Characterizing Environmental Extremes Using Machine Learning

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This project developed a machine learning-based method to identify environmental extremes by segmenting data into regions with consistent conditions (e.g., temperature, oxygen, acidity). Extremes were defined based on historical variability within each region, enabling more targeted and context-aware analysis of environmental changes.