Optimization of Water Level from Great Lakes Based on Vector Autoregressive Model and Goal Programming Model

Ziheng Zhou; Binzhe Li

doi:10.54097/zag2js05

Authors

Ziheng Zhou
Binzhe Li

DOI:

https://doi.org/10.54097/zag2js05

Keywords:

Water Level, Network, VAR, Goal Programming.

Abstract

The Great Lakes, the largest group of freshwater lakes in the world, have profound impacts on residents, ecosystems, water resource utilization, shipping, and tourism industries. Addressing water level variability, this study integrates network science, goal programming algorithm, and Model Predictive Control to establish a comprehensive and adaptive model for optimizing dam adjustment mechanisms and maximizing stakeholder benefits. Initially, a Vector Autoregression Model is developed for the Great Lakes and connecting river flows toward the Atlantic Ocean to conditionally project future paths of specified variables. This model yields a network representation of the Great Lakes system. Subsequently, a Goal Programming Model is constructed to determine optimal water levels throughout the year based on extensive literature review and priority rankings. Leveraging insights from the 2014 plan, a detailed analysis is conducted on Lake Ontario water levels, focusing solely on stakeholders and influential factors. This research contributes a robust methodology for managing water levels in the Great Lakes region, providing valuable insights for sustainable water resource management.

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