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基于机器学习的自动化模型用于预测和评估建筑湿地的抗生素去除

Hongxu Bao¹, Wanxin Yin², Hongcheng Wang³

¹College of the Environment, Liaoning University, Shenyang 110036, China; State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China.

Bioresource technology

|July 3, 2023

概括

自动机器学习模型准确地预测了建筑湿地 (CWs) 中的抗生素去除. 基板类型显著影响移除效率,为优化CW操作提供了洞察力.

科学领域:

环境工程环境工程
水处理技术水处理技术
机器学习应用机器学习应用

背景情况:

建筑湿地 (CWs) 对于从废水中去除抗生素至关重要.
优化CW操作对于有效的抗生素去除至关重要.
目前的建模方法难以捕捉所涉及的复杂生化过程.

研究的目的:

开发强大的机器学习模型,用于预测CW中的抗生素去除.
确定影响抗生素去除性能的关键操作变量.
用数据驱动的洞察力为优化CW流程提供框架.

主要方法:

两个自动化机器学习 (AutoML) 模型被开发和训练在CW操作数据上.
模型的性能被用像平均绝对误差和确定系数这样的指标来评估.
使用了可解释的AI技术,包括变量重要性和沙普利增量解释.

主要成果:

自动ML模型在不同的训练数据集大小中表现出强大的抗生素去除预测性能.
基质类型被确定为比影响废水质量或工厂类型更重要的因素.
这些模型成功地预测了抗生素的去除,而不需要在操作调整中进行人类干预.

结论:

自动机器学习为预测和优化建筑湿地的抗生素去除提供了一个强大的工具.
了解操作变量的影响,特别是基板类型,是提高CW效率的关键.
这项研究为CW系统的数据驱动操作调整提供了宝贵的参考,以改善抗生素去除.

关键词:

可以解释的分析分析.关键变量是关键变量.分区策略分区策略.强大的建模方法.

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