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The influence of parameter fitting methods on model structure selection in automated modeling of aquatic ecosystems

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  • Čerepnalkoski, Darko
  • Taškova, Katerina
  • Todorovski, Ljupčo
  • Atanasova, Nataša
  • Džeroski, Sašo

Abstract

Modeling dynamical systems involves two subtasks: structure identification and parameter estimation. ProBMoT is a tool for automated modeling of dynamical systems that addresses both tasks simultaneously. It takes into account domain knowledge formalized as templates for components of the process-based models: entities and processes. Taking a conceptual model of the system, the library of domain knowledge, and measurements of a particular dynamical system, it identifies both the structure and numerical parameters of the appropriate process-based model. ProBMoT has two main components corresponding to the two subtasks of modeling. The first component is concerned with generating candidate model structures that adhere to the conceptual model specified as input. The second subsystem uses the measured data to find suitable values for the constant parameters of a given model by using parameter estimation methods. ProBMoT uses model error to rank model structures and select the one that fits measured data best.

Suggested Citation

  • Čerepnalkoski, Darko & Taškova, Katerina & Todorovski, Ljupčo & Atanasova, Nataša & Džeroski, Sašo, 2012. "The influence of parameter fitting methods on model structure selection in automated modeling of aquatic ecosystems," Ecological Modelling, Elsevier, vol. 245(C), pages 136-165.
    • Handle: RePEc:eee:ecomod:v:245:y:2012:i:c:p:136-165
    DOI: 10.1016/j.ecolmodel.2012.06.001
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    References listed on IDEAS

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    1. Atanasova, Nataša & Todorovski, Ljupčo & Džeroski, Sašo & Kompare, Boris, 2008. "Application of automated model discovery from data and expert knowledge to a real-world domain: Lake Glumsø," Ecological Modelling, Elsevier, vol. 212(1), pages 92-98.
    2. Tashkova, Katerina & Šilc, Jurij & Atanasova, Nataša & Džeroski, Sašo, 2012. "Parameter estimation in a nonlinear dynamic model of an aquatic ecosystem with meta-heuristic optimization," Ecological Modelling, Elsevier, vol. 226(C), pages 36-61.
    3. Gilboa, Yael & Friedler, Eran & Gal, Gideon, 2009. "Adapting empirical equations to Lake Kinneret data by using three calibration methods," Ecological Modelling, Elsevier, vol. 220(23), pages 3291-3300.
    4. Abbas Afshar & Hamideh Kazemi & Motahareh Saadatpour, 2011. "Particle Swarm Optimization for Automatic Calibration of Large Scale Water Quality Model (CE-QUAL-W2): Application to Karkheh Reservoir, Iran," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(10), pages 2613-2632, August.
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    Cited by:

    1. Nikola Simidjievski & Ljupčo Todorovski & Sašo Džeroski, 2016. "Modeling Dynamic Systems with Efficient Ensembles of Process-Based Models," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-27, April.
    2. Larocque, Guy R. & Bhatti, Jagtar & Arsenault, André, 2015. "Integrated modelling software platform development for effective use of ecosystem models," Ecological Modelling, Elsevier, vol. 306(C), pages 318-325.
    3. Simidjievski, Nikola & Todorovski, Ljupčo & Džeroski, Sašo, 2015. "Learning ensembles of population dynamics models and their application to modelling aquatic ecosystems," Ecological Modelling, Elsevier, vol. 306(C), pages 305-317.
    4. Larocque, Guy R. & Bhatti, Jagtar & Arsenault, André, 2014. "Integrated modelling software platform development for effective use of ecosystem models," Ecological Modelling, Elsevier, vol. 288(C), pages 195-202.

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