Publications by authors named "Kechao Li"
Article Synopsis
- The study focuses on identifying arsenic-contaminated areas in soil for better management and reclamation, addressing past shortcomings in machine learning approaches that ignore the rarity of contaminated samples.
- A new machine learning framework was developed to tackle imbalanced data through techniques like spectral preprocessing, dataset resampling, and model comparisons, achieving impressive performance metrics in classifying contaminated soil.
- Using the optimized model, researchers successfully predicted global areas at high risk of arsenic contamination, marking a significant advancement in soil contamination classification and management.
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.
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