Artificial intelligence-based prediction model for the elemental occurrence form of tailings and mine wastes.

With the advent of the second industrial revolution, mining and metallurgical processes generate large volumes of tailings and mine wastes (TMW), which worsens global environmental pollution. Studying the occurrence of metal and metalloid elements in TMW is an effective approach to evaluating pollution linked to TMW. However, traditional laboratory-based measurements are complicated and time-consuming; thus, an empirical method is urgently needed that can rapidly and accurately determine elemental occurrence forms. In this study, a model combining Bayesian optimization and random forest (RF) approaches was proposed to predict TMW occurrence forms. To build the RF model, a dataset of 2376 samples was obtained, with mineral composition, elemental properties, and total concentration composition used as inputs and the percentage of occurrence forms as the model output. The correlation coefficient (R), coefficient of determination, mean absolute error, root mean squared error, and root mean squared logarithmic error metrics were used for model evaluation. After Bayesian optimization, the optimal RF model achieved accurate predictive performance, with R values of 0.99 and 0.965 on the training and test sets, respectively. The feature significance was analyzed using feature importance and Shapley additive explanatory values, which revealed that the electronegativity and total concentration of the elements were the two features with the greatest influence on the model output. As the electronegativity of an element increases, its corresponding residual fraction content gradually decreases. This is because the solubility typically increases with the solvent's polarity and electronegativity. Overall, this study proposes an RF model based on the nature of TMW that can rapidly and accurately predict the percentage values of metal and metalloid element occurrence forms in TMW. This method can minimize testing time requirements and help to assess TMW pollution risks, as well as further promote safe TMW management and recycling.

Improved classification of soil As contamination at continental scale: resolving class imbalances using machine learning approach.

The identification of arsenic (As)-contaminated areas is an important prerequisite for soil management and reclamation. Although previous studies have attempted to identify soil As contamination via machine learning (ML) methods combined with soil spectroscopy, they have ignored the rarity of As-contaminated soil samples, leading to an imbalanced learning problem. A novel ML framework was thus designed herein to solve the imbalance issue in identifying soil As contamination from soil visible and near-infrared spectra. Spectral preprocessing, imbalanced dataset resampling, and model comparisons were combined in the ML framework, and the optimal combination was selected based on the recall. In addition, Bayesian optimization was used to tune the model hyperparameters. The optimized model achieved recall, area under the curve, and balanced accuracy values of 0.83, 0.88, and 0.79, respectively, on the testing set. The recall was further improved to 0.87 with the threshold adjustment, indicating the model's excellent performance and generalization capability in classifying As-contaminated soil samples. The optimal model was applied to a global soil spectral dataset to predict areas at a high risk of soil As contamination on a global scale. The ML framework established in this study represents a milestone in the classification of soil As contamination and can serve as a valuable reference for contamination management in soil science.