Machine learning-based prediction of toxic metals concentration in an acid mine drainage environment, northern Tunisia

Abstract

In northern Tunisia, Sidi Driss sulfide ore valorization had produced a large waste amount. The long tailings exposure period and in situ minerals interactions produced an acid mine drainage (AMD) which contributed to a strong increase in the mobility and migration of huge heavy metal (HM) quantities to the surrounding soils. In this work, the soil mineral proportions, grain sizes, physicochemical properties, SO₄^2- and S contents, and Machine Learning (ML) algorithms such as the Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN) models were used to predict the soil HM quantities transferred from Sidi-Driss mine drainage to surrounding soils. The results showed that the HM concentrations had significantly increased with the increase of decomposition and oxidation of galena, marcasite, pyrite, and sphalerite-marcasite and Fe-oxide-hydroxides quantities and the sulfate dissolution (marked with SO₄^2- ions increase) that produced the decreased soil pH. Compared to SVM, and ANN models outputs, the RF model that revealed higher R²_val, RPD, RPIQ, and lower error indices had satisfactorily predicted the soil HM accumulation coming from the AMD environment.

Keywords: Acid mine drainage (AMD); Heavy metals (HMs); Machine Learning (ML); Mine tailings; Mineralogical compositions.