Ecological impacts of hydropower projects in the lower reaches of the Yarlung Zangbo River driven by machine learning: Focusing on nitrogen and phosphorus cycle prediction and assessment

• Federated learning overcomes the problem of sparsity in high-altitude data. • Physical Information Neural Networks (PINNs) accurately simulate interface processes. • Causal machine learning drives ecological regulation decisions. • Glacier pulse hydroelectric dispatch minute level coupled prediction. The lower the Yarlung Zangbo River hydropower project is the core infrastructure of the national energy security and carbon neutrality strategy, but it is located in the extreme environment of the Qinghai Tibet Plateau, facing the dual challenges of ecological vulnerability and multi process coupling. Traditional evaluation methods have systematically failed in key issues such as sediment phosphorus release prediction due to sparse monitoring networks (station density of only 0.14 per thousand square kilometers) and limitations in mechanistic models (inability to analyze nonlinearity, time delay, and feedback loops). The measured SRP flux exceeded the environmental impact assessment prediction value by 3.1 times. This review systematically analyzes the paradigm innovation of machine learning driven ecological prediction in hydropower engineering, and establishes an interdisciplinary framework of hydrological mechanism constraints ecological process observation computer science algorithms. In response to data sparsity, federated learning integrates heterogeneous data from five provinces (with monitoring coverage increased to 92%), overcoming the limitation of spatial interpolation error of ± 47%; The combination of transfer learning and meta learning reduces the variance of SRP release prediction in hypoxic environments by 58%. In response to the complexity of dynamic coupling, physical information neural networks (PINNs) are embedded into the frozen soil hydrothermal iron reduction double equation (Stephen equation ∂ T/∂ t=k∇ 2 T+Lf ∂ φ/∂ t and d [Fe 2+ ]/dt=k [Fe 3+ ] [OM]), achieving a lag response prediction error of less than 0.05 mg/L for SRP release; Graph Convolutional Network (GCN) quantifies the water sediment microorganism electron transfer symbiotic edge (weight 0.78), revealing the core mechanism of the 2.3-fold increase in nanowire conductivity when Eh < -200 mV. In response to extreme climate adaptation, reinforcement learning (DDPG) constructs a Pareto front for power generation ecology livelihood, compressing power generation losses to < 15% under millennium snowmelt floods; The LSTM PINNs hybrid architecture couples glacier pulse phase prediction (error < 3 hours) with adaptive water level control, achieving a 62% reduction in algal blooms in the Linzhi Reservoir area by 2028. Causal machine learning is based on the DoWhy framework to analyze the contribution of water level drop>2m to algal blooms (28.7% ± 3.5%), driving the generation of bilingual scenario deduction reports (the ecological discharge+photovoltaic compensation scheme increased herders' income by 12%). This review proposes a roadmap for building a Yajiang Digital Twin in the short term, developing causal machine learning in the medium term, and promoting AI ecological model coupling in the long term. It also calls for the construction of an ISO-19157 certified ML benchmark database for hydropower in Xizang Plateau, to provide a replicable Chinese scheme for intelligent ecological governance of major infrastructure in global cold regions.