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Daily Rainfall-Runoff Modeling at Watershed Scale: A Comparison Between Physically-Based and Data-Driven Models

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Computational Science and Its Applications – ICCSA 2021 (ICCSA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12955))

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Abstract

In the last decades, data-driven (DD) machine-learning models have been rapidly developed and widely applied to solve hydrologic problems. To explore DD approaches’ capability in rainfall-runoff modeling compared to knowledge-driven models, we conducted a thorough comparison between Soil & Water Assessment Tool (SWAT) and Random Forest (RF) models. They were implemented to simulate the daily surface runoff at Santa Lucía Chico watershed in Uruguay. Aiming at making a fair comparison, the same input time series for RF and SWAT models were considered. Both approaches are able to represent the daily surface runoff adequately. The RF model shows a higher accuracy for calibration/training, while the SWAT model yields better results for validation/testing, indicating that the latter has a better generalization capacity. Furthermore, RF outperforms SWAT in terms of computational time needed for a proper calibration/training. Strategies to improve RF performance and interpretability should include feature selection, feature engineering and a more sophisticated sensitivity analysis technique.

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Author information

Authors and Affiliations

  1. Universidad de la República, 11300, Montevideo, Uruguay

    Federico Vilaseca, Alberto Castro, Christian Chreties & Angela Gorgoglione

Authors
  1. Federico Vilaseca
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  2. Alberto Castro
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  3. Christian Chreties
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  4. Angela Gorgoglione
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Corresponding author

Correspondence to Federico Vilaseca .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Monash University, Clayton, VIC, Australia

    David Taniar

  7. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

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Vilaseca, F., Castro, A., Chreties, C., Gorgoglione, A. (2021). Daily Rainfall-Runoff Modeling at Watershed Scale: A Comparison Between Physically-Based and Data-Driven Models. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12955. Springer, Cham. https://doi.org/10.1007/978-3-030-87007-2_2

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  • DOI: https://doi.org/10.1007/978-3-030-87007-2_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87006-5

  • Online ISBN: 978-3-030-87007-2

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