The Great Lakes of the United States and Canada are the largest freshwater lakes in the world, and people face changing dynamics and stakeholder conflicts when it comes to lake issues. The purpose of this study was to investigate the influence of multi-objective programming and system dynamics models on the optimal water level results in the Great Lakes. A multi-objective programming model was constructed to maximize benefits and minimize costs, and multiple factors affecting water level change were considered. The genetic algorithm was used to solve the model to obtain the global optimal solution. By establishing a system dynamics model to simulate the fluctuation of water level in the Great Lakes, considering the influence of climate and human activities on the water level, and further refining the water level control, the results show that the model can effectively simulate the water level change, provide an important basis for relevant decision-making, and provide an important reference for the optimal control of the water level in the Great Lakes. In addition, the results of this study can help to provide new ideas for water level control of the same type of large lakes.

Han Lin. Simulation of lake system based on multi-objective optimization algorithm and system dynamics model. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 8: 21-26. https://doi.org/10.25236/AJCIS.2024.070804.

[1] Woolway R I, Kraemer B M, Lenters J D, et al. Global lake responses to climate change[J]. Nature Reviews Earth & Environment, 2020, 1(8): 388-403.

[2] Zhu S, Hrnjica B, Ptak M, et al. Forecasting of water level in multiple temperate lakes using machine learning models[J]. Journal of Hydrology, 2020, 585: 124819.

[3] Chaudhari S, Felfelani F, Shin S, et al. Climate and anthropogenic contributions to the desiccation of the second largest saline lake in the twentieth century[J]. Journal of Hydrology, 2018, 560: 342-353.

[4] Nguyen X H. Combining statistical machine learning models with ARIMA for water level forecasting: The case of the Red river[J]. Advances in Water Resources, 2020, 142: 103656.

[5] Mohammadi B, Guan Y, Aghelpour P, et al. Simulation of Titicaca lake water level fluctuations using hybrid machine learning technique integrated with grey wolf optimizer algorithm[J]. Water, 2020, 12(11): 3015.

[6] Xu J, Fan H, Luo M, et al. Transformer based water level prediction in Poyang Lake, China[J]. Water, 2023, 15(3): 576. 

[7] Alhijawi B, Awajan A. Genetic algorithms: Theory, genetic operators, solutions, and applications[J]. Evolutionary Intelligence, 2023: 1-12. 

[8] Jike G, Yuhui Q, Chunming W, et al. Review of genetic algorithm research[J]. Application research of computers, 2008, 25(10): 2912-2913. 

[9] Zhu S, Lu H, Ptak M, et al. Lake water-level fluctuation forecasting using machine learning models: a systematic review[J]. Environmental Science and Pollution Research, 2020, 27(36): 44807-44819. 

[10] Ershkov S V, Prosviryakov E Y, Burmasheva N V, et al. Towards understanding the algorithms for solving the Navier–Stokes equations[J]. Fluid Dynamics Research, 2021, 53(4): 044501.