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A computational methodology applied to optimize the performance of a river model under uncertainty conditions

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Abstract

Advances in computational science have made an explosion of computational models for analyzing and predicting the behavior of complex environmental systems possible, such as river models. Model accuracy is highly influenced by many sources of uncertainties, and one of these sources is parameter uncertainty. In this research, we present a search and optimization methodology to achieve a higher prediction quality of a computational system for calculating the translation of waves in rivers. Our proposal aims to achieve this goal using the least amount of computational resources. We address this issue by performing a two-phase optimization via simulation methodology. The first phase consists in a global exploration step over the entire search space. This phase identifies promising regions for optimization based on a neighborhood structure of the problem, using a Monte Carlo heuristic plus the K-means method. The second phase is a fine-grained approach that consists in seeking the best solution, either the optimum or a sub-optimum by performing a “reduced exhaustive search” in such promising regions. We achieve a speed-up of 20× when searching the best parameter settings in comparison with an exhaustive search in the whole space of candidates’ parameter settings. This acceleration is measured in terms of the number of simulations run required to find a solution. When using our methodology and parallel computing, we reduced from 11 to 0.5 days the complete time. We achieved a 22× gain, fulfilling the objective of reducing the use of computing resources.

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Notes

  1. In this site we can find the data needed for the validation methodology, i.e., https://alerta.ina.gob.ar/pub/mapa.

  2. INA. Hydraulic Laboratory. Study of hydraulic problems, through theoretical and experimental analysis, and simulation in physical and mathematical models. Available online at https://www.ina.gov.ar/lha/index.php.

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Acknowledgements

This research has been supported by the Agencia Estatal de Investigación (AEI), Spain and the Fondo Europeo de Desarrollo Regional (FEDER) UE, under contract PID2020-112496GB-I00 and partially funded by the Fundacion Escuelas Universitarias Gimbernat (EUG).

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Authors and Affiliations

  1. Science Institute, National University of General Sarmiento, Buenos Aires, Argentina

    Adriana Gaudiani

  2. Computer Architecture and Operating Systems Department, Universitat Autónoma de Barcelona, Bellaterra, Barcelona, Spain

    Alvaro Wong, Emilio Luque & Dolores Rexachs

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  1. Adriana Gaudiani
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  2. Alvaro Wong
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Correspondence to Adriana Gaudiani.

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Gaudiani, A., Wong, A., Luque, E. et al. A computational methodology applied to optimize the performance of a river model under uncertainty conditions. J Supercomput 79, 4737–4759 (2023). https://doi.org/10.1007/s11227-022-04816-6

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