Issue 4, 2020

Fast-developing machine learning support complex system research in environmental chemistry

Abstract

Complex systems and their various effects are always of concern in environmental chemistry (EC). With the increasing number of research dimensions, traditional paradigms based on methodological reductionism have failed to help us determine the accurate effects or behaviors of chemical pollutants in multi-media environments. As an adept means of handling complicated objects, artificial intelligence (AI) supported by various machine learning (ML) algorithms is one of the best ways to cope with this problem. In this perspective, we try to explain some similarities between the complex matter in EC and the AI networks and provide some suggestions for combining the two networks for EC data mining.

Graphical abstract: Fast-developing machine learning support complex system research in environmental chemistry

Article information

Article type
Perspective
Submitted
16 nov 2019
Accepted
09 dec 2019
First published
12 dec 2019

New J. Chem., 2020,44, 1179-1184

Fast-developing machine learning support complex system research in environmental chemistry

Q. Duan and J. Lee, New J. Chem., 2020, 44, 1179 DOI: 10.1039/C9NJ05717J

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements