Novel genetic-based negative correlation learning for estimating soil temperature

• Main Authors: S. M. R. Kazemi, Behrouz Minaei Bidgoli, Shahaboddin Shamshirband, Seyed Mehdi Karimi, Mohammad Ali Ghorbani, Kwok-wing Chau, Reza Kazem Pour
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2018-01-01
• Series: Engineering Applications of Computational Fluid Mechanics
• Subjects: Daily soil temperature; neural network ensemble model; negative correlation learning; genetic algorithm; estimation
• Online Access: http://dx.doi.org/10.1080/19942060.2018.1463871

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.