Leveraging scenario differences for cross-task generalization in water plant transfer machine learning models.

ABSTRACT: Machine learning (ML) models are increasingly deployed in urban water systems to optimize operations, enhance efficiency, and curb resource consumption amid growing sustainability demands. Yet, their transferability across plants is hampered by scenario differences-variations in environmental factors, protocols, and data distributions-that erode performance and necessitate energy-intensive retraining. While existing strategies focus on minimizing these differences via domain adaptation or fine-tuning, none exploit them as inherent prior knowledge for improved generalization. Here we show an environmental information adaptive transfer network (EIATN) framework that can leverage scenario differences to enable effective generalization across distinct prediction tasks within the same water pla

SUBMITTER: Wang YQ 

PROVIDER: S-EPMC12320164 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature