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Novel approach for soil classification using machine learning methods

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Abstract

In this study, we have proposed a new classification method for determining different soil classes based on three machine learning approaches, namely: support vector classification (SVC), multilayer perceptron (MLP), and random forest (RF) models. For the development of models, we have used a database of 4888 soil samples obtained from Vietnam projects. In the model’s study, 15 soil properties factors (variables) have been selected as input parameters for classifying soil samples into 5 soil classes: lean clay (CL), elastic silt (MH), fat clay (CH), clayey sand (SC), and silt (ML). To evaluate and analyze the results quantitatively and qualitatively, various methods such as learning curve (time and number of training samples), confusion matrix, and several statistical metrics such as precision, recall, accuracy, and F1-score were used. Results indicated that performance of all the three models (average accuracy score = 0.968) is good but of the SVC model (accuracy score = 0.984) is best in accurate classification of soils.

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Acknowledgements

This study was funded by the Ministry of Education and Training under grant number B2020-GHA-03, chaired by the University of Transportation. The authors would like to thank the support of the Department of Science, Technology, and Environment (Ministry of Education and Training), the University of Transport and Communications, and other agencies for providing data used in this research.

Author information

Authors and Affiliations

  1. University of Transport and Communications, Hanoi, 100000, Vietnam

    Manh Duc Nguyen & An Ho Sy

  2. Department of Civil Engineering, Transilvania University of Brasov, 5, Turnului Str, 00152, Brasov, Romania

    Romulus Costache

  3. Danube Delta National Institute for Research and Development, 165 Babadag Street, 820112, Tulcea, Romania

    Romulus Costache

  4. Department of Geography and Urban Planning, Tabriz Branch, Islamic Azad University, Tabriz, Iran

    Hassan Ahmadzadeh

  5. University of Transport Technology, Hanoi, 100000, Vietnam

    Hiep Van Le & Binh Thai Pham

  6. DDG (R), Geological Survey of India, Gandhinagar, 382010, India

    Indra Prakash

Authors
  1. Manh Duc Nguyen
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  2. Romulus Costache
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  3. An Ho Sy
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  4. Hassan Ahmadzadeh
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  5. Hiep Van Le
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  6. Indra Prakash
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  7. Binh Thai Pham
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Contributions

Manh Duc Nguyen: conceptualization, methodology, software, writing—review and editing, validation, and supervision. Romulus Costache: conceptualization, methodology, software, writing—review and editing, validation, and supervision. An Ho Sy: data curation, writing—original draft, software, and validation. Peyman Yariyan: data curation, writing—original draft, software, and validation. Hassan Ahmadzadeh: data curation, writing—original draft, software, and validation. Hiep Van Le: data curation, writing—original draft, software, and validation. Indra Prakash: writing—review and editing, validation, and supervision. Binh Thai Pham: conceptualization, methodology, software, writing—review and editing, validation, and supervision.

Corresponding author

Correspondence to Binh Thai Pham.

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Nguyen, M.D., Costache, R., Sy, A.H. et al. Novel approach for soil classification using machine learning methods. Bull Eng Geol Environ 81, 468 (2022). https://doi.org/10.1007/s10064-022-02967-7

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  • DOI: https://doi.org/10.1007/s10064-022-02967-7

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