مختصر البحث:
Estimating groundwater salinity is important
for the use of groundwater resources for irrigation purposes
and provides a suitable guide for the management
of groundwater. In this study, the artificial neural networks
(ANNs) were adopted to estima…
Estimating groundwater salinity is important
for the use of groundwater resources for irrigation purposes
and provides a suitable guide for the management
of groundwater. In this study, the artificial neural networks
(ANNs) were adopted to estimate the salinity of
groundwater identified by total dissolved solids (TDS),
sodium adsorption ratio (SAR),andsodium(Na
) percent,
using electrical conductivity, magnesium (Mg
+
2+
), calcium
(Ca
2+
),potassium(K
+
), and potential of hydrogen (pH) as
input elements. Samples of groundwater were brought
from 51 wells situated in the plateau of Najaf–Kerbala provinces.
The network structure was designed as 6-4-3and
adopted the default scaled conjugate gradient algorithm
for training using SPSS V24 software. It was observed that
the proposed model with four neurons was exact in estimating
the irrigation salinity. It has shown a suitable
agreement between experimental and ANN values of irrigation
salinity indices for training and testing datasets
based on statistical indicators of the relative mean error
and determination coefficient R
2
between ANN outputs and
experimentaldata.TDS,SAR,andNapercentpredicted
output tracked the measured data with an R
of 0.96,
0.97, and 0.96 with relative error of 0.038, 0.014, and
0.021, respectively,fortesting,andR
2
of 0.95, 0.96, and
0.96 with relative error of 0.053, 0.065, and 0.133, respectively,
for training. This is an indication that the designed
network was satisfactory. The model could be utilized for
