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Using Machine Learning to Assess Parameters Associated with Harmful Algal Blooms for Lake Erie
Citation:
Feng Chang, C., M. Astitha, Valerie Cover, C. Tang, P. Vlahos, D. Wanik, AND J. Yan. Using Machine Learning to Assess Parameters Associated with Harmful Algal Blooms for Lake Erie. Meteorology and Climate - Modeling Air Quality, Davis, CA, September 11 - 13, 2019.
Impact/Purpose:
The ability to predict water quality in lakes is important since healthy lakes provide numerous environmental benefits that positively influence our quality of life and the strength of our economy. Dissolved oxygen (DO) levels within lakes are important indicators of lake ecosystem health. In this study, we use machine learning techniques and modeled and observed data to characterize drivers of DO levels, and to develop a predictive model.
Description:
The ability to predict water quality in lakes is important since healthy lakes provide numerous environmental benefits that positively influence our quality of life and the strength of our economy. A combination of urban areas, industries, and agricultural activities have contributed to an increase loading of nutrient pollution into Lake Erie, particularly phosphorus and nitrogen. In this study, in-situ measurements of dissolved oxygen (DO), total phosphorus (total P), and total nitrogen (total N) in