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Certificate of Completion
THIS ACKNOWLEDGES THAT
HAS COMPLETED THE MAY-SUMMER 2024 DEEP LEARNING BOOT CAMP
Meltem Uyanik
Roman Holowinsky, PhD
September 06, 2024
DIRECTOR
DATE
TEAM
Ocean Optimizers (New Atlantis)
Margaret Swerdloff, Junaid Hasan, Meltem Uyanik, Maitituerdi Aihemaiti, Aryama Singh
Our project aims to predict oxygen depletion in the Gulf of Mexico by analyzing the impact of ocean variables such as water temperature, salinity, and pH on dissolved oxygen levels, which directly influence marine health, including phytoplankton populations critical for the ecosystem. Using the NCCOS Coastal Pollutants Dataset, we employed k-nearest neighbors imputation for handling missing data and leveraged autoencoder architectures to extract latent features from the ocean variables. The best-performing model, without hidden layers, achieved high predictive accuracy (MSE of 0.0077, R² of 0.9991) when combined with XGBoost. Our SHAP analysis identified water temperature, air temperature, and barometric pressure as the most influential factors in predicting oxygen levels. These insights can help target areas at risk of oxygen depletion, guiding conservation efforts to protect marine biodiversity amidst climate change.