Naghibi et al. (2025) Assessing dust storm risks in water-scarce regions: a machine learning approach

Identification

• Journal: Elsevier eBooks
• Year: 2025
• Date: 2025-11-29
• Authors: Amir Naghibi, Hossein Hashemi, Seyed Mohsen Mousavi, Pengxiang Zhao, Ali Mansourian
• DOI: 10.1016/b978-0-443-26722-2.00012-x

Research Groups

• Centre for Advanced Middle Eastern Studies (CMES), Lund University, Lund, Sweden
• Division of Water Resources Engineering, Faculty of Engineering, Lund University, Lund, Sweden
• Department of Environmental Planning and Design, Shahid Beheshti University, Tehran, Iran
• Faculty of Science, Department of Physical Geography and Ecosystem Science, GIS Centre, Lund University, Lund, Sweden

Short Summary

This paper aims to assess dust storm risks in water-scarce regions using a machine learning approach, emphasizing the severe environmental, health, and socio-economic impacts of dust storms and the critical need for their study and mitigation.

Objective

• To assess dust storm risks in water-scarce regions by employing a machine learning approach, with the goal of defining primary dust storm sources and providing mitigation solutions to reduce damage.

Study Configuration

• Spatial Scale: Regional (focus on water-scarce regions, with examples from the Middle East, Sistan region of Iran, and West Africa).
• Temporal Scale: Not specified in the provided text.

Methodology and Data

• Models used: Machine learning approach (specific models not detailed in the provided text).
• Data sources: Not specified in the provided text.

Main Results

• Not available in the provided text, as it is the introduction chapter.

Contributions

• Proposing a machine learning approach for assessing dust storm risks, particularly in water-scarce regions, to identify sources and inform mitigation strategies.
• Synthesizing the multifaceted adverse impacts of dust storms across environmental, health, social, and economic sectors, highlighting the urgency for intervention.

Funding

• Not specified in the provided text.

Citation

@article{Naghibi2025Assessing,
  author = {Naghibi, Amir and Hashemi, Hossein and Mousavi, Seyed Mohsen and Zhao, Pengxiang and Mansourian, Ali},
  title = {Assessing dust storm risks in water-scarce regions: a machine learning approach},
  journal = {Elsevier eBooks},
  year = {2025},
  doi = {10.1016/b978-0-443-26722-2.00012-x},
  url = {https://doi.org/10.1016/b978-0-443-26722-2.00012-x}
}