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 this https URL
Abstract (translated)
海岸低氧问题，尤其是在墨西哥湾北部地区，是一个持续的生态和经济关注点。季节性模型只能提供粗略预测，无法捕捉到日常生态系统管理所需的细尺度变化。我们提出了一项研究，比较了四种深度学习架构在每日低氧分类上的表现：双向长短期记忆网络（BiLSTM）、医疗变换器（Medformer）、时空变换器（ST-Transformer）和时间卷积网络（TCN）。我们使用2009年至2020年的十二年逐日回溯数据对模型进行了训练。我们的训练数据来自于一个耦合的水动力-生物地球化学模型，同样地，我们将2020年至2024年的回溯数据作为测试数据。 我们构建了分类模型，包括水柱分层、沉积物耗氧和温度依赖性分解速率等因素。我们在相同的预处理方法、输入/输出格式以及验证协议下评估了每种架构的表现。所有模型均实现了高分类准确率和强鉴别能力，其中ST-Transformer在所有指标和测试期内表现最佳（AUC-ROC: 0.982-0.992）。此外，我们还采用了麦克内马尔检验方法来识别模型预测中的统计显著性差异。 我们的贡献在于提供了一个可重复的框架，用于操作实时低氧预测，并支持环境及海洋建模系统社区以及生态系统恢复力方面的更广泛努力。源代码可在以下链接获取：[https://this-url.com](https://this-url.com)
URL
https://arxiv.org/abs/2602.05178
