Benchmarking Artificial Intelligence Models for Daily Coastal Hypoxia Forecasting

• URL: http://arxiv.org/abs/2602.05178v1
• Date: Thu, 05 Feb 2026 01:16:17 GMT
• Title: Benchmarking Artificial Intelligence Models for Daily Coastal Hypoxia Forecasting
• Authors: Magesh Rajasekaran, Md Saiful Sajol, Chris Alvin, Supratik Mukhopadhyay, Yanda Ou, Z. George Xue,
• Abstract summary: Coastal hypoxia, especially in the northern part of Gulf of Mexico, presents a persistent ecological and economic concern.<n>We present study that compares four deep learning architectures for daily hypoxia classification.<n>We constructed classification models incorporating water column stratification, sediment oxygen consumption, and temperature-dependent decomposition rates.
• Score: 0.3640438043380942
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Coastal hypoxia, especially in the northern part of Gulf of Mexico, presents a persistent ecological and economic concern. Seasonal models offer coarse forecasts that miss the fine-scale variability needed for daily, responsive ecosystem management. We present study that compares four deep learning architectures for daily hypoxia classification: Bidirectional Long Short-Term Memory (BiLSTM), Medformer (Medical Transformer), Spatio-Temporal Transformer (ST-Transformer), and Temporal Convolutional Network (TCN). We trained our models with twelve years of daily hindcast data from 2009-2020 Our training data consists of 2009-2020 hindcast data from a coupled hydrodynamic-biogeochemical model. Similarly, we use hindcast data from 2020 through 2024 as a test data. We constructed classification models incorporating water column stratification, sediment oxygen consumption, and temperature-dependent decomposition rates. We evaluated each architectures using the same data preprocessing, input/output formulation, and validation protocols. Each model achieved high classification accuracy and strong discriminative ability with ST-Transformer achieving the highest performance across all metrics and tests periods (AUC-ROC: 0.982-0.992). We also employed McNemar's method to identify statistically significant differences in model predictions. Our contribution is a reproducible framework for operational real-time hypoxia prediction that can support broader efforts in the environmental and ocean modeling systems community and in ecosystem resilience. The source code is available https://github.com/rmagesh148/hypoxia-ai/

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