River-lake health assessment is critical for ecological governance, but overestimation risk induced by dual uncertainties (predictive uncertainty of indicator values and methodological uncertainty of weights) compromises the reliability of assessment results and governance decisions. To address this gap, this study proposes a general risk analysis framework integrating dual uncertainty quantification and overestimation risk coupling. First, a multi-dimensional assessment system with 12 key indicators (covering hydrology, water quality, aquatic biology, and social services) was established via Meta-analysis and quantitative screening. For uncertainty quantification: the Physics-Informed Neural Networks (PINN) model was used to characterize indicator value uncertainty based on 1264 historical monitoring samples; four complementary weighting methods (AHP, EWM, CRITIC, PCA) were integrated with a game theory-based framework (Nash equilibrium) to resolve weight conflicts, and weight uncertainty was quantified via normal distribution assumption (mean = coordinated weight, standard deviation = 1/10 of mean). The First-Order Second-Moment (FOSM) method was then adopted to establish a coupled “dual uncertainties-overestimation risk” model, quantifying the probability and magnitude of overestimation risk. Validated in Poyang Lake (China’s largest freshwater lake), results identified total phosphorus (TP), total nitrogen (TN), and Fish Biological Loss Index (FBLI) as high-risk indicators, with maximum allowable thresholds of 5.10–7.40, 7.04–9.38, and 6.05–9.02 across risk levels (1–50%), respectively. The comprehensive overestimation risk score ranged from 74.16 to 81.17, providing actionable thresholds for governance. This framework systematically addresses the insufficient consideration of dual uncertainties in existing studies, offering a scientific and operable tool for improving the reliability of river-lake health assessment and supporting targeted ecological protection decisions globally.
Wu et al. (Tue,) studied this question.