USING ARTIFICIAL NEURAL NETWORK TECHNIQUE FOR THE ESTIMATION OF CD CONCENTRATION IN CONTAMINATED SOILS
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Published in Vol. 1 (2026) of International Journal of Information Science & Engineering.
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Abstract
The aim of this paper is to design artificial neural network as an alternative accurate tool to estimate
concentration of Cadmium in contaminated soils for any depth and time. First, fifty soil samples were harvested
from a phytoremediated contaminated site located in Qanat Aljaeesh in Baghdad city in Iraq. Second, a series of
measurements were performed on the soil samples.
The inputs are the soil depth, the time, and the soil parameters but the output is the concentration of Cu in the
soil for depth x and time t.
Third, design an ANN and its performance was evaluated using a test data set and then applied to estimate the
concentration of Cadmium. The performance of the ANN technique was compared with the traditional laboratory
inspecting using the training and test data sets. The results of this work show that the ANN technique trained on
experimental measurements can be successfully applied to the rapid estimation of Cadmium concentration.
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Copyright (c) 2026 International Journal of Information Science & Engineering
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