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

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