Data-Driven Machine Learning in Environmental Pollution: Gains and Problems
文献类型：期刊论文
| 作者 | Liu, Xian ; Lu, Dawei; Zhang, Aiqian; Liu, Qian; Jiang, Guibin
|
| 刊名 | ENVIRONMENTAL SCIENCE & TECHNOLOGY
 |
| 出版日期 | 2022-02-15 |
| 卷号 | 56期号:4页码:2124-2133 |
| ISSN号 | 0013-936X |
| 关键词 | WATER TREATMENT PLANTS PM2.5 CONCENTRATIONS N2O EMISSIONS BIG DATA MULTIVARIATE REGRESSION PREDICTION |
| 英文摘要 | The complexity and dynamics of the environment make it extremely difficult to directly predict and trace the temporal and spatial changes in pollution. In the past decade, the unprecedented accumulation of data, the development of high-performance computing power, and the rise of diverse machine learning (ML) methods provide new opportunities for environmental pollution research. The ML methodology has been used in satellite data processing to obtain ground-level concentrations of atmospheric pollutants, pollution source apportionment, and spatial distribution modeling of water pollutants. However, unlike the active practices of ML in chemical toxicity prediction, advanced algorithms such as deep neural networks in environmental process studies of pollutants are still deficient. In addition, over 40% of the environmental applications of ML go to air pollution, and its application range and acceptance in other aspects of environmental science remain to be increased. The use of ML methods to revolutionize environmental science and its problem-solving scenarios has its own challenges. Several issues should be taken into consideration, such as the tradeoff between model performance and interpretability, prerequisites of the machine learning model, model selection, and data sharing. |
| 源URL | [https://ir.rcees.ac.cn/handle/311016/47500]  |
| 专题 | 生态环境研究中心_环境化学与生态毒理学国家重点实验室 |
| 作者单位 | 1.Chinese Acad Sci, Res Ctr Ecoenvironm Sci, State Key Lab Environm Chem & Ecotoxicol, Beijing 100085, Peoples R China 2.Univ Chinese Acad Sci, Sch Environm, Hangzhou Inst Adv Study, Hangzhou 310012, Peoples R China 3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100190, Peoples R China 4.Jianghan Univ, Inst Environm & Hlth, Wuhan 430056, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Xian,Lu, Dawei,Zhang, Aiqian,et al. Data-Driven Machine Learning in Environmental Pollution: Gains and Problems[J]. ENVIRONMENTAL SCIENCE & TECHNOLOGY,2022,56(4):2124-2133. |
| APA | Liu, Xian,Lu, Dawei,Zhang, Aiqian,Liu, Qian,&Jiang, Guibin.(2022).Data-Driven Machine Learning in Environmental Pollution: Gains and Problems.ENVIRONMENTAL SCIENCE & TECHNOLOGY,56(4),2124-2133. |
| MLA | Liu, Xian,et al."Data-Driven Machine Learning in Environmental Pollution: Gains and Problems".ENVIRONMENTAL SCIENCE & TECHNOLOGY 56.4(2022):2124-2133. |
入库方式： OAI收割
来源：生态环境研究中心
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。