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

• Main Authors: Vinod Kumar, Parveen Sihag, Ali Keshavarzi, Shevita Pandita, Andrés Rodríguez-Seijo
• Format: Article
• Language: English
• Published: MDPI AG 2021-09-01
• Series: Applied Sciences
• Subjects: boosted regression trees (BT); ecological risk assessment; heavy metals; lead; multilayer perceptron (MLP); neural networks (NNs)
• Online Access: https://www.mdpi.com/2076-3417/11/18/8362

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.