Modeling and Analysis of Lake Water Levels Using System Dynamics and Principal Component Analysis
DOI:
https://doi.org/10.62051/00khjg86Keywords:
Principal component analysis; Hydrodynamic assessment; Great Lakes; System Dynamics; water level.Abstract
Understanding the intricate dynamics of the Great Lakes water levels, shaped by environmental factors like rainfall and evaporation, is crucial for devising sustainable water management strategies. This study develops a model to manage the Great Lakes' water levels by analyzing the interconnectedness of the lake network. Utilizing principal component analysis, key variables like rainfall and evaporation were evaluated for their impact on water levels. System dynamics models integrated with flow conservation laws allowed for a detailed hydrodynamic assessment. The study introduces a "narrow seam method" for dry river scenarios, enhancing model accuracy. Annual water level trends from 2000 are depicted, offering insights into future water management practices in response to environmental changes.
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[1] ANGRADI T R, WILLIAMS K C, HOFFMAN J C, et al. Goals, beneficiaries, and indicators of waterfront revitalization in Great Lakes Areas of Concern and coastal communities [J]. Journal of Great Lakes Research, 2019, 45 (5): 851 - 863.
[2] BAXTER B K, BUTLER J K. Climate change and great Salt Lake [J]. Great Salt Lake biology: a terminal lake in a time of change, 2020: 23 - 52.
[3] MIANABADI A, DAVARY K, MIANABADI H, et al. Toward the development of a conceptual framework for the complex interaction between environmental changes and rural-urban migration [J]. Frontiers in Water, 2023, 5.
[4] MEMARI S. Combining Remote Sensing, Machine-Learning, and Mechanistic Modeling to Improve Coastal Hydrodynamics and Water Quality Modeling in the Laurentian Great Lakes [D]. Michigan State University, 2023.
[5] KHERIF F, LATYPOVA A. Principal component analysis [M]//Machine learning. Elsevier, 2020: 209 - 225.
[6] KARAMIZADEH S, ABDULLAH S M, MANAF A A, et al. An overview of principal component analysis [J]. Journal of Signal and Information Processing, 2020, 4.
[7] HASAN B M S, ABDULAZEEZ A M. A review of principal component analysis algorithm for dimensionality reduction [J]. Journal of Soft Computing and Data Mining, 2021, 2 (1): 20 - 30.
[8] MORAIS C L, PARASKEVAIDI M, CUI L, et al. Standardization of complex biologically derived spectrochemical datasets [J]. Nature Protocols, 2019, 14 (5): 1546 - 1577.
[9] SALEM N, HUSSEIN S. Data dimensional reduction and principal components analysis [J]. Procedia Computer Science, 2019, 163: 292 - 299.
[10] LONG Y, YANG T, GAO W, et al. Prevention and control of algae residue deposition in long-distance water conveyance project [J]. Environmental Pollution, 2024, 344: 123294.
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