From Forecasting to Rolling Optimization: Real-Time Hybrid Modeling for Water Quantity–Quality Regulation in the SNWD Extended Eastern Route

Climate change is increasing hydro-climatic variability and amplifying water quality risks, posing major challenges for operating large-scale inter-basin water transfer projects. Operators must make real-time decisions while accounting for multiple source waters that exhibit distinct and nonstationary quality characteristics, alongside persistent limitations of process-based models and data scarcity in upstream boundary conditions. To address these challenges, we propose a three-tier hybrid modeling framework that integrates machine learning (ML), process-based hydrodynamic–water quality simulation, and multi-objective optimization to enable coordinated regulation of water quantity and quality in the extended Eastern Route of the South-to-North Water Diversion Project (SNWD).

The framework is driven by continuous observations from monitoring stations distributed along the project route and is implemented as a three-level modeling cascade. Level 1 develops ML-based upstream boundary prediction models using Long Short-Term Memory (LSTM) networks to produce 7-day-ahead forecasts of key water quality variables for heterogeneous source waters (Yellow River water, diversion water, and local water). Forecast targets include CODMn (permanganate index), NH₃–N, total nitrogen (TN), total phosphorus (TP), and dissolved oxygen (DO), while pH is treated as a compliance constraint. This anticipatory component mitigates data scarcity and captures nonlinear inflow dynamics, providing actionable boundary conditions for downstream assessment. Level 2 constructs a mechanism–data fusion module that couples process-based hydrodynamic and water quality models with ML-based corrections informed by real-time monitoring. By assimilating monitoring observations together with future engineering operation plans and diversion demand assessments, the module simulates transport, mixing, and water quality evolution along the transfer route. Level 3 applies multi-objective optimization to generate rolling diversion schedules that balance water supply reliability against pollution risk under climate-stress scenarios. The optimizer outputs updated, implementable schedules as new data and near-term plans become available, supporting operational water management.

A spatio-temporal decoupling strategy is further introduced to separate source-specific variability from in-route transport processes, enabling interpretable attribution of observed water quality changes to different sources and facilitating targeted regulation across critical segments. Operational deployment demonstrates enhanced decision support: the 7-day predictive lead time enables proactive coordination of multi-source diversions, and the optimized rolling regulation reduces concentrations of the regulated indicators (CODMn, NH₃–N, TN, and TP) by approximately 9% while improving Water Quality Index (WQI) scores by about 11% at the critical control section DiSanDian. The proposed hybrid framework provides a scalable and transferable pathway for integrating AI with process-based understanding to improve water quality simulation and real-time management, contributing to climate adaptation and resilience strategies for complex water infrastructure systems.