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Modeling soil temperatures at different depths by using three different neural computing techniques

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Abstract

This study compares the accuracy of three different neural computing techniques, multi-layer perceptron (MLP), radial basis neural networks (RBNN), and generalized regression neural networks (GRNN), in modeling soil temperatures (ST) at different depths. Climatic data of air temperature, wind speed, solar radiation, and relative humidity from Mersin Station, Turkey, were used as inputs to the models to estimate monthly ST values. In the first part of the study, the effect of each climatic variable on ST was investigated by using GRNN models. Air temperature was found to be the most effective variable in modeling monthly ST. In the second part of the study, the accuracy of GRNN models was compared with MLP, RBNN, and multiple linear regression (MLR) models. RBNN models were found to be better than the GRNN, MLP, and MLR models in estimating monthly ST at the depths of 5 and 10 cm while the MLR and GRNN models gave the best accuracy in the case of 50- and 100-cm depths, respectively. In the third part of the study, the effect of periodicity on the training, validation, and test accuracy of the applied models was investigated. The results indicated that the adding periodicity component significantly increase models’ accuracies in estimating monthly ST at different depths. Root mean square errors of the GRNN, MLP, RBNN, and MLR models were decreased by 19, 15, 19, and 15 % using periodicity in estimating monthly ST at 5-cm depth.

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Acknowledgment

This study was partly supported by The Turkish Academy of Sciences (TÜBA). The first author would like to thank TÜBA for their support of this study.

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Authors and Affiliations

  1. Civil Engineering Department, Canik Basari University, Samsun, Turkey

    Ozgur Kisi

  2. Engineering Faculty, Civil Engineering Department, Anadolu University, Eskisehir, Turkey

    Mustafa Tombul

  3. Department of Water Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

    Mohammad Zounemat Kermani

Authors
  1. Ozgur Kisi
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  2. Mustafa Tombul
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  3. Mohammad Zounemat Kermani
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Correspondence to Ozgur Kisi.

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Kisi, O., Tombul, M. & Kermani, M.Z. Modeling soil temperatures at different depths by using three different neural computing techniques. Theor Appl Climatol 121, 377–387 (2015). https://doi.org/10.1007/s00704-014-1232-x

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  • DOI: https://doi.org/10.1007/s00704-014-1232-x

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