Soft Computing Techniques for Appraisal of Potentially Toxic Elements from Jalandhar (Punjab), India

Vinod Kumar,Parveen Sihag,Shevita Pandita,Andrés Rodríguez-Seijo,Ali Keshavarzi

doi:10.3390/app11188362

Vinod Kumar, Parveen Sihag + Show 3 more

Open Access

https://doi.org/10.3390/app11188362

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Abstract

The contamination of potentially toxic elements (PTEs) in agricultural soils is a serious concern around the globe, and modelling approaches is imperative in order to determine the possible hazards linked with PTEs. These techniques accurately assess the PTEs in soil, which play a pivotal role in eliminating the weaknesses in determining PTEs in soils. This paper aims to predict the concentration of Cu, Co and Pb using neural networks (NNs) based on multilayer perceptron (MLP) and boosted regression trees (BT). Statistical performance estimation factors were rummage-sale to measure the performance of developed models. Comparison of the coefficient of correlation and root mean squared error suggest that MLP-established models perform better than BT-based models for predicting the concentration of Cu and Pb, whereas BT models perform better than MLP established models at predicting the concentration of Co.

Highlights

The aim of the study was to evaluate the effectiveness of multilayer perceptron (MLP)- and boosted regression trees (BT)-based models to predict the Co, Cu and Pb in the soil
The neural network models based upon MLP were executed by employing MATLAB
In the BT regression model of Cu and Pb, the root nodes of the regression tree spilt on phosphorus and pH, and both these variables are imperative in maintaining Cu and Pb [49]

Summary

Introduction

The diverse distribution of PTEs in soils, the widespread causes of contamination, and inappropriate monitoring knowledge are the key concerns for scientists in assessing the multi-source of PTEs in soils at a regional level; exploring suitable strategies to handle this problem is necessary imperative. Understanding all these aspects, modelling techniques are an imperative approach to assess the PTEs’ origin and their interface with soil properties [14,15]

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