Multi-objective decision model for wastewater treatment technology selection based on machine learning.

This study integrated life cycle assessment (LCA), machine learning (ML), and analytic hierarchy process (AHP) to optimize wastewater treatment technology selection in the upper Yellow River Basin-a region constrained by limited carrying capacity and ecological fragility. LCA results from a representative city in Gansu Province identified anaerobic-anoxic-oxic combined with sequencing batch reactor (AAO + SBR) as the configuration with the lowest environmental footprint. Monte Carlo simulations were employed to augment the dataset, ensuring statistical reliability. In a comparative analysis, the XGBoost outperformed random forest (RF) and support vector machine (SVM), reducing mean squared error (MSE) by 1.4-3.1%. Ultimately, the integrated AHP-ML model confirmed AAO + SBR and AAO with membrane bioreactor (AAO + MBR) as the optimal technologies under current condition. The data-driven intelligent model constructed in this study, reconciling treatment efficiency with ecological sustainability, provided precise guidance for low-carbon wastewater governance in the Yellow River Basin and similar ecologically fragile regions.