Chitosan-Based Flocculant Heavy Metal Removal Prediction Using Machine Learning Models.

ABSTRACT: The expanding impact of heavy metals (HMs) on environmental and public health necessitates the development of advanced predictive models that enhance the precision and efficiency of monitoring and remediation strategies. This study aimed to evaluate newly developed machine learning (ML) models for predicting the removal of HMs such as cadmium (Cd²⁺), copper (Cu²⁺), nickel (Ni²⁺), lead (Pb²⁺), and zinc (Zn²⁺) using chitosan-based flocculants (CBFs) from wastewater. A gradient boosting regressor (GBR), Hist gradient boosting regressor (HGBR), random forest regressor (RFR), and extreme gradient boosting regressor (XGBR) were developed, with a cluster label generated by K-means clustering included as an additional feature to enhance model

SUBMITTER: Yaseen ZM 

PROVIDER: S-EPMC12529125 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature