ABSTRACT A genetic-based neural network ensemble (GNNE) is applied for estimation of daily soil temperatures (DST) at distinct depths. A sequential genetic-based negative correlation learning algorithm (SGNCL) is adopted to… Click to show full abstract
ABSTRACT A genetic-based neural network ensemble (GNNE) is applied for estimation of daily soil temperatures (DST) at distinct depths. A sequential genetic-based negative correlation learning algorithm (SGNCL) is adopted to train the GNNE parameters. CLMS algorithm is used to achieve the optimum weights of components. Recorded data for two different stations located in Iran are used for the development of the GNNE models. Furthermore, the GNNE predictions are compared with the existing machine-learning models. The results demonstrate that GNNE outperforms other methods for the prediction of DSTs.
               
Click one of the above tabs to view related content.