Identification of spatially heterogeneous drivers of lake eutrophication in China with explainable automated machine learning

This study validates the XAutoML framework as a scalable prototype for managing global environmental gradients by effectively disentangling the complex interactions between anthropogenic pressures and natural background conditions, and offers a transferable methodology for achieving precise, spatially differentiated ecosystem management in heterogeneous regions worldwide.