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@ARTICLE{Fathololoumi:894126,
      author       = {Fathololoumi, S. and Vaezi, A. R. and Alavipanah, S. K. and
                      Montzka, C. and Ghorbani, A. and Biswas, A.},
      title        = {{S}oil temperature modeling using machine learning
                      techniques},
      journal      = {Desert},
      volume       = {25},
      number       = {2},
      issn         = {1026-1346},
      address      = {Tehran},
      publisher    = {Univ., International Research Center for Living with
                      Desert},
      reportid     = {FZJ-2021-03053},
      pages        = {185-199},
      year         = {2020},
      abstract     = {Soil Temperature (ST) is critical for environmental
                      applications. While its measurement is often difficult,
                      estimation from environmental parameters has shown promise.
                      The purpose of this study was to model ST in cold season
                      from soil properties and environmental parameters. This
                      study was conducted as a pot experiment in Ardebil, Iran.
                      Automatic thermal sensors were installed at 5 and 10 cm
                      depths. Besides, soil properties and environmental
                      parameters were determined based on field and laboratory
                      works. Machine learning methods including Multiple Linear
                      Regression (MLR), Artificial Neural Network (ANN), and
                      Adaptive Neuro-Fuzzy Interface System (ANFIS) were used for
                      modeling ST. The air temperature was observed as the most
                      effective factor in ST modeling. The relationship between
                      soil and air temperature was stronger at 5 cm depth compared
                      to 10 cm. The R2 between soil and air temperature was higher
                      in the absence of sunlight than in its presence. The
                      prediction of ANFIS (R2= 0.96 and MAPE= 10.5) was closer to
                      the observed ST values compared to the ANN (R2= 0.91 and
                      MAPE= 35) and MLR (R2= 0.57 and MAPE= 41). The results
                      revealed the advantage of ANFIS method for ST modeling. This
                      approach can be applied for soil depths and locations with
                      data gap.},
      cin          = {IBG-3},
      ddc          = {550},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {2173 - Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217)},
      pid          = {G:(DE-HGF)POF4-2173},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.22059/jdesert.2020.79256},
      url          = {https://juser.fz-juelich.de/record/894126},
}