整合非目标分析和机器学习:污染源识别的框架

IF 11.4 1区 工程技术 Q1 ENGINEERING, CHEMICAL
Peng Liu, Ding Pan, Xin-Yi Jiao, Ji-Ning Liu, Peng-Hui Du, Peng-Cheng Li, Meng-Zhu Xue, Yan-Chao Jin, Cai-Shan Wang, Xue-Rong Wang, Ying-Zhi Ding, Guang-Ning Zhu, Jing-Hao Yang, Wen-Ze Wu, Lu-Feng Liang, Xin-Hui Liu, Li-Ping Li
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引用次数: 0

摘要

基于机器学习的非目标分析(ML-based NTA)面临着将复杂化学信号与污染源联系起来的关键挑战。本文提出了一个用于污染源识别的机器学习辅助NTA的系统框架,强调了数据处理、模式识别和模型验证中的关键步骤的策略和考虑。该框架为将原始NTA数据转化为支持知情决策的可操作环境见解提供了实用指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Integrating non-target analysis and machine learning: a framework for contaminant source identification

Integrating non-target analysis and machine learning: a framework for contaminant source identification

Machine learning-based non-target analysis (ML-based NTA) faces the critical challenge of linking complex chemical signals to contamination sources. This review proposes a systematic framework of ML-assisted NTA for contaminant source identification, emphasizing the strategies and considerations of key steps in data processing, pattern recognition, and model validation. The framework provides practical guidance for translating raw NTA data to actionable environmental insights that support informed decision-making.

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来源期刊
npj Clean Water
npj Clean Water Environmental Science-Water Science and Technology
CiteScore
15.30
自引率
2.60%
发文量
61
审稿时长
5 weeks
期刊介绍: npj Clean Water publishes high-quality papers that report cutting-edge science, technology, applications, policies, and societal issues contributing to a more sustainable supply of clean water. The journal's publications may also support and accelerate the achievement of Sustainable Development Goal 6, which focuses on clean water and sanitation.
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