Balouei et al. (2025) Advanced AI, machine learning, and deep learning tools for climate studies

Identification

• Journal: Elsevier eBooks
• Year: 2025
• Date: 2025-12-06
• Authors: Fatemeh Balouei, Mostafa Kabolizadeh, Hamidreza Rabiei‐Dastjerdi
• DOI: 10.1016/b978-0-443-36396-2.00019-6

Research Groups

• Faculty of Earth Sciences, Shahid Chamran University of Ahvaz, Ahvaz, Iran
• Department of Remote Sensing and GIS, Shahid Chamran University of Ahvaz, Ahvaz, Iran
• School of History and Geography, Dublin City University, Dublin, Ireland

Short Summary

This chapter reviews advanced Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) tools for climate studies, particularly their application in addressing drought issues by integrating meteorological and remote sensing indices. It discusses the capabilities of these technologies in overcoming limitations of traditional drought monitoring and prediction methods.

Objective

• To review and discuss the application of advanced Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) tools for climate studies, specifically in addressing drought issues by integrating various drought indices.

Study Configuration

• Spatial Scale: Variable, depending on the reviewed studies, typically regional to global for drought monitoring.
• Temporal Scale: Variable, depending on the reviewed studies, from short-term drought events to long-term climate trends.

Methodology and Data

• Models used: Machine learning, Deep learning, Neural Networks (NN), Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN).
• Data sources: Meteorological station data (for SPI, SPEI indices), Remote sensing data (for primary remote sensing indices).

Main Results

As this is a chapter outline, specific quantitative results are not provided. However, the chapter is expected to demonstrate the enhanced capabilities of AI, ML, and DL techniques in improving the accuracy and efficiency of drought monitoring and prediction compared to traditional methods, integrating both ground-based and remote sensing data.

Contributions

This chapter contributes a comprehensive review and synthesis of advanced AI, machine learning, and deep learning methodologies applied to climate studies, particularly highlighting their utility and potential in overcoming limitations of traditional drought monitoring and prediction approaches by integrating diverse data sources.

Funding

Not specified in the provided chapter outline.

Citation

@article{Balouei2025Advanced,
  author = {Balouei, Fatemeh and Kabolizadeh, Mostafa and Rabiei‐Dastjerdi, Hamidreza},
  title = {Advanced AI, machine learning, and deep learning tools for climate studies},
  journal = {Elsevier eBooks},
  year = {2025},
  doi = {10.1016/b978-0-443-36396-2.00019-6},
  url = {https://doi.org/10.1016/b978-0-443-36396-2.00019-6}
}