Application of neural network model for the prediction of chromium concentration in phytoremediated contaminated soils

Abstract

The assessment of 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. Many soil characteristics can change the availability of chromium (Cr) to plants in soils. However, accurate, rapid and simple predictive models of metal concentrations are still lacking in soil and plant analysis. In the present work a novel artificial neural network (ANN) model was developed as an alternative rapid and accurate tool for the prediction of Cr concentration in dwarf bean leaves grown in the laboratory on phytoremediated contaminated soils treated with different amendments. First, sixteen (4x4) soil samples were harvested from a phytoremediated contaminated site located in south-western France. Second, a series of measurements were performed on the soil samples.The inputs are the soil amendment, the soil pH, the soil electrical conductivity and the dissolved organic carbon of the soil and the output is the concentration of Cr in the dwarf bean 2 leaves. Third, an ANN model was developed and its performance was evaluated using a test data set and then applied to predict the exposition of the bean leaves to the Cr concentration versus the soil inputs. The performance of the ANN method was compared with the traditional multi linear regressions method using the training and test data sets. The results of this studyshow that the ANN model trained on experimental measurements can be successfully applied to the rapid prediction of plant exposition to Cr.

In Situ and Ex Situ Bioremediation of Heavy Metals: The Present Scenario

“…ANN is a type of computational simulation based on mathematical models which works on the basis of the neurological functions of the brain (Manahan, 2010). The Application of ANN involves mathematical modelling that can be used for the purpose of quantification of Cr concentration with the dwarf bean and the amount of phytoremediation being performed by the plant (Hattab et al, 2013). The mechanism of gene editing involves the process of manipulation of DNA with the help of engineered nucleases.…”

Prediction of the Impact of Land Use and Soil Type on Concentrations of Heavy Metals and Phthalates in Soil Based on Model Simulation

“…A critical aspect of predicting soil pollution is the determination of the concentrations of inorganic and organic pollutants. Since examining the relationship between different soil properties and pollutant concentrations is complex, time-consuming, and expensive, in recent years, emphasis has been placed on developing models that simulate these relationships [ 1 , 2 ]. The main objective of this study is to determine the possibilities of predicting the impact of land use and soil type on the concentrations of heavy metals (HMs) and phthalates (PAEs) using an artificial neural network (ANN) model.…”

A Comparison of Performance of Artificial Neural Networks for Prediction of Heavy Metals Concentration in Groundwater Resources of Toyserkan Plain

“…In the environmental forecasting context, recent experiments have reported that ANNs may offer a promising alternative for estimating heavy metals' concentration (25)(26)(27)(28)(29)(30) According to the knowledge of the authors of this study, there is not any published work in the literature related to comparison of two different neural networks (MLP and RBF), in forecasting of heavy metals concentration in groundwater resources of Toyserkan plain.…”