Comparison of the performance of gradient boost, linear regression, decision tree, and voting algorithms to separate geochemical anomalies areas in the fractal environment

In this investigation, the Gradient Boosting (GB), Linear Regression (LR), Decision Tree (DT), and Voting algorithms were applied to predict the distribution pattern of Au geochemical data. Trace and indicator elements, including Mo, Cu, Pb, Zn, Ag, Ni, Co, Mn, Fe, and As, were used with these machine learning algorithms (MLAs) to predict Au concentration values in the Doostbigloo porphyry Cu-Au-Mo mineralization area. The performance of the models was evaluated using the Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) metrics. The proposed ensemble Voting algorithm outperformed the other models, yielding more accurate predictions according to both metrics. The predicted data from the GB, LR, DT, and Voting MLAs were modeled using the Concentration-Area fractal method, and Au geochemical anomalies were mapped. To compare and validate the results, factors such as the location of the mineral deposits, their surface extent, and mineralization trend were considered. The results indicate that integrating hybrid MLAs with fractal modeling significantly improves geochemical prospectivity mapping. Among the four models, three (DT, GB, Voting) accurately identified both mineral deposits. The LR model, however, only identified Deposit I (central), and its mineralization trend diverged from the field data. The GB and Voting models produced similar results, with their final maps derived from fractal modeling showing the same anomalous areas. The anomaly boundaries identified by these two models are consistent with the two known reserves in the region. The results and plots related to prediction indicators and error rates for these two models also show high similarity, with lower error rates than the other models. Notably, the Voting model demonstrated superior performance in accurately delineating mineral deposit locations and identifying realistic mineralization trends while minimizing false anomalies. • Application of voting learning algorithm for prediction of Au geochemical pattern. • The outputs of MLAs were modeled using C-A fractal method. • Comparing the performance of variety MLAs for geochemical prospecting. • Introduce the location of mineralization trend by hybrid model of MLA-Fractal.