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International Journal of Environment Science and Technology
Center for Environment and Energy Research and Studies (CEERS)
ISSN: 1735-1472 EISSN: 1735-1472
Vol. 11, No. 6, 2014, pp. 1781-1786
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Bioline Code: st14174
Full paper language: English
Document type: Short Communication
Document available free of charge
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International Journal of Environment Science and Technology, Vol. 11, No. 6, 2014, pp. 1781-1786
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Prediction of copper and chromium concentrations in bean leaves based on an artificial neural network model
Hattab, N. & Hambli, R.
Abstract
The assessment of copper and chromium concentrations
in plants requires the quantification of a large
number of soil factors that affect their potential availability
and subsequent toxicity and a mathematical model that
predicts their relative concentrations in plants. While many
soil characteristics have been implicated as altering copper
and chromium availability to plants in soil, accurate, rapid
and simple predictive models of metal concentrations are
still lacking for soil and plant analysis. In the current study,
an artificial neural network model was developed and
applied to predict the exposure of bean leaves (BL) to high
concentrations of copper and chromium versus some
selected soil properties (pH, soil electrical conductivity and
dissolved organic carbon). A series of measurements was
performed on soil samples to assess the variation of copper
and chromium concentrations in BL versus the soil inputs.
The performance of the artificial neural network model was
then evaluated using a test data set and applied to predict
the exposure of the BL to the metal concentration versus
the soil inputs. Correlation coefficients of 0.99981 and
0.9979 for Cu and 0.99979 and 0.9975 for Cr between the
measured and artificial neural networks predicted values
were found, respectively, during the testing and validation
procedures. Results showed that the artificial neural network
model can be successfully applied to the rapid and
accurate prediction of copper and chromium concentrations
in BL.
Keywords
Artificial neural networks; Soil; Copper/chromium concentrations; Bean leaves
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