Evaluation and Prediction of Big Data-Driven Marine Ecological Restoration Effect Based on CRITIC-IGA Hybrid Model

The marine ecosystem is facing multiple threats such as pollution, overfishing, and coastal erosion, and an objective and quantifiable assessment method of restoration effect is urgently needed. However, the existing models mostly rely on subjective weighting or are difficult to cope with the uncertainty and high-dimensional nonlinear characteristics of ecological data. In this paper, a multilevel fuzzy comprehensive evaluation model combining Criteria Importance Through Intercriteria Correlation (CRITIC) objective weighting and improved genetic algorithm (IGA) is proposed to evaluate and predict the effect of marine ecological restoration driven by big data. The model is based on more than 10,000 multi-source spatio-temporal data of eight restoration projects (including mangroves and wetland coastlines) in typical coastal cities of China from 2015 to 2023. The uncertainty of ecological indicators is represented by a fuzzy membership function, and the weights are dynamically optimized in a data-driven way. The results show that this method significantly improves the objectivity, robustness, and adaptability of the assessment, and can more truly reflect the temporal and spatial differentiation of the restoration effect, which provides strong support for the dynamic optimization of the restoration scheme, the precise allocation of resources to the scientific management decision, and promotes the sustainable development of the green marine economy.