ArticleWater environment research : a research publication of the Water Environment Federation2026
Tree-Based Machine Learning Models for Predicting Phosphate Adsorption Performance of Layered Double Hydroxides: Insights Into Mechanisms and Process Optimization.
Article in Water environment research : a research publication of the Water Environment Federation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Optimizing layered double hydroxides (LDHs) for phosphate removal is challenged by complex, multivariable interactions. To bridge this knowledge gap, this study introduces a novel data-driven framework leveraging a comprehensive 2251-record dataset and advanced machine learning to predict and optimize LDHs adsorption capacity. Among six tree-based models evaluated, CatBoost achieved the best overall performance across five random seeds, with a test R
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