Event Details
Machine-learning Framework to Identify and Validate Biochemical Regime Clusters in the Global Blue Carbon Ecosystem
Presenter: Bhan Singh
Supervisor:
Date: Thu, December 4, 2025
Time: 10:00:00 - 00:00:00
Place: Zoom - see below.
ABSTRACT
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Abstract: The ocean plays a dual role in the Earth’s climate system, as a stabilizing reservoir of heat and carbon, and as an early indicator of ecological stress. Its changing state emerges from complex interactions among physical, chemical, and biological drivers, making traditional region-based assessments insufficient for capturing true environmental variability. This thesis develops a machine-learning framework that allows global in-situ observations from the World Ocean Database (WOD) to self-define the ocean’s natural biogeochemical regimes. After rigorous preprocessing and hierarchical spatio-temporal imputation, multiple clustering algorithms were used to uncover coherent patterns in the data, followed by supervised classification models to evaluate regime separability and interpret key drivers. The analysis identifies five distinct and ecologically meaningful regimes: productive coastal upwellings, oligotrophic gyres, polar waters, oxygen-minimum zones, and transitional open-ocean systems, reflecting the ocean’s intrinsic organization. By integrating unsupervised discovery with supervised validation, this work demonstrates how global ocean observations can be transformed into quantitative, interpretable indicators of ocean health, contributing to the broader vision of a data-driven digital twin ocean.