Fractional-order derivative coupled machine learning models for quantitative prediction of soil Ni content

Key Points

• Soil nickel content can be predicted using advanced data models, indicating significant findings in environmental science.
• The key metric identifies a strong correlation between fractional-order derivative modeling and prediction accuracy.
• Assessment using coupled machine learning models to analyze soil samples across diverse locations and conditions yielded promising results.
• This work highlights the need for innovative approaches in predicting soil contaminants, with broader implications for environmental management.