Optimizing Water Level Regulation in the Great Lakes: An Integrated Approach Using Genetic Algorithms and Network Flow Models for Sustainable Resource Management

The Great Lakes, as a vital freshwater resource, are significantly influenced by climate change and anthropogenic activities, leading to acute water level fluctuations with profound impacts on various sectors including agriculture, industry, and ecology. This study addresses the need for scientifically sound water level regulation by developing an integrated approach that employs a genetic algorithm and a network flow model. The model is designed to balance the interests of multiple stakeholders and optimize water levels across different periods, thereby ensuring the sustainable use of water resources and ecological balance. Through comprehensive data collection and processing, the study constructs a robust prediction model that accounts for economic activities and ecological protection. The application of genetic algorithms enhances the efficiency and accuracy of the model, providing a practical tool for decision-makers in water resource management. The findings underscore the model's reliability and its potential to support policy-making for sustainable water resource management in the Great Lakes region.