用深度学习模型提升墨西哥湾每日缺氧预报精度。
Benchmarking Artificial Intelligence Models for Daily Coastal Hypoxia Forecasting
- 对比四种神经网络，用12年海洋数据训练分类模型。
- 时空变压器模型表现最佳，准确率最高达0.992（AUC-ROC）。
- 代码开源，适合环境建模与生态韧性研究者使用。
墨西哥湾北部沿海缺氧问题长期威胁生态与经济。现有季节性模型预报粗糙，难以满足每日动态管理需求。本文比较了四种深度学习架构在每日缺氧分类任务中的表现：双向长短期记忆网络（BiLSTM）、Medformer（医学变换器）、时空变换器（ST-Transformer）和时序卷积网络（TCN）。模型基于2009–2020年耦合水动力-生物地球化学模型的逐日回算数据进行训练，并以2020–2024年数据作为测试集。模型融合了水体分层、沉积物耗氧及温度依赖分解速率等关键因子。所有模型均实现高分类准确率与强判别能力，其中ST-Transformer在所有评估指标与时间段中表现最优，AUC-ROC达0.982–0.992。通过McNemar检验验证了模型预测差异的统计显著性。本研究构建了一个可复现的实时缺氧预测框架，有助于推动环境与海洋建模领域的生态系统韧性建设。源代码已公开于https://github.com/rmagesh148/hypoxia-ai/
原文摘要 · Abstract (English)
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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