Multi-depth daily soil temperature modeling: meteorological variables or time series?

This study presents the first-time application of a novel emotional neural network (ENN) for soil temperature modeling. Two scenarios were considered for soil temperature forecasting: (i) meteorological variable-based modeling and (ii) time series-based modeling. For the first scenario, meteorological variables, including average air temperature, average wind speed, and total solar radiation, were considered as the inputs of a predictor model, while for the second one, the time delays of the soil temperature time series were considered as input(s) for forecasting future time-step soil temperature profiles. The multi-depth daily soil temperature datasets from Springfield and Champaign stations, located in Illinois, USA, were collected at the 10-cm and 20-cm depths to evaluate the proposed model. Moreover, the proposed ENN model was compared with other popular modeling techniques, including generic programming (GA), least square support vector machine (LSSVM), and multivariate adaptive regression splines (MARS). These case studies indicate the superior performance of the ENN compared to other popular modeling techniques for soil temperature applications. The mean relative error of scenario 2 was in the 5-7% range, while it was more than 40% for scenario 1.

Automated summary

This study introduces a novel emotional neural network (ENN) for soil temperature modeling. Two different scenarios were considered for forecasting soil temperature, and the proposed ENN model outperformed other modeling techniques in terms of accuracy.