Using self-adaptive evolutionary algorithm to improve the performance of an extreme learning machine for estimating soil temperature

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@Article{Nahvi:2016:CEA,
  author =       "Behnaz Nahvi and Jafar Habibi and Kasra Mohammadi and 
                 Shahaboddin Shamshirband and Othman Saleh Al Razgan",
  title =        "Using self-adaptive evolutionary algorithm to improve
                 the performance of an extreme learning machine for
                 estimating soil temperature",
  journal =      "Computers and Electronics in Agriculture",
  volume =       "124",
  pages =        "150--160",
  year =         "2016",
  ISSN =         "0168-1699",
  DOI =          "doi:10.1016/j.compag.2016.03.025",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0168169916300977",
  abstract =     "In this study, the self-adaptive evolutionary (SaE)
                 agent is employed to structure the contributing
                 elements to process the management of extreme learning
                 machine (ELM) architecture based on a logical
                 procedure. In fact, the SaE algorithm is used for
                 possibility of enhancing the performance of the ELM to
                 estimate daily soil temperature (ST) at 6 different
                 depths of 5, 10, 20, 30, 50 and 100 cm. In the
                 developed SaE-ELM model, the network hidden node
                 parameters of the ELM are optimized using SaE
                 algorithm. The precision of the SaE-ELM is then
                 compared with the ELM model. Daily weather data sets
                 including minimum, maximum and average air temperatures
                 (Tmin, Tmax and Tavg), atmospheric pressure (P) and
                 global solar radiation (RS) collected for two Iranian
                 stations of Bandar Abbas and Kerman with different
                 climate conditions have been used. After primary
                 evaluation, Tmin, Tmax and Tavg are considered as final
                 inputs for the ELM and SaE-ELM models due to their high
                 correlations with ST at all depths. The achieved
                 results for both stations reveal that both ELM and
                 SaE-ELM models offer desirable performance to estimate
                 daily ST at all depths; nevertheless, a slightly more
                 precision can be obtained by the SaE-ELM model. The
                 performance of the ELM and SaE-ELM models are verified
                 against genetic programming (GP) and artificial neural
                 network (ANN) models developed in this study. For
                 Bandar Abbass station, the obtained mean absolute bias
                 error (MABE) and correlation coefficient (R) for the
                 ELM model at different depths are in the range of
                 0.9116-1.5988 degree C and 0.9023-0.9840, respectively
                 while for the SaE-ELM model they are in the range of
                 0.8660-1.5338 degree C and 0.9084-0.9893, respectively.
                 In addition, for Kerman Station the attained MABE and
                 RMSE for the ELM model vary from 1.6567 to 2.4233
                 degree C and 0.8661 to 0.9789, respectively while for
                 the SaE-ELM model they vary from 1.5818 to 2.3422
                 degreeC and 0.8736 to 0.9831, respectively.",
  keywords =     "genetic algorithms, genetic programming, Soil
                 temperature, Extreme Learning Machine (ELM),
                 Self-Adaptive Evolutionary Extreme Learning Machine
                 (SaE-ELM), Estimation, Agent",
  notes =        "Department of Computer Engineering, Science and
                 Research Branch, Islamic Azad University, Tehran,
                 Iran",
}

Genetic Programming entries for Behnaz Nahvi Jafar Habibi Kasra Mohammadi Shahaboddin Shamshirband Othman Saleh Al Razgan

Citations