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

Yuxuan Tang; Yuke Zhang; Yixi Wu

doi:10.62051/hqk6e436

Authors

Yuxuan Tang
Yuke Zhang
Yixi Wu

DOI:

https://doi.org/10.62051/hqk6e436

Keywords:

Great Lakes; Water Level Regulation; Genetic Algorithm; Network Flow Model; Sustainability.

Abstract

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.

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References

[1] Lü Haifeng, et al. A Hybrid Water Level Prediction Model Based on CNN-LSTM-Attention and Autoregressive Techniques[J]. Water Resources and Hydropower Engineering, 2024, 55(06): 16-31.

[2] Long Yuan-nan, et al. Research on a Water Level Prediction Scheme Using the IPSO-EGA-LSTM Model for Dongting Lake[J]. Resources and Environment in the Yangtze Basin, 2024, 33(06): 1262-1272.

[3] Tan Kun, Huang Qianzi. Water Level Prediction Technology Based on Multi-Feature Spatiotemporal Fusion Networks[J]. Water Transport Engineering, 2024, 05: 151-155.

[4] Tian Songyi, et al. Adaptive Robust Prediction of Groundwater Levels Based on an LSTM Network with Attention Mechanism Fusion[J]. Engineering Science and Technology, 2024, 56(01): 54-64.

[5] Huang Ying, et al. Research and Implementation of an Efficient Long Sequence Water Level Prediction Model[J]. Journal of University of Electronic Science and Technology, 2023, 52(04): 595-601.

[6] Zhang Sen, et al. Research on Water Consumption Rate Calculation of Hydropower Stations and Its Impact on Water Level Prediction[J]. Hydropower Generation, 2023, 49(12): 94-100.

[7] Chen Huanliang, et al. Research on Water Level Prediction of Karst Springs Based on Genetic Algorithm Optimized BP Neural Network[J]. Journal of Water Resources and Water Engineering, 2022, 33(04): 50-57+63.

[8] Yu X P, Qiu D S, Cao M. Application of machine learning algorithms in predicting water levels and flow rates in Jinan Spring Group[J]. Shandong Water Conservancy, 2022, (11): 7-9.

[9] Chen Juan, Zhong Ping'an, Xu Bin. Risk Analysis of Reservoir Flood Control Scheduling Based on Stochastic Differential Equations[J]. Journal of Hohai University (Natural Sciences), 2013, 41(05): 400-404.

[10] Tao Xidong. Water Quality Management System in the Great Lakes Region of the United States and Canada: Experience and Insights[J]. Social Sciences, 2009, 06: 25-32+187.