Multivariate explainable machine learning assists in redefining high-performance phosphate removal by layered double hydroxides.

The excessive discharge of phosphorus can trigger eutrophication, thereby posing significant threats to water quality and ecosystem health. Layered double hydroxides (LDHs) are considered promising adsorbents for phosphate removal due to their unique layered structures and tunable properties. However, the full realization of dephosphorization performance of LDHs is determined by multiple factors, including structural features, synthesis conditions, and operational parameters. This complex interplay renders the optimization of their design and application a formidable challenge. Herein, an optimized multilevel nested random forest (MNRF) model was proposed to systematically analyze, predict, and enhance the phosphate adsorption performance of LDHs. This approach not only enabled precise prediction of phosphate adsorption capacity (PAC) and phosphate removal efficiency (PRE), but offered a comprehensive assessment of features importance from diverse perspectives. Through multivariate interpretability analysis using tree-based diagnostics with multi-metric disassembly of implied trees, Shapley values, partial dependence plots, and individual conditional expectations, we identified the key adsorbent properties and reaction parameters that determine the dephosphorization performance of LDHs. Decisive structural features include metal type, synthesis temperature, and synthesis time, while critical operational parameters include initial concentration, dosage, and pH. Experimental validation further confirmed the model's predictions, highlighting that the Mg-Al LDH prepared under model-guided conditions is effective in scenarios requiring high phosphate uptake capacity, achieving a PAC of 98.32 mg g. Meanwhile, the Ca-Fe LDH synthesized following the model's guidance is suitable for the deep treatment of medium-to-low phosphate concentrations, demonstrating a PRE exceeding 93 %. This study offers an innovative design and optimization guide for redefining high-performance LDHs via machine learning, enhancing phosphate removal performance and advancing sustainable water treatment techniques.