The Estimation of Monthly Mean Soil Temperature at Different Depths in Sivas Province, Turkey by Artificial Neural Networks
Abstract
In this study, soil temperature of Sivas province was estimated by the
artificial neural networks (ANNs) method using data obtained from five
different meteorological measurement stations situated in provincial
borders. Nineteen years of (2000–2018) monthly mean air temperature
data obtained from five different soil depths (5, 10, 20, 50 and 100 cm)
was used for ANN analysis. Predicted and measured soil temperatures
were strongly correlated with determination coefficient (R2) values ranging
between 0.9767 and 0.9941. Mean Absolute Error (MAE) ranged
from 0.532°C to 1.381°C, while Mean Absolute Percentage Error (MAPE)
ranged from 5.692% to 16.263% and Root Mean Squared Error (RMSE)
ranged between 0.694°C and 1.666°C. It was found that the predicted
values are in good agreement with the measured data. However, there
was a tendency to underestimate the soil temperature.