Traditional evaluations of revetment projects primarily focus on structural safety and economic analysis, which cannot comprehensively reflect the overall effectiveness of such projects. To address this issue, this paper establishes a comprehensive evaluation index system for ecological revetments based on ecosystem theory and sustainable development principles. The system is tailored for the Pinglu Canal Ecological Revetment Demonstration Project. It assesses three key aspects: structural stability, ecological health, and socioeconomic benefits. Subjective weights were calculated using the Fuzzy Analytic Hierarchy Process (FAHP). Objective weights were determined by optimizing the Projection Pursuit (PP) model with the Tent-improved Crocodile Ambush Optimization Algorithm (TCAOA). Game theory was employed to compute the combined weights. The evaluation grade of the ecological revetment project was subsequently determined using a cloud model. The results show that the cloud eigenvalues of the project’s comprehensive evaluation are (1.096, 0.209, 0.047), and the application effectiveness is rated as “Excellent”. The cloud expected values for structural stability, ecological health, and socioeconomic benefits are 1.02, 1.18, and 1.15, respectively. All of these values are at the “Excellent” level. Compared with GA-PP and PSO-PP, TCAOA-PP converges faster and more stably. It requires only 347 iterations, achieves a coefficient variation of 3.8%, and reduces computation time by 23%. By revealing the nonlinear coupling relationships among indicators, the model presented in this paper provides a methodological foundation for establishing an evaluation framework that is ecologically interpretable for bank protection. This study has important practical significance for promoting the high-quality development of inland waterways and the construction of ecological revetments.

Evaluating the aquatic ecological and environmental consequences of dredging projects with precision is essential for reconciling engineering objectives with the long-term health of aquatic ecosystems. This study establishes an evaluation system for the aquatic ecological and environmental impacts of dredging engineering based on the Pressure–State–Response (PSR) analytical framework, and constructs a comprehensive assessment system through Velocity Pausing Particle Swarm Optimization–Projection Pursuit (VPPSO-PP) coupled with fuzzy pattern recognition. Taking the Pinglu Canal project as a case study, the objective weights of indicators are obtained via the VPPSO-PP method, and the impact levels are determined by combining the fuzzy pattern recognition model. Case studies show that the quality of discharged residual water is the most critical factor affecting the aquatic ecological environment, ranking highest with a weight of 0.0839, followed by the proportion of aquatic ecological restoration investment at 0.0685. Among the five typical dredging sections of the Pinglu Canal, the Shaping River section and the Offshore Estuary Section were rated as having a “mild impact.” In contrast, the Main Stream of Qinjiang River section, the Watershed section, and the Qinzhou urban section were rated as having a “moderate impact.” These evaluation results are consistent with the actual engineering conditions. The model developed in this study enables a quantitative and objective assessment of the aquatic ecological impacts of dredging projects. It provides a scientific basis and a practical tool for ecological management and decision-making in dredging operations.

Accurate quantification of ecological losses in estuarine environments is often hindered by the mismatch between coarse-resolution biological surveys and fine-scale physical disturbances from engineering activities. While numerical models can simulate high-resolution environmental shifts, the inherent sparsity of ecological monitoring points limits the precision of spatial impact assessments. This study develops an integrated spatial-downscaling framework to transform sparse monitoring data into a high-resolution spatial continuum. A three-tiered modeling approach was used: first, the estuarine domain was partitioned into five eco-hydrodynamic zones using an entropy-weighted Support Vector Machine (SVM); second, localized chained Generalized Additive Models (GAMs) were established within each zone using MIKE-simulated hydrodynamic and water-quality data as proxy drivers; and third, these localized response functions were propagated across the study area to quantify multi-trophic biomass and economic losses. The framework revealed substantial spatial non-stationarity. Dredging operations locally altered the estuarine hydrodynamic regime. In northern channels, decreases in flow velocity were statistically associated with phytoplankton biomass to decline by 5.0% to 23.42%. Conversely, southern velocity increases enhanced water exchange and plankton growth. Using silt curtains as a mitigation strategy reduced the loss of phytoplankton by 11.4% and zooplankton by 9.6%. As a result, the total economic loss decreased from 26.54 million CNY to 25.34 million CNY, equivalent to a 4.5% reduction in economic loss. These results indicate that the proposed downscaling method can generate spatially explicit biological estimates. By offering a systematic pathway for impact evaluation and compensation in data-limited coastal regions, this framework supports more ecologically sustainable dredging operations. Nevertheless, the framework remains dependent on the representativeness of sparse monitoring stations, and future applications should integrate cross-estuary validation to improve transferability and uncertainty control.