.png){kind=icon}
返回
Machine learning: Next promising trend for microplastics study
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jenvman.2023.118756.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Microplastics (MPs), as an emerging pollutant, pose a significant threat to humans and ecosystems. However, traditional MPs characterization methods are limited by sample requirements and characterization time. Machine Learning (ML) has emerged as a vital technology for analyzing MPs pollution due to its accuracy, broad application, and powerful feature extraction. Nevertheless, environmental scientists require threshold knowledge before using ML, restricting the ML application in MPs research. Furthermore, imbalanced development of ML in MPs research is a pressing concern. In order to achieve a wide ML application in MPs research, in this review, we comprehensively discussed the size and sources of MPs datasets in relevant literature to help environmental scientists deepen their understanding of the construction of MPs datasets. Commonly used ML algorithms are analyzed from the perspective of interpretability and the need for computer facilities. Additionally, methods for improving and evaluating ML model performance, such as dataset pre-processing, model optimization, and model assessment metrics, are discussed. According to datasets and characterization techniques, MPs identification using ML was divided into three categories in this work: spectral identification, image identification, and spectral imaging identification. Finally, other applications of ML in MPs studies, including toxicity analysis, pollutants adsorption, and microbial colonization, are comprehensively discussed, which reveals the great application potential of ML. Based on the discussion above, this review suggests an algorithm selection strategy to assist researchers in selecting the most suitable ML algorithm in different situations, improving efficiency and decreasing the costs of trial and error. We believe that this work sheds light on the application of ML in MPs study.
Keyword:
Microplastics
Machine learning
Algorithm selection strategies
Model performance
期刊
IF:
8.4
论文数:
2.8W
被引数:
13.7W
机构
引用论文
An ensemble machine learning method for microplastics identification with FTIR spectrum基于FTIR光谱识别微塑料的集成机器学习方法
OAAI
OAAI分享收藏SMACC: A System for Microplastics Automatic Counting and ClassificationSMACC: 一种微塑料自动计数和分类系统.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏Customizable Machine-Learning Models for Rapid Microplastic Identification Using Raman MicroscopyANALYTICAL CHEMISTRY
ANALYTICAL CHEMISTRYIF6.7
PREAI
分享收藏Machine learning-based real-time prediction of micropollutant behaviour in forward osmosis membrane (waste)water treatment基于机器学习的正渗透膜 (废水) 处理中微污染物行为的实时预测
PREAI
分享收藏Photo aging of polypropylene microplastics in estuary water and coastal seawater: Important role of chlorine ionWATER RESEARCH
WATER RESEARCHIF12.4
PREAI
分享收藏
查看更多内容