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在数据稀缺的情况下,未来海洋微塑料分布的多场景模拟:深度学习方法

Bowen Cui¹, Huaiyuan Qi², Mengyang Liu³

¹State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen 361102, China; College of Ocean and Earth Sciences, Xiamen University, Xiamen 361102, China.

|July 23, 2025

概括

一个新的深度学习框架,CGMAT,准确预测海洋微塑料污染趋势. 它预测台湾海峡的水位急剧上升,挪威水域的水位逐渐上升,突出了主要的污染驱动因素.

科学领域:

环境科学环境科学
海洋生物学海洋生物学
数据科学数据科学数据科学

背景情况:

海洋微塑料 (MP) 污染对环境构成重大威胁.
由于数据稀缺和复杂的时空变化,对MP丰度的准确预测受到阻碍.
了解MP分布驱动因素对于有效的缓解策略至关重要.

研究的目的:

开发一种新的深度学习框架 (CGMAT) 来预测海洋微塑料污染.
确定影响MP时空分布的关键驱动因素.
预测各种海洋生态系统的未来MP趋势.

主要方法:

在跨域多图注意网络 (CGMAT) 中,将Few-Shot Learning (FSL) 与基于变压器的架构集成.
增强来自台湾海峡和挪威沿海水域的异质数据集.
多场景模拟和多尺度特征融合分析.

主要成果:

CGMAT表现出高性能,解释差异得分 (EVS) 为0.91,平均绝对百分比误差 (MAPE) 为0.18%.
预计MP度将在台湾海峡急剧增加 (到2030年将达到312-376颗/立方米),并在挪威水域逐渐增加 (到2031年将达到15-53颗/立方米).
MP动态是由经济干预,环境响应时间和地理因素决定的.

结论:

根据CGMAT框架,有效地解决了海洋污染模型中的数据稀缺问题.
未来的MP污染趋势显示出显著的区域差异.
该研究提供了一种广泛适用的DL方法,用于海洋生态系统管理和污染减轻.

关键词:

数据稀缺性数据稀缺性深度学习是一种深度学习.不太可能的学习策略 (FSL)微塑料是一种微塑料.多场景模拟多场景模拟变压器变压器变压器

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