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

), with the GBR model obtaining the lowest error rate in the testing. The results signified a robust capability of the HGBR model for generalization and its capacity as a trustworthy tool in the framework of environmental monitoring. Future research directions required the exploration of the synthesis of these models into real-time predictive monitoring systems and an exploration of the application of integrated ML approaches to boost the predictive accuracy and reliability across wider environmental conditions.