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微塑料阻力和沉速度的新建模拟方法

Shicheng Li¹, Xin Ma²

¹Department of Civil and Architectural Engineering, KTH Royal Institute of Technology, 10044, Stockholm, Sweden.

Journal of environmental management

|November 23, 2024

概括

本研究引入了一种机器学习框架,用于预测微塑料 (MP) 沉积. 它比传统方法提供更快,更准确的阻力和速度模型,有助于环境管理.

科学领域:

环境科学环境科学
流体动力学流体动力学
计算科学计算科学

背景情况:

微塑料 (MP) 的运输和沉积在水生环境中对于环境管理至关重要.
现有的模型在预测MP行为时往往缺乏准确性和效率.
需要新的计算方法来提高我们对MP动态的理解.

研究的目的:

开发一种新的机器学习 (ML) 框架,用于准确和可解释的微塑料 (MP) 阻力和速度模型.
为了提高MP在各种MP类型 (1D,2D,3D和混合) 中的MP定位行为预测.
为传统建模方法提供更有效,更准确的替代方案.

主要方法:

利用机器学习技术为MPs创建拖动和速度模型.
在各种MP形状和类型中验证了框架的预测准确性.
进行了灵敏度分析,以确定影响MP定位的关键参数.

主要成果:

实现了高预测准确度,阻力模型的R平方值为0.86-0.95,速度模型为0.92-0.95.
与实证方法 (59%的RMSE) 和符号回归 (18% - 27%) 相比,证明了显著的错误减少.
确定了相对密度差异和无维直径作为关键定位预测指标,形状参数因MP类型而异.

结论:

该ML框架提供了微塑料沉动态的准确和有效的预测.
这种更好的理解可以为有针对性的缓解策略提供信息,以减少环境MP的影响.
该研究强调了MP运输建模中特定物理参数的重要性.

关键词:

拖动系数拖动系数微塑料是一种微塑料.多基因遗传编程多基因遗传编程巴雷托优化的优化.终端速度终端速度

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