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. Review articles, perspectives, and original research papers are welcome for submission.
Typical topics may include, but not limited to,
l 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 Solutions
l Topic 2: Artificial Intelligence System for the Precise Prediction of Structure-Property Relationship and Discovering New Materials
l Topic 3: Machine Learning for Prediction of Solvent-, Adsorption-, Membrane-based Carbon Capture, Utilization and Storage
l Topic 4: Novel Solvent, Sorbent, and Membrane Design Using Machine Learning-enabled Optimization.
l Topic 5: Machine Learning-based Tools for the Air Quality Prediction, Soil Moisture Prediction, and the Prediction of Water Resource Availability.
l Topic 6: Water Quality Prediction Using Machine Learning Methods.
Guest Editors
Dr. Yongsheng Chen, Georgia Institute of Technology, USA
Dr. Xiaonan Wang, Tsinghua University, China
Dr. Joe F. Bozeman III, Georgia Institute of Technology, USA
Dr. Shouliang Yi, U.S. Department of Energy, National Energy Technology Laboratory, USA
Schedule & Submission
Open date for submissions: March 1, 2022
Submission window close day: December 31, 2022
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.
The papers for this special issue can be submitted to FESE via:
https://mc.manuscriptcentral.com/fese
Submissions must follow the FESE “Instruction for Authors”:
https://www.springer.com/journal/11783/submission-guidelines
Please select the type of manuscript: “Special issue: Machine Learning” in the list of ongoing special issues. The special issue welcomes all types of content, including reviews, cutting-edge research, and perspectives.
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.
More details or information about this journal can be found @: https://www.springer.com/journal/11783
http://journal.hep.com.cn/fese