Novel genetic-based negative correlation learning for estimating soil temperature

S. M.R. Kazemi, Behrouz Minaei Bidgoli, Shahaboddin Shamshirband, Seyed Mehdi Karimi, Mohammad Ali Ghorbani, Kwok Wing Chau, Reza Kazem Pour

Research output: Journal article publicationJournal articleAcademic researchpeer-review

30 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)506-516
Number of pages11
JournalEngineering Applications of Computational Fluid Mechanics
Issue number1
Publication statusPublished - 1 Jan 2018


  • Daily soil temperature
  • Estimation
  • Genetic algorithm
  • Negative correlation learning
  • Neural network ensemble model

ASJC Scopus subject areas

  • Computer Science(all)
  • Modelling and Simulation

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