Special Issue "Recent Advances and Future Prospects of Machine Learning in Predictive Modeling of Atmospheric Sciences"

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: 30 September 2021.

Special Issue Editors

Dr. Sunil Kumar Jha
Website
Guest Editor
1. Nanjing University of Information Science and Technology, Ningliu Road, Nanjing, Jiangsu Province, P.R.C. 210044, Nanjing, Jiangsu, China
2. University of Information Technology and Management, Sucharskiego 2, 35-225 Rzeszów, Poland
Interests: pattern recognition; machine learning; atmospheric data mining; remote sensing; atmospheric data prediction
Dr. Xiaorui Zhang
Website
Guest Editor
Nanjing University of Information Science & Technology, No.219, Ningliu Road, Nanjing, Jiangsu, China
Interests: Human-Computer Interaction; Virtual Reality Technology
Dr. Limao Zhang
Website
Guest Editor
School of Civil and Environmental Engineering, Nanyang Technological University, N1-01a-29, 50 Nanyang Avenue 639798, Singapore
Interests: artificial intelligence; tunnelling excavation; bim data analytics; structural health monitoring; data-driven simulation; uncertainty modelling and risk analysis; decision support systems
Dr. Nilesh Patel
Website
Guest Editor
Oakland University, 318 Meadow Brook Rd, Rochester, MI 48309, USA

Special Issue Information

Dear Colleagues,

In recent years, machine learning (ML) algorithms have been widely used in predictive modeling of different research domains of science and engineering. ML algorithms design an automated, accurate, and robust decision-making system by extracting the meaningful conclusion from the observations. ML algorithms have been implemented successfully in predictive modeling applications of atmospheric sciences in past research, such as circulation pattern classification, risk assessment of atmospheric emissions, atmospheric rive forecast, prediction of geothermal heat flux, diagnosis of cold atmospheric plasma sources, turbulence forecasting, hazard assessment, and forecasting of rainfall changes, etc. Due to the growing demand for ML in most of the aspect of our life, it is sensible to use it in enhancing the prediction efficiency in predictive modeling of atmospheric sciences research and applications. Extracting the meaningful conclusion by using the advanced ML approaches in the analysis of the experimental and simulated observations of atmospheric phenomena is the demanding research at present. With this objective, the present issue invites researchers to submit their novel and unpublished research related to the current advancement of ML in predictive modeling research and applications of atmospheric sciences. The present issue will cover a broad range of topics related to applications of ML approaches in the analysis of atmospheric data with the following subtopics.

  • Climate change modeling using machine learning
  • Machine learning in meteorology and hydrology applications
  • Role of machine learning in renewable energy
  • Analysis of data of atmospheric events in following subtopics but not limited
    • Data assimilation
    • Missing value imputation, preprocessing, and denoising
    • Outlier detection and removal
    • Feature extraction and selection
    • Classification and clustering
    • Simulation, modeling, and optimization
    • Reliability analysis
  • Big data in atmospheric sciences and its analysis
  • Transfer and deep learning in predictive modeling in atmospheric sciences
  • Intelligent forecasting in atmospheric sciences
  • Reinforcement and ensemble learning uses in atmospheric sciences
  • Predictive modeling in atmospheric sciences using evolutionary approaches
  • Hybrid ML approaches in efficient modeling of events of atmospheric sciences
  • Other advanced ML approaches and tools in atmospheric data modeling and applications.

Dr. Sunil Kumar Jha
Dr. Xiaorui Zhang
Dr. Limao Zhang
Dr. Nilesh Patel
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • atmospheric sciences
  • predictive modelling
  • atmospheric data mining
  • intelligent forecasting

Published Papers

This special issue is now open for submission.
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