整合基于物理的建模和深度学习,在海洋中进行高分辨率的垂直叶绿素a预测
Xun Zhang1, Miao Hu1, Xiulin Geng1
1College of Communication Engineering, Hangzhou Dianzi University, Hangzhou, 310018, China.
Marine pollution bulletin
|March 6, 2026
概括
准确的海洋叶绿素预测对海洋生态系统至关重要. 结合光学分析和深度学习的新混合模型改善了垂直分布预测,增强了我们对海洋碳循环的理解.
科学领域:
- 海洋学 海洋学 海洋学
- 海洋生物地质化学海洋生物地质化学
- 环境科学中的人工智能
背景情况:
- 准确预测海洋叶绿素的垂直分布对于海洋生态系统结构和碳循环至关重要.
- 传统的方法,如Argo浮标和遥感,在垂直分辨率和云干扰方面存在局限性.
- 现有的物理和深度学习模型由于理想化的假设或数据要求,在复杂的海洋环境中难以稳定.
研究的目的:
- 开发和验证一个混合框架来预测海洋叶绿素的垂直分布.
- 将一个主动-被动的融合水柱光学配置模型与深度学习架构集成.
- 使用贝叶斯模型组合方法来提高预测准确度.
主要方法:
- 一个主动-被动的融合水柱光学轮模型的实施.
- 深度学习架构的应用:变压器,长短期记忆 (LSTM) 和卷积神经网络 (CNN).
- 纳入贝叶斯模型组合策略以整合多个模型预测.
主要成果:
- 积极-被动融合模型产生了与光学特性和验证数据 (MAE:0.0230.207 mg/m3) 一致的叶绿素-a 配置文件.
- 深度学习模型表现出高的预测能力 (R2高达0.97),有效地捕捉了垂直的叶绿素-a变异性.
- 贝叶斯模型组合改善了预测,将印度洋的MAE从0.0326降低到0.0289 mg/m3.
结论:
- 拟议的混合框架为预测海洋叶绿素的垂直分布提供了一种强大而可靠的方法.
- 光学分析,深度学习和贝叶斯方法的整合克服了传统方法的局限性.
- 该框架为海洋生态系统和碳循环研究提供了必要的准确,物理可解释的数据.
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