The Global Environmental Engineers is pleased to announce a special issue focused on "Advancements in Air Quality Modeling through Machine Learning Coupling." (View Flyer)
This issue aims to integrate machine learning techniques with air quality models to enhance accuracy, efficiency, and predictive capabilities. We invite leading researchers in environmental engineering, data science, and machine learning to contribute their innovative approaches, methodologies, and case studies. Join us in shaping the future of air quality assessments!
Topics of Interest Include, but are Not Limited to:
- Development of machine learning algorithms for air quality modeling.
- Integration of satellite data and air quality models using machine learning.
- Feature selection and dimensionality reduction techniques for air quality modeling.
- Real-time air quality prediction using machine learning models.
- Uncertainty quantification and sensitivity analysis in coupled models.
- Application of deep learning and neural networks in air quality assessment.
- Hybrid modeling approaches combining physical models with machine learning.
- Case studies demonstrating the effectiveness of coupled models.
Researchers and experts in the field are encouraged to seize this opportunity to showcase their expertise and contribute to the advancement of air quality modeling. We invite you to submit your cutting-edge research to join us in shaping the future of this critical area of study.
Dr. Hosni Snoun
Numthaja Co, Ltd, Jeddah, KSA.
Dr. Moez Krichen
FCSIT, Al-Baha University, Al-Baha, KSA
Submission Deadline: October 31, 2023
Don't miss the chance to be a part of this significant advancement in air quality modeling. We look forward to receiving your valuable contributions for this Special Issue.
For more information, please visit our journal's website or email us at email@example.com.
Prof. Hongxing Dai
The Global Environmental Engineers
Beijing University of Technology, Beijing, P.R. China