, 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. Jun Guo & Jianzhong Zhou & Qiang Zou & Yi Liu & Lixiang Song, 2013. "A Novel Multi-Objective Shuffled Complex Differential Evolution Algorithm with Application to Hydrological Model Parameter Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(8), pages 2923-2946, June.
    2. Duan Chen & Qiuwen Chen & Arturo S. Leon & Ruonan Li, 2016. "A Genetic Algorithm Parallel Strategy for Optimizing the Operation of Reservoir with Multiple Eco-environmental Objectives," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(7), pages 2127-2142, May.
    3. Minglong Dai & Jianzhong Zhou & Xiang Liao, 2016. "Research on Combination Forecast Mode of Conceptual Hydrological Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4483-4499, October.
    4. Akshay Kadu & Basudev Biswal, 2022. "A Model Combination Approach for Improving Streamflow Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 5945-5959, December.
    5. Jianzhong Zhou & Shuo Ouyang & Xuemin Wang & Lei Ye & Hao Wang, 2014. "Multi-Objective Parameter Calibration and Multi-Attribute Decision-Making: An Application to Conceptual Hydrological Model Calibration," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(3), pages 767-783, February.
    6. Prakash Kaini & Kim Artita & John Nicklow, 2012. "Optimizing Structural Best Management Practices Using SWAT and Genetic Algorithm to Improve Water Quality Goals," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(7), pages 1827-1845, May.
    7. Qi Wang & Enrico Creaco & Marco Franchini & Dragan Savić & Zoran Kapelan, 2015. "Comparing Low and High-Level Hybrid Algorithms on the Two-Objective Optimal Design of Water Distribution Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(1), pages 1-16, January.
    8. Ahmad Sharafati & Siyamak Doroudi & Shamsuddin Shahid & Ali Moridi, 2021. "A Novel Stochastic Approach for Optimization of Diversion System Dimension by Considering Hydrological and Hydraulic Uncertainties," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(11), pages 3649-3677, September.
    9. Tao Bai & Lianzhou Wu & Jian-xia Chang & Qiang Huang, 2015. "Multi-Objective Optimal Operation Model of Cascade Reservoirs and Its Application on Water and Sediment Regulation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2751-2770, June.
    10. Tian Peng & Jianzhong Zhou & Chu Zhang & Na Sun, 2018. "Modeling and Combined Application of Orthogonal Chaotic NSGA-II and Improved TOPSIS to Optimize a Conceptual Hydrological Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(11), pages 3781-3799, September.
    11. Zeinab Takbiri & Abbas Afshar, 2012. "Multi-Objective Optimization of Fusegates System under Hydrologic Uncertainties," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(8), pages 2323-2345, June.
    12. Jose-Luis Molina & Raziyeh Farmani & John Bromley, 2011. "Aquifers Management through Evolutionary Bayesian Networks: The Altiplano Case Study (SE Spain)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(14), pages 3883-3909, November.
    13. Gift Dumedah, 2012. "Formulation of the Evolutionary-Based Data Assimilation, and its Implementation in Hydrological Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(13), pages 3853-3870, October.

    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. Koecklin, Manuel Tong & Longoria, Genaro & Fitiwi, Desta Z. & DeCarolis, Joseph F. & Curtis, John, 2021. "Public acceptance of renewable electricity generation and transmission network developments: Insights from Ireland," Energy Policy, Elsevier, vol. 151(C).
    2. Becken, Susanne & Stantic, Bela & Chen, Jinyan & Connolly, Rod M., 2022. "Twitter conversations reveal issue salience of aviation in the broader context of climate change," Journal of Air Transport Management, Elsevier, vol. 98(C).
    3. Rockstuhl, Sebastian & Wenninger, Simon & Wiethe, Christian & Ahlrichs, Jakob, 2022. "The influence of risk perception on energy efficiency investments: Evidence from a German survey," Energy Policy, Elsevier, vol. 167(C).
    4. Tong Koecklin, Manuel & Fitiwi, Desta & de Carolis, Joseph F. & Curtis, John, 2020. "Renewable electricity generation and transmission network developments in light of public opposition: Insights from Ireland," Papers WP653, Economic and Social Research Institute (ESRI).
    5. Archana R. Panhalkar & Dharmpal D. Doye, 2020. "An approach of improving decision tree classifier using condensed informative data," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 47(4), pages 431-445, December.
    6. Michele Cincera, 2005. "Firms' productivity growth and R&D spillovers: An analysis of alternative technological proximity measures," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 14(8), pages 657-682.
    7. Horstmann, Felix, 2017. "Measuring the shopper's attitude toward the point of sale display: Scale development and validation," Journal of Retailing and Consumer Services, Elsevier, vol. 36(C), pages 112-123.
    8. Elizaveta Zinovyeva & Raphael C. G. Reule & Wolfgang Karl Hardle, 2021. "Understanding Smart Contracts: Hype or Hope?," Papers 2103.08447, arXiv.org.
    9. Dario Cottafava & Giulia Sonetti & Paolo Gambino & Andrea Tartaglino, 2018. "Explorative Multidimensional Analysis for Energy Efficiency: DataViz versus Clustering Algorithms," Energies, MDPI, vol. 11(5), pages 1-18, May.
    10. Chester Harris, 1955. "Characteristics of two measures of profile similarity," Psychometrika, Springer;The Psychometric Society, vol. 20(4), pages 289-297, December.
    11. Brian C Wesolowski & Alex Hofmann, 2016. "There’s More to Groove than Bass in Electronic Dance Music: Why Some People Won’t Dance to Techno," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-23, October.
    12. Quang Bao Le & Boubaker Dhehibi, 2019. "A Typology-Based Approach for Assessing Qualities and Determinants of Adoption of Sustainable Water Use Technologies in Coping with Context Diversity: The Case of Mechanized Raised-Bed Technology in E," Sustainability, MDPI, vol. 11(19), pages 1-21, September.
    13. Marrel, Amandine & Iooss, Bertrand, 2024. "Probabilistic surrogate modeling by Gaussian process: A new estimation algorithm for more robust prediction," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    14. Arévalo, Franklim & Barucca, Paolo & Téllez-León, Isela-Elizabeth & Rodríguez, William & Gage, Gerardo & Morales, Raúl, 2022. "Identifying clusters of anomalous payments in the salvadorian payment system," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 3(1).
    15. Shahzad, Murtuza & Alhoori, Hamed & Freedman, Reva & Rahman, Shaikh Abdul, 2022. "Quantifying the online long-term interest in research," Journal of Informetrics, Elsevier, vol. 16(2).
    16. Ermal Shpuza, 2023. "The shape and size of urban blocks," Environment and Planning B, , vol. 50(1), pages 24-43, January.
    17. Boztug, Yasemin & Reutterer, Thomas, 2008. "A combined approach for segment-specific market basket analysis," European Journal of Operational Research, Elsevier, vol. 187(1), pages 294-312, May.
    18. Martin Kueppers & Christian Perau & Marco Franken & Hans Joerg Heger & Matthias Huber & Michael Metzger & Stefan Niessen, 2020. "Data-Driven Regionalization of Decarbonized Energy Systems for Reflecting Their Changing Topologies in Planning and Optimization," Energies, MDPI, vol. 13(16), pages 1-15, August.
    19. Zhang, Yu & Li, Yanting & Zhang, Guangyao, 2020. "Short-term wind power forecasting approach based on Seq2Seq model using NWP data," Energy, Elsevier, vol. 213(C).
    20. João Antunes Rodrigues & Alexandre Martins & Mateus Mendes & José Torres Farinha & Ricardo J. G. Mateus & Antonio J. Marques Cardoso, 2022. "Automatic Risk Assessment for an Industrial Asset Using Unsupervised and Supervised Learning," Energies, MDPI, vol. 15(24), pages 1-17, December.

    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:24:y:2010:i:15:p:4469-4489. 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.