Author(s)

Lijing Yang, Xinkai Yue, Yuhang Liu, Yue Zhong

Corresponding Author

Xinkai Yue

Abstract

This study addresses the water level management challenges in the Great Lakes region by proposing a weight indicator system based on the Analytic Hierarchy Process (AHP) and incorporating Partial Differential Equations (PDEs) to simulate and control the lake water flow and water level variations. By applying the 3δ principle to the water level data spanning the past two decades, the study effectively eliminates outliers, ensuring the accuracy of the data for analysis. The weight indicator system constructed in this research takes into account the needs of various stakeholders, including construction, navigation, ecology, power generation, and agriculture, and develops specific weight matrices for the unique conditions of each lake. The experimental results demonstrate that the model can effectively meet the needs of stakeholders and offers a practical solution for water level control in the Great Lakes. By comparing the actual water level data from 2017 with the model predictions, the model's Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) indicators all outperformed the actual data, confirming the model's effectiveness in controlling water levels. These findings underscore the model's potential for practical application and provide a solid foundation for future improvements and refinements. Further research could explore additional factors affecting water levels, such as climate change, extreme weather events, and human activities, to enhance the model's adaptability and accuracy.

Keywords

Water level; Great Lakes; Analytic Hierarchy Process; Staggered method; Partial differential equation