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Prediction of Soil Saturated Electrical Conductivity by Statistical Learning

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Information Management and Big Data (SIMBig 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1577))

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Abstract

The diagnosis of saline soils requires the analysis of electrical conductivity in saturated soil paste extract. Its analysis is expensive, tedious, and highly time-consuming, therefore, commercial laboratories analyze the aqueous extract in a 1:1 ratio and then transform the value into saturation extract using equations. The research aimed to calibrate a statistical learning method to predict the electrical conductivity adapted to Peruvian conditions. For this, we apply different models from highly interpretable to black-box, such as multiple linear model, generalized additive models, Bayesian additive regression tree, extreme gradient boosting trees, and neural networks. In general, the models with beast predictive power were neural network and extreme gradient boosting trees, and the beast interpretable was Bayesian additive regression trees. The generalized additive models present the best balance between prediction power and interpretability with low application on extremely salty soils.

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Author information

Authors and Affiliations

  1. Universidad Nacional Agraria La Molina, Lima, Peru

    Carlos Mestanza, Pedro Gutiérrez & Lorenzo Hurtado

  2. Pontificia Universidad Católica del Perú, Lima, Peru

    Miguel Chicchon & Cesar Beltrán

Authors
  1. Carlos Mestanza
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  2. Miguel Chicchon
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  3. Pedro Gutiérrez
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  4. Lorenzo Hurtado
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  5. Cesar Beltrán
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Corresponding author

Correspondence to Carlos Mestanza .

Editor information

Editors and Affiliations

  1. National Institutes of Health, Bethesda, MD, USA

    Juan Antonio Lossio-Ventura

  2. Visibilia, São Paulo, Brazil

    Jorge Valverde-Rebaza

  3. Universidad Peruana de Ciencias Aplicadas, Lima, Peru

    Eduardo Díaz

  4. ENSIIE and SAMOVAR, Evry, France

    Denisse Muñante

  5. The Open University, Milton Keynes, UK

    Carlos Gavidia-Calderon

  6. Federal University of São Carlos, São Carlos, Brazil

    Alan Demétrius Baria Valejo

  7. University of Engineering and Technology UTEC, Lima, Peru

    Hugo Alatrista-Salas

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Mestanza, C., Chicchon, M., Gutiérrez, P., Hurtado, L., Beltrán, C. (2022). Prediction of Soil Saturated Electrical Conductivity by Statistical Learning. In: Lossio-Ventura, J.A., et al. Information Management and Big Data. SIMBig 2021. Communications in Computer and Information Science, vol 1577. Springer, Cham. https://doi.org/10.1007/978-3-031-04447-2_27

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  • DOI: https://doi.org/10.1007/978-3-031-04447-2_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-04446-5

  • Online ISBN: 978-3-031-04447-2

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