Abstract
An artificial neural network (ANN) model was developed to predict monthly soil temperatures in the Aegean Region of Turkey. The model used soil temperature data measured by the Turkish State Meteorological Service between 2000 and 2006 at the Kütahya, Manisa, Uşak, Afyonkarahisar, İzmir, Aydın, Denizli and Muǧla meteorological stations at depths of 5, 10, 20, 50 and 100 cm below the surface. The monthly air temperature, depth and month of the year were used in the input layer of the artificial neural network model, while the monthly soil temperature was the target data in the output layer. A prediction model was developed using the MATLAB program and the results derived from the model were compared with measured values. The mean absolute error (MAE) for the derived results from the different depths at all stations ranged from 0.32–0.82°C, while the corresponding range for the validation data set was 0.27–0.76°C.
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Abbreviations
- ANN:
-
Artificial neural network
- D:
-
Soil depth (cm)
- F:
-
Sigmoid function
- FFNN:
-
Feed forward neural network
- GRNN:
-
Generalized regression neural networks
- k:
-
The number of source nodes
- LM:
-
The Levenberg Marquard
- m:
-
Measured value
- M:
-
Month of the year
- MAE:
-
Mean absolute error
- n:
-
Total number of data
- p:
-
Predicted value
- R:
-
Correlation coefficient
- RBNN:
-
Radial basis neural networks
- S:
-
Soil temperature (°C)
- T:
-
Air temperature (°C)
- TSMS:
-
The Turkish state meteorological service
- U:
-
Summation function
- X:
-
Inputs
- X min :
-
Minimal value
- X max :
-
Maximal value
- X N :
-
Normalized value
- X O :
-
Original value
- W:
-
Weight
- Y:
-
Output
- \({\varphi}\) :
-
Activation function
- \({\varphi(x)}\) :
-
Sigmoid logistic non-linear function
- θ :
-
Threshold
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Bilgili, M. Regional Assessment of Monthly Soil Temperatures in the Aegean Region of Turkey. Arab J Sci Eng 37, 765–775 (2012). https://doi.org/10.1007/s13369-012-0199-0
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DOI: https://doi.org/10.1007/s13369-012-0199-0