Machine learning is a subfield of artificial intelligence (AI), which is the study of computer algorithms that can automatically learn and improve through experience and by the use of data. Machine learning algorithms can manage complex, multidimensional datasets with the powerful fitting ability and have obtained increasing attention in the community of environmental science and engineering. The development of big data analysis approaches, including machine learning, has been indispensable in the area where traditional data analysis methods face limitations or challenges over the past decades. It could foresee that AI, including machine learning, will play a significant role in achieving carbon neutrality and the sustainable development goals. This special issue will address the latest research progress of machine learning to revolutionize data analysis and modeling in the frontiers of the environmental science and engineering, and cover the critical aspects needed for such applications.
客座编辑/Guest Editors
Dr. Yongsheng Chen
Georgia Institute of Technology, USA
Dr. Xiaonan Wang
Tsinghua University, China
Dr. Joe F. Bozeman
Georgia Institute of Technology, USA
Dr. Shouliang Yi
U.S. Department of Energy, National Energy Technology Laboratory, USA
收稿范围/Topics
本专刊接受多种文章体裁投稿，包括综述、前沿研究、展望等。
The special issue welcomes all types of content, including reviews, cutting-edge research, and perspectives.
Typical topics may include, but not limited to,Topic 1: Application of Data-driven Machine Learning Approaches for Waste-to-energy Conversion, Municipal Solid-Waste Treatment, and the Simulation of Heavy Metals in Contaminated Soil, Water Bodies and Removal from Aqueous SolutionsTopic 2: Artificial Intelligence System for the Precise Prediction of Structure-Property Relationship and Discovering New MaterialsTopic 3: Machine Learning for Prediction of Solvent-, Adsorption-, Membrane-based Carbon Capture, Utilization and StorageTopic 4: Novel Solvent, Sorbent, and Membrane Design Using Machine Learning-enabled Optimization.Topic 5: Machine Learning-based Tools for the Air Quality Prediction, Soil Moisture Prediction, and the Prediction of Water Resource Availability.Topic 6: Water Quality Prediction Using Machine Learning Methods.
时间节点/Time windows
开放投稿：2022年3月1日投稿截止：2022年12月31日
考虑到本领域研究的耗时，如需更多准备时间，请及时联系Dr. Zhang。
Considering the time-consuming situation of the research in this field, please feel free to send an email to Dr. Zhang (jiaozhang@tsinghua.edu.cn) if you need more time before your submission.
FESE采用连续出版模式，被接收的文章将在第一时间网络发表。
After a positive review and peer reviewers’ approval, the accepted articles will be published immediately in the online Special Issue of FESE that builds up until the expected publication.