Optimization of agricultural non-point source nitrogen pollution control in reservoir watersheds: a machine learning-driven spatially integrated governance framework

Key Points

• A machine learning-driven framework significantly optimizes non-point source nitrogen pollution control.
• The model incorporates spatially integrated governance strategies, enhancing ecological interventions in watersheds.
• Assessment using advanced spatial analysis techniques reveals critical nitrogen sources across agricultural landscapes.
• This framework may enable sustainable water management practices, but requires further validation in diverse agricultural settings.