Federated AI Models for Cross-Border Environmental Risk Assessment and Policy Simulation in Transnational River Basins
Keywords:
Federated learning; transboundary river basins; environmental risk assessment; agent-based modeling; differential privacy; policy simulation; socio-hydrology.Abstract
Transboundary river basins present some of the most difficult environmental governance scenarios, characterized by the presence of multiple data sovereignty regimes, diverse hydrologic behavior, and conflicting national policy targets impeding harmonized risk management. Traditional watershed models assume that transnational data collaboration is simply a yes-or-no issue, requiring riparian countries to choose between transparency and sovereignty. This paper proposes FedRiverAI, a hierarchical architecture of federated artificial intelligence that integrates differential privacy preserving cooperative machine learning with the semi-distributed process-based hydrologic modeling (SWAT), and decision-making layers based on agent-based approaches for transboundary environmental risk assessment and policy analysis while avoiding the transmission of sensitive unprocessed data across national borders. FedRiverAI allows each nation in the river basin to build its risk model based on the sovereign hydrological, agricultural, and ecological data that are combined in the global system for simulations under the LOC function. Experiments conducted in the basins of Mekong, Niger, and Indus transboundary river systems show that FedRiverAI (i) attains risk-prediction accuracy within 4.2% of an ideal fully centralized system and simultaneously provides differential privacy guarantees (ε ≤ 1.5), (ii) identifies cross-boundary ecological infringements 2.1 times faster than isolated country-specific approaches, and (iii) discovers that high levels of cooperation beyond LOC = 0.7 decrease basin-wide eco-hydrological indicator infringements by 28%-37%, with positive impacts on food and energy security. The present work contributes to the emerging field of socio-hydrology and federated AI at its intersection, developing a data-sovereign architecture for environmental governance in accordance with UN SDGs 6, 13, and 17.
