Hydrometeorological Parameters in Prediction of Soil Temperature by Means of Artificial Neural Network: Case Study in Wyoming

Abstract

The current study was undertaken to analyze the performance of three back-propagation training algorithms of artificial neural network (ANN) along with a multiple linear regression model (MLR) for transient simulation of short/midterm soil temperature (TS). Each ANN used a different type of learning algorithm (gradient descent, conjugate gradient, and Levenberg-Marquardt) as methods for daily and weekly TS prediction. The analysis was performed as a case study using three meteorological parameters [air temperature (TA), net radiation (NR), and relative humidity (RH)] and two hydrological variables [precipitation (P) and runoff (Q)] as input data for a region in Wyoming. Pearson and cross correlation analyses were applied to investigate the relationships between input and target values to determine several input combinations. The correlation analysis indicated that the meteorological parameters of TA and NR were reasonably correlated with TS. Based on the input combinations, 14 models were constructed for each of the daily/weekly MLR and ANNs. The accuracy of the predictions was evaluated by the RMS error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe coefficients between the measured and predicted TS values. The Levenberg-Marquardt ANN method was found to provide a more accurate prediction than the other two types of ANNs and the MLR model. Furthermore, it was found that weekly structure of ANNs performance surpasses that of any other daily structures. Although results illustrated that the soil temperature is a function of meteorological variables, the measured TS is also shown to be positively influenced by hydrological parameters—a fact that has not been pointed out by correlation analyses.