从模型开发到缓解:用于预测和最小化水处理中的三甲的机器学习
Md Mahjib Hossain1, Rabbi Sikder1, Guanghui Hua2
1Department of Civil and Environmental Engineering, South Dakota School of Mines and Technology, Rapid City, South Dakota 57701, United States.
Environmental science & technology
|June 3, 2025
概括
机器学习可以准确地预测饮用水中的三甲 (I-THM). 这种方法简化了缓解策略,通过识别关键因素和优化消毒剂剂量,导致更安全的水处理.
科学领域:
- 环境化学环境化学
- 水处理技术水处理技术
- 计算化学计算化学
背景情况:
- 消毒副产品 (DBPs),特别是三甲 (I-THMs),是水处理中的健康问题.
- 由于水质参数,消毒剂和来源的相互作用,预测I-THM形成是复杂的.
研究的目的:
- 开发和验证用于预测I-THM形成的机器学习模型.
- 确定影响I-THM生成的关键因素,并提出缓解策略.
主要方法:
- 使用了来自已发表研究的1534个样本的数据集.
- 评估了五个整体机器学习模型,其中CatBoost回归显示出最佳性能.
- 整合了特定域的特征,并使用递归特征消除来简化模型.
主要成果:
- 在外部验证数据上,CatBoost模型获得了0.67的R2,证明了强大的通用性.
- 确定了关键预测因素和缓解策略,包括尽量减少,化物,/DOC,UV254和SUVA.
- 用增量和贝叶斯优化优化剂量以最小化I-THM.
结论:
- 机器学习是预测和减轻水处理中的I-THM的强大工具.
- 开发的模型提供了可行的策略,以提高饮用水的安全性和减少实验努力.
关键词:
功能工程的特点工程.离子对比介质 (ICM) 是一种有离子的对比介质.含的消毒副产品 (I-DBPs) 是一种消毒副产品.三甲基 (I-THM) 是一种有的三甲基.机器学习 (ML) 是指机器学习.更多相关视频
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