Neural network intelligent anti-interference controller for water level equilibrium control in lake systems: a case study of the Great Lakes in North America

Xiangze Meng

doi:10.1117/12.3035322

21 July 2024 Neural network intelligent anti-interference controller for water level equilibrium control in lake systems: a case study of the Great Lakes in North America

Xiangze Meng

Author Affiliations +

Proceedings Volume 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024); 132192B (2024) https://doi.org/10.1117/12.3035322
Event: 4th International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2024), 2024, Kaifeng, China

Abstract

Due to the complexity of hydrological characteristics in lake systems and their susceptibility to environmental changes, water level control in lake systems has always been a highly challenging issue. This paper proposes a water level equilibrium control scheme for lake systems based on an intelligent anti-interference architecture using neural networks. By analyzing the spatial distribution structure and historical hydrological data of the Great Lakes system, a mathematical model for water level equilibrium control is established. Subsequently, an intelligent controller structure based on neural networks is proposed. This controller can real-time perceive changes in lake system water levels and automatically adjust control strategies to maintain water levels fluctuating within appropriate ranges. Finally, the proposed method is applied to a simulator of the Great Lakes system in North America. The results indicate that the proposed method can achieve stable control of lake water levels under different environmental conditions, demonstrating good stability and robustness.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Xiangze Meng "Neural network intelligent anti-interference controller for water level equilibrium control in lake systems: a case study of the Great Lakes in North America", Proc. SPIE 13219, Fourth International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2024), 132192B (21 July 2024); https://doi.org/10.1117/12.3035322

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KEYWORDS

Control systems

Neural networks

Education and training

Rain

Performance modeling

Analytical research

Data modeling

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