Soil temperature (TS) strongly influences a wide range of biotic and abiotic processes. As an alternative to direct measurement, indirect determination of TS from meteorological parameters has been the focus of attention of environmental researchers. The main purpose of this study was to estimate daily TS at six depths (5, 10, 20, 30, 50 and 100 cm) by using a multilayer perceptron (MLP) artificial neural network (ANN) model and a multivariate linear regression (MLR) method in an arid region of Iran. Mean daily meteorological parameters including air temperature (Ta), solar radiation (RS), relative humidity (RH) and precipitation (P) were used as input data to the ANN and MLR models. The model results of the MLR model were compared to those of ANN. The accuracy of the predictions was evaluated by the correlation coefficient (r), the root mean-square error (RMSE) and the mean absolute error (MAE) between the measured and predicted TS values. The results showed that the ANN method forecasts were superior to the corresponding values obtained by the MLR model. The regression analysis indicated that Ta, RH, RS and P were reasonably correlated with TS at various depths, but the most effective parameters influencing TS at different depths were Ta and RH.
