Data-Driven Machine Learning in Environmental Pollution: Gains and Problems
Chinese Academy of Sciences · Research Center for Eco-Environmental Sciences · +2 more institutions
Abstract
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…
Citation impact
- FWCI
- 37.13
- Percentile
- 100%
- References
- 70
Authors
5- XLXian Liu
Chinese Academy of Sciences, Research Center for Eco-Environmental Sciences
- DLDawei Lü
Chinese Academy of Sciences, Research Center for Eco-Environmental Sciences
- AZAiqian ZhangCorresponding
Chinese Academy of Sciences, Jianghan University, Research Center for Eco-Environmental Sciences, University of Chinese Academy of Sciences
- QLQian LiuCorresponding
Chinese Academy of Sciences, Jianghan University, Research Center for Eco-Environmental Sciences, University of Chinese Academy of Sciences
- GJGuibin Jiang
Chinese Academy of Sciences, Research Center for Eco-Environmental Sciences, University of Chinese Academy of Sciences
Topics & keywords
- Interpretability
- Pollutant
- Pollution
- Computer science
- Environmental pollution
- Environmental science
- Process (computing)
- Environmental monitoring