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.