Modeling soil temperatures at different depths by using three different neural computing techniques

Abstract

This study compares the accuracy of three different neural computing techniques, multi-layer perceptron (MLP), radial basis neural networks (RBNN), and generalized regression neural networks (GRNN), in modeling soil temperatures (ST) at different depths. Climatic data of air temperature, wind speed, solar radiation, and relative humidity from Mersin Station, Turkey, were used as inputs to the models to estimate monthly ST values. In the first part of the study, the effect of each climatic variable on ST was investigated by using GRNN models. Air temperature was found to be the most effective variable in modeling monthly ST. In the second part of the study, the accuracy of GRNN models was compared with MLP, RBNN, and multiple linear regression (MLR) models. RBNN models were found to be better than the GRNN, MLP, and MLR models in estimating monthly ST at the depths of 5 and 10 cm while the MLR and GRNN models gave the best accuracy in the case of 50- and 100-cm depths, respectively. In the third part of the study, the effect of periodicity on the training, validation, and test accuracy of the applied models was investigated. The results indicated that the adding periodicity component significantly increase models' accuracies in estimating monthly ST at different depths. Root mean square errors of the GRNN, MLP, RBNN, and MLR models were decreased by 19, 15, 19, and 15 % using periodicity in estimating monthly ST at 5-cm depth.