###### 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 runmage-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.  
boosted regression trees (BT); ecological risk assessment; heavy metals; lead; multilayer perceptron (MLP); neural networks (NNs); soil carbon; phosphorus +
Footnote †: journal: Medical  
Different modelling techniques have emerged to help assess the origin of PTEs and their possible interaction with soil properties quickly and cost-effectively. Traditionally, geostatistical and mapping/GIS techniques have been used (e.g., [12; 13]); however, linear regression techniques, comprising principal component analysis-multiple linear regression (PCA-MLR), and neural networks have been successfully applied in recent years for soil mapping and contamination prediction, since there are simple methods to source identification of soil contaminants that require relatively few samples and reduced workload [15; 16; 17; 18; 19; 20]. Various researchers used these techniques to predict the concentration of PTEs; for example, [PERSON] et al. [21] predicted the As, Pb, Cr, Cd and Hg content, with total Cd content and pH as covariates and R\({}^{2}\) varies from 0.109 to 0.456. [PERSON] et al. [22] also projected the content of Fe and Ni. Different researchers all over the globe have studied further, numerous explorations using machine learning techniques in diverse areas of environmental engineering [23; 24; 25; 26; 27; 28; 29]. Machine learning techniques such as neural networks (NNs), dependent on multilayer perceptron (MLP) and boosted regression trees (BT) and stepwise regression, can predict non-linear associations amid diverse parameters. These techniques give information about the significance of variables in the method that may assist in controlling the PTEs contamination in soil environs and diminishes the health perils of PTE revelation [30; 31].  
In India, extensive fertilizers, pesticides, and rapid growth of industrial and urban growth development have a great impact on soil contamination, but there is no suitable dataset regarding agricultural roadside soils and standard methodologies about modelling strategies [2; 32; 33]. To achieve this goal, we have applied different modeling approaches such as neural networks (NNs) based on multilayer perceptron (MLP) and boosted regression trees (BT) to predict the concentration of Pb, Co and Cu in roadside agricultural soils in Punjab (India). The outcomes of this study will help in controlling and regulating the pollution of PTEs in soil.