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Modeling Dynamic Systems with Efficient Ensembles of Process-Based Models

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  • Nikola Simidjievski
  • Ljupčo Todorovski
  • Sašo Džeroski

Abstract

Ensembles are a well established machine learning paradigm, leading to accurate and robust models, predominantly applied to predictive modeling tasks. Ensemble models comprise a finite set of diverse predictive models whose combined output is expected to yield an improved predictive performance as compared to an individual model. In this paper, we propose a new method for learning ensembles of process-based models of dynamic systems. The process-based modeling paradigm employs domain-specific knowledge to automatically learn models of dynamic systems from time-series observational data. Previous work has shown that ensembles based on sampling observational data (i.e., bagging and boosting), significantly improve predictive performance of process-based models. However, this improvement comes at the cost of a substantial increase of the computational time needed for learning. To address this problem, the paper proposes a method that aims at efficiently learning ensembles of process-based models, while maintaining their accurate long-term predictive performance. This is achieved by constructing ensembles with sampling domain-specific knowledge instead of sampling data. We apply the proposed method to and evaluate its performance on a set of problems of automated predictive modeling in three lake ecosystems using a library of process-based knowledge for modeling population dynamics. The experimental results identify the optimal design decisions regarding the learning algorithm. The results also show that the proposed ensembles yield significantly more accurate predictions of population dynamics as compared to individual process-based models. Finally, while their predictive performance is comparable to the one of ensembles obtained with the state-of-the-art methods of bagging and boosting, they are substantially more efficient.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pone00:0153507
    DOI: 10.1371/journal.pone.0153507
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    References listed on IDEAS

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    1. 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.
    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. Knudby, Anders & Brenning, Alexander & LeDrew, Ellsworth, 2010. "New approaches to modelling fish–habitat relationships," Ecological Modelling, Elsevier, vol. 221(3), pages 503-511.
    4. 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.
    5. Marit Ackermann & Mathieu Clément-Ziza & Jacob J Michaelson & Andreas Beyer, 2012. "Teamwork: Improved eQTL Mapping Using Combinations of Machine Learning Methods," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-8, July.
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    Cited by:

    1. Nakayama, Tadanobu & Wang, Qinxue & Okadera, Tomohiro, 2021. "Evaluation of spatio-temporal variations in water availability using a process-based eco-hydrology model in arid and semi-arid regions of Mongolia," Ecological Modelling, Elsevier, vol. 440(C).

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