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RFAGB模型:基于波海海域遥感数据的微塑料逆转新机器学习模型

Ao Shen¹, Yongzheng Ma¹, Yuan Li¹

¹School of Marine Science and Technology, Tianjin University, Tianjin 300072, China.

|September 2, 2025

概括

一个新的随机森林吸收梯度增强模型 (RFAGB) 提高了监测微塑料污染的遥感精度. 这种方法精确地绘制了波海的微塑料分布图,

科学领域:

环境科学
遥感技术
海洋污染的监测

背景情况:

微塑料污染是一个重要的全球环境问题,对生态系统和人类健康构成潜在风险.
传统的微塑料监测频谱方法成本高且耗时长.
远程探测提供了具有成本效益的大规模微塑料检测的潜力,但需要提高准确性.

研究的目的:

使用遥感数据开发一种精确有效的微塑料丰度逆转方法.
调查波海微塑料的空间分布情况.
确定影响微塑料变化的关键因素.

主要方法:

开发和应用一种新的随机森林吸收梯度增强模型 (RFAGB).
利用遥感数据进行微塑料丰度逆转.
分析微塑料的分布,并确定波海的高价值区域.

主要成果:

与单个机器学习模型相比,RFAGB模型在准确度上显著提高 (23%的R平方,RMSE减少67%).
莱州湾的平均微塑料含量最高 (1.06±0.48个物品/m3).
在中部的博海海域发现了两个显著的微塑料热点.

结论:

基于遥感的微塑料监测提供了更强大的稳定性和准确性.
卫星遥感技术对海洋微塑料的定期和大规模监测具有很大的前景.
污染源输入和水力动力因素可能是微塑料空间和时间变化的驱动因素.

关键词:

波海海梯度提升模型超级学习者微塑料的数量随机森林模型卫星遥感数据

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