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Evaluation of machine learning methods to predict soil moisture constants with different combinations of soil input data for calcareous soils in a semi arid area

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  • Yamaç, Sevim Seda
  • Şeker, Cevdet
  • Negiş, Hamza

Abstract

This study evaluated the performance of deep learning (DL), artificial neural network (ANN) and k-nearest neighbour (kNN) models to estimate field capacity (FC) and permanent wilting point (PWP) using four combinations of soil data. The DL, ANN and kNN models are compared with the previous published pedotransfer functions (PTF). The data consist of 256 calcareous soil samples collected from Konya-Çumra plain, Turkey. The results demonstrated that the DL_a with inputs of soil texture components, bulk density, organic matter and lime contents, particle density and aggregate stability showed the best performances with coefficient of determination (R2) of 0.829, correlation coefficient (r) of 0.911, mean absolute error (MAE) of 0.027 and relative root mean square error (RRMSE) 9.397 % in FC estimation for calcareous soil samples. For the PWP estimation of calcareous soil samples, the kNN_b with soil texture components, bulk density, organic matter and lime content and particle density indicated the best performance with the value of R2 to 0.800, of r to 0.894, of MAE to 0.021 and RRMSE to 12.043 %. Lastly, the results showed that the DL, ANN and the kNN models perform better than the previously applied PTF for calcareous soils. Therefore, the DL model could be recommended for the estimation of FC when full soil data are available and the kNN model could be recommended for estimation of PWP with all combinations of soil data.

Suggested Citation

  • Yamaç, Sevim Seda & Şeker, Cevdet & Negiş, Hamza, 2020. "Evaluation of machine learning methods to predict soil moisture constants with different combinations of soil input data for calcareous soils in a semi arid area," Agricultural Water Management, Elsevier, vol. 234(C).
    • Handle: RePEc:eee:agiwat:v:234:y:2020:i:c:s037837741932356x
    DOI: 10.1016/j.agwat.2020.106121
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    References listed on IDEAS

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    1. Landeras, Gorka & Ortiz-Barredo, Amaia & López, Jose Javier, 2008. "Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country (Northern Spain)," Agricultural Water Management, Elsevier, vol. 95(5), pages 553-565, May.
    2. Horne, D.J. & Scotter, D.R., 2016. "The available water holding capacity of soils under pasture," Agricultural Water Management, Elsevier, vol. 177(C), pages 165-171.
    3. de Fraiture, Charlotte & Wichelns, Dennis, 2010. "Satisfying future water demands for agriculture," Agricultural Water Management, Elsevier, vol. 97(4), pages 502-511, April.
    4. Yamaç, Sevim Seda & Todorovic, Mladen, 2020. "Estimation of daily potato crop evapotranspiration using three different machine learning algorithms and four scenarios of available meteorological data," Agricultural Water Management, Elsevier, vol. 228(C).
    5. Givi, J. & Prasher, S.O. & Patel, R.M., 2004. "Evaluation of pedotransfer functions in predicting the soil water contents at field capacity and wilting point," Agricultural Water Management, Elsevier, vol. 70(2), pages 83-96, November.
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    Cited by:

    1. Hamza Negiş, 2023. "Using Models and Artificial Neural Networks to Predict Soil Compaction Based on Textural Properties of Soils under Agriculture," Agriculture, MDPI, vol. 14(1), pages 1-14, December.
    2. Priya Brata Bhoi & Veeresh S. Wali & Deepak Kumar Swain & Kalpana Sharma & Akash Kumar Bhoi & Manlio Bacco & Paolo Barsocchi, 2021. "Input Use Efficiency Management for Paddy Production Systems in India: A Machine Learning Approach," Agriculture, MDPI, vol. 11(9), pages 1-27, August.
    3. Yamaç, Sevim Seda, 2021. "Artificial intelligence methods reliably predict crop evapotranspiration with different combinations of meteorological data for sugar beet in a semiarid area," Agricultural Water Management, Elsevier, vol. 254(C).

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