用机器学习技术预测不同床位类型的开放道中的微塑料运输和河流调节
A Zakib Fazil1, D D S Dhawala Wijeratna1, Pattiyage I A Gomes1
1Department of Civil Engineering, Faculty of Engineering, Sri Lanka Institute of Information Technology, Sri Lanka.
Environmental pollution (Barking, Essex : 1987)
|August 2, 2025
概括
河流监管对微塑料的运输和保留有重大影响. 机器学习模型准确地预测了微塑料的运动,帮助污染管理策略.
科学领域:
- 环境科学 环境科学
- 水文学的水文学
- 环境工程 环境工程
背景情况:
- 微塑料污染是一个日益严重的全球性问题.
- 了解水生系统中的微塑料运输对于有效管理至关重要.
- 河流环境是微塑料传播的重要途径.
研究的目的:
- 研究微塑料的运输和保留在各种条件下的开放道.
- 评估河床组成和调节结构对微塑料命运的影响.
- 开发和验证用于预测微塑料行为的机器学习模型.
主要方法:
- 实验室实验模拟不同床材 (烯酸,沙子,卵石) 和调节结构 (,门) 的开放通道流动.
- 测试了三种不同密度的微塑料类型 (910-1350 kg/m3).
- 机器学习模型的应用和验证,包括决策树,随机森林,XGB和堆叠组合,使用夏普利增量扩展 (SHAP) 分析.
主要成果:
- 微塑料的保留与排放等独立变量有很弱的相关性,但强烈依赖于河流调节类型.
- 排水门保留的微塑料比堤少,允许一些微塑料类型下游逃逸.
- 机器学习模型,特别是堆叠组合,准确预测了微塑料运输 (R2=0.99,MSE=2.00),SHAP分析确定密度和床粗度是关键因素.
结论:
- 河流调节结构在微塑料保留动态中发挥着关键作用.
- 机器学习模型为河流中的微塑料运输提供了强大的预测工具.
- 这些发现支持制定有针对性的河流管理战略,以减轻微塑料污染.
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