Predictive Machine Learning Model to Assess the Adsorption Efficiency of Biochar-Heavy Metals for Effective Remediation of Soil-Plant Environment.

ABSTRACT: Biochar is crucial for agricultural output and plays a significant role in effectively eliminating heavy metals (HMs) from the soil, which is essential for maintaining a soil-plant environment. This work aimed to assess machine learning models to analyze the impact of soil parameters on the transformation of HMs in biochar-soil-plant environments, considering the intricate non-linear relationships involved. A total of 211 datasets from pot or field experiments were evaluated. Fourteen factors were taken into account to assess the efficiency and bioavailability of HM-biochar amendment immobilization. Four predictive models, namely linear regression (LR), partial least squares (PLS), support vector regression (SVR), and random forest (RF), were compared to predict the immobilization efficien

SUBMITTER: Li X 

PROVIDER: S-EPMC11359540 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature