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Evaluation of deep machine learning-based models of soil cumulative infiltration

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Abstract

Infiltration is the process by which water enters the soil, and it plays a significant role in the hydrologic cycle. Direct measurement of infiltration is time consuming; however, empirical and physical models are inaccurate. In this study, we compared the results of a deep learning-based convolutional neural network (CNN) algorithm with those of models based on standalone support vector regression (SVR) and group method of data handling (GMDH) algorithms. We also tested a hybridized SVR and GMDH-based model enhanced by three metaheuristic algorithms: gray wolf optimization (GWO), bat algorithm (BA), and particle swarm optimization (PSO). Measured variables including measurement time; sand, clay and silt contents; bulk density; and soil moisture content were used as model inputs to predict cumulative infiltration as an output. Finally, models were evaluated using the Pearson correlation coefficient, root mean square error, mean absolute error, Nash–Sutcliffe efficiency, percentage of bias, and relative error. Weighting of the model input parameters/variables demonstrated that time was the most effective variable for predicting cumulative infiltration, whereas the most powerful parameter input combination required all seven variables. The prediction model evaluation results showed that the hybridized models improved the predictive capability of the standalone models.

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Funding

Khabat Khosravi was partially supported by a grant from the Ferdowsi University of Mashhad (No. FUM-1399102452809).

Author information

Authors and Affiliations

  1. Department of Range and Watershed Management, Lorestan University, Khorramabad, Iran

    Alireza Sepahvand

  2. Department of Watershed Management, Ferdowsi University of Mashhad, Mashhad, Iran

    Ali Golkarian & Khabat Khosravi

  3. School of Environmental and Geographical Sciences, University of Nottingham Malaysia, Selangor, Malaysia

    Lawal Billa

  4. Institute of Geographic Sciences and Natural Resources, Chinese Academy of Sciences, Beijing, China

    Kaiwen Wang

  5. Geoscience Platform Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro Yuseong-gu, Daejeon, 34132, Korea

    Fatemeh Rezaie

  6. Department of Geophysical Exploration, Korea University of Science and Technology, 217 Gajeong-roYuseong-gu, Daejeon, 305-350, Republic of Korea

    Fatemeh Rezaie

  7. Department of Computer Engineering, Faculty of Valiasr, Tehran Branch, Technical and Vocational University (TVU), Tehran, Iran

    Somayeh Panahi

  8. Department of Civil Engineering, University of Tabriz, Tabriz, Iran

    Saeed Samadianfard

  9. Department of Earth and Environment, Florida International University, Miami, FL, USA

    Khabat Khosravi

Authors
  1. Alireza Sepahvand
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  2. Ali Golkarian
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  3. Lawal Billa
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  4. Kaiwen Wang
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  5. Fatemeh Rezaie
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  6. Somayeh Panahi
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  7. Saeed Samadianfard
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  8. Khabat Khosravi
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Contributions

Conceptualization, K.K; and A.G.; methodology, K.K; A.G; and F.R.; software, F.R; formal analysis, K.K and F.R.; writing—original draft preparation, K.K; K.W; F.R.; L.B, S.P and S.S.; Data curation: A.S; writing—review and editing, K.K; A.G; L.B. supervision, A.G. All authors have read and agreed to the published version of the manuscript.

Corresponding authors

Correspondence to Ali Golkarian or Khabat Khosravi.

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Communicated by H. Babaie

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Sepahvand, A., Golkarian, A., Billa, L. et al. Evaluation of deep machine learning-based models of soil cumulative infiltration. Earth Sci Inform 15, 1861–1877 (2022). https://doi.org/10.1007/s12145-022-00830-7

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  • DOI: https://doi.org/10.1007/s12145-022-00830-7

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