Topical Collection on "Advanced Applications of Artificial Intelligence and Machine Learning in Environmental and Earth Sciences"
Deadline for manuscript submissions: 8 October 2024
Collection Editors
Florida Atlantic University, USA
Email: mahmadi2021@fau.edu
University of Alabama, USA
Email: agholizadehlonbar@crimson.ua.edu
Dr. Morteza Rahimi
Florida International University, USA
Email: mrahi011@fiu.edu
Dr. Fahimeh Asgari
University of North Texas, USA
Email: FahimehAsgari@my.unt.edu
Dr. Saeed Asadi
University of Texas at Arlington, USA
Email: saeedasadi810@gmail.com
Dr. Masoumeh Farhadi Nia
University of Massachusetts Lowell, USA
Email: Masoumeh_FarhadiNia@uml.edu
Topical Collection Information
The rapid advancements in Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized numerous scientific fields, and their application in environmental and earth sciences is no exception. This special issue aims to explore the innovative applications of AI and ML in addressing critical environmental challenges and advancing our understanding of earth systems. The integration of AI and ML techniques offers unprecedented opportunities for enhancing predictive capabilities, optimizing resource management, and mitigating environmental risks.
This special issue invites high-quality, original research articles, reviews, and case studies that highlight the potential and practical applications of AI and ML in various domains of environmental and earth sciences. The goal is to provide a comprehensive overview of current trends, novel methodologies, and future directions in this rapidly evolving field.
The topics of interest for this special issue include, but are not limited to:
- AI-based climate models
- Predictive analytics for climate change impacts
- Machine learning applications in meteorology and climatology
- AI-driven remote sensing technologies
- Real-time environmental monitoring systems
- Predictive models for pollution control and management
- Optimization of water resources using AI
- AI applications in sustainable agriculture and forestry
- Renewable energy forecasting and optimization
- Early warning systems for natural disasters using AI
- Machine learning models for earthquake and tsunami prediction
- AI in flood risk assessment and management
- AI and ML in geospatial data analysis
- Remote sensing image classification using deep learning
- Applications of AI in geographic information systems (GIS)
- AI in wildlife monitoring and conservation
- Predictive models for ecosystem health assessment
- Machine learning applications in biodiversity studies
- AI-based methods for detecting environmental pollutants
- Machine learning in soil and water remediation strategies
- Predictive models for air and water quality management