Water quality prediction in non-stationary environmental systems requires not only high predictive accuracy but also structural robustness under physical, ecological, and operational constraints. This study reframes multi-model fusion as a constraint-governed inference architecture and synthesizes advances in rule-based adjudication, reliability-aware aggregation, post-fusion projection, dual-track adaptation, and hierarchical backoff control. By establishing a taxonomy of boundary constraints—specifically mass conservation, reaction kinetics, hydraulic transport, and ecological tipping points—an admissible prediction manifold identifies key structural limitations in existing paradigms, particularly their vulnerability to physical inconsistency and diminished reliability during non-stationary distribution shifts. A unified end-to-end robust framework is proposed in which candidate predictions are separated from admissibility validation, uncertainty is directly coupled to aggregation logic, and degradation pathways are explicitly defined under distribution shift. Furthermore, a multidimensional robustness evaluation matrix is introduced, incorporating structural consistency, ecological compliance, calibration quality, and adaptive stability alongside conventional accuracy metrics. The study advances water quality forecasting from model-centric optimization toward architecture-level governance, demonstrating that constraint-aware designs improve structural consistency, robustness under distribution shifts, and early warning reliability, providing a systematic reference for developing resilient, transparent, and operationally deployable environmental prediction systems.
Ma et al. (Tue,) studied this question.