A Combined-Weight Water Quality Evaluation Model for Seasonal Dynamics in Urban Rivers: Integrating Subjective and Objective Weighting Approaches (International Journal of Computer Science and Engineering, 2026)
The evaluation of urban river water quality is frequently complicated by pronounced seasonal fluctuations, yet conventional assessment models often rely on fixed weighting schemes that fail to capture such temporal heterogeneity. This study constructs a comprehensive evaluation model based on combined weights, integrating the Analytic Hierarchy Process (AHP) as a subjective component with the Entropy Weight Method as an objective counterpart, and subsequently applies it to examine seasonal water quality variations in a representative urban river system. The combined weights are determined through an optimization framework that seeks to minimize the deviation between the subjective and objective vectors, offering a potentially more balanced representation of parameter importance across different hydrological regimes. When implemented using data collected from multiple monitoring stations across dry, wet, and transitional seasons, the model revealed that the contribution of individual pollutants, particularly nitrogen and phosphorus species, shifted considerably between seasons, a pattern that single-weight approaches tended to obscure rather than illuminate. Interestingly, the combined-weight model did not uniformly outperform the single-weight alternatives in all monitoring periods; during the transitional season, for instance, the model exhibited higher sensitivity to pH fluctuations, suggesting that the weighting structure might be overly responsive to parameter variability under mixed hydrological conditions. These findings imply that seasonal stratification is not merely a data-partitioning exercise but rather a critical methodological consideration that can fundamentally alter the interpretation of water quality status.