Using self-adaptive evolutionary algorithm to improve the performance of an extreme learning machine for estimating soil temperature
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @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