The paper presents a comparison of modern approaches to the interpolation of the spatial distribution of the chemical elements in the upper soil layer by the example of heavy metals chromium (Cr) and copper (Cu). Spots with an abnormally high Cr content were found on the examined area. Copper, on the contrary, was distributed evenly. The study is based on the data from soil screening in Tarko-Sale, Russia. For the prediction were selected models based on artificial neural networks (multilayer perceptron (MLP)), random forest (RF) algorithms, and the hybrid method in which MLP is used as a classifier (tree) (RMLPF). Models have been implemented in MATLAB. Approaches involving artificial neural networks (MLP and RMLPF) turned out to be more precise for abnormally distributed Cr. Models based on the RF algorithm are more precise for uniformly distributed Cu. In general, the proposed RMLPF model is showed the best results.