数据驱动的机器学习提高了硫胺抗生素在水相中的生物炭吸附的预测
Mudi Zhai1, Bomin Fu2, Zhaozhong Wu3
1College of Environmental Science and Engineering, Key Laboratory of Yangtze River Water Environment, Ministry of Education, Tongji University, Siping Rd 1239, Shanghai 200092, China.
Bioresource technology
|June 6, 2025
概括
机器学习模型现在可以预测水中的生物炭对硫胺抗生素的吸附. 沃斯斯坦生成对抗网络 (WGAN) 提高了对这些关键环境污染物的预测准确性.
科学领域:
- 环境化学环境化学
- 吸附科学 吸附科学
- 机器学习应用 机器学习应用
背景情况:
- 硫胺抗生素 (SAs) 对水生生态系统构成环境风险.
- 生物炭 (BCs) 是有效的吸附剂,可以从水中去除SAS.
- 需要预测模型来评估BCs的SA吸附.
研究的目的:
- 开发一个数据驱动的机器学习 (ML) 模型,用于预测水相中的BCs上的SA吸附.
- 使用瓦斯斯坦生成对抗网络 (WGAN) 进行数据增强以提高模型性能.
主要方法:
- 采用了一种机器学习方法,将Wasserstein生成对抗网络 (WGAN) 纳入数据增强.
- 应用极端梯度增强模型来预测硫胺抗生素在生物炭上的吸附.
- 验证了生成的虚拟数据与原始吸附数据集的相似性.
主要成果:
- 该WGAN成功生成了与原始数据集非常相似的虚拟吸附数据.
- 使用WGAN进行数据增强显著提高了极端梯度增强模型的性能.
- 在预测硫胺抗生素的吸附量时,可以获得更高的模型准确度.
结论:
- 这项研究证明了WGAN数据增强对改善基于ML的BCsSA吸附的ML预测的有效性.
- 开发的ML方法为预测基于废物的BCs对SAS的吸附行为提供了一种新的方法.
- 为评估和管理水生系统中硫胺抗生素相关的环境风险提供了宝贵的见解.
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