Date of Award


Degree Name

Master of Science


Geography and Environmental Resources

First Advisor

Li, Ruopu

Second Advisor

Duram, Leslie


Harmful Algal Blooms (HABs) occur due to the excessive growth of algal in waterbodies such as lakes, rivers, and ponds. The cyanotoxins produced by HABs are harmful to wildlife, animals, and humans when ingested or exposed. Due to the toxic and rapid growth of HABs, it is essential to assess potential causes of HABs over broad geographical scales. This observational study aims to understand the spatiotemporal patterns and drivers of HABs across the State of Illinois using both regular environmental monitoring and citizen science datasets from the Illinois Environmental Protection Agency (IEPA). The Ambient Lake Monitoring Program and the Illinois Clean Lakes Program regularly conduct chlorophyll-a measurements, collectively referred to as the ALMP + ICLP dataset. Similarly, the Volunteer Lake Monitoring Program of the Illinois Environmental Protection Agency (IEPA) organizes volunteer citizens to collect Secchi-disk measurements, known as the VLMP dataset. Machine learning algorithms including Random Forest, Artificial Neural Network, and Support Vector Machine are used to evaluate HABs and trophic states of HABs based on nine meteorological variables, six lake morphological variables, and eight land use and land cover variables. The data characteristics found the Cook county area consisted of over half of the total VLMP observations. The meteorological variables were most important for accuracy and classification in the Random Forest modeling, and the VLMP dataset performed the best at trophic state classification, and the Random Forest model performed the best overall compared to the other machine learning models. This study concludes that the VLMP is a beneficial and comparable tool when coupled with the ALMP + ICLP data for HAB monitoring in Illinois.




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