Assessing dust storm risks in water-scarce regions : a machine learning approach

Summary, in English

Climatic factors and water and land use management mainly impact dust storm frequency and intensity. Water scarcity and a drier climate will lead to more severe and frequent dust storms in the near and far future. This makes dust storm susceptibility a dynamic phenomenon requiring spatiotemporal analyses. Regarding the complexity of this phenomenon, this chapter discusses AI-based methods for spatiotemporal modeling of dust storm susceptibility. Those include mixture discriminant analysis (MDA) and heterogeneous discriminant analysis (HDA) algorithms. The findings depicted that the MDA and HDA algorithms modeled dust storm susceptibility with area under the receiver operating characteristic curves ranging between 0.79 and 0.87 for four dry and wet hydrological conditions periods, demonstrating robust applicability across regions facing similar environmental challenges. The findings also revealed that elevation, precipitation, wind speed, and Normalized Difference Vegetation Index (NDVI) were most important in dust storm source susceptibility. NDVI reflects human impacts through land use, land cover, or cropping-type changes. Vegetation is also affected by sociopolitical aspects in the region, such as war, land use, and water management in a transboundary context. Therefore, it is essential to consider and investigate human and natural impacts. As a key transboundary water resource, the Tigris and Euphrates River Basin is critical in mitigating dust storm risks, necessitating strong and coordinated regional governance.

• Author: Amir Naghibi; Hossein Hashemi; Seyed Mohsen Mousavi; Pengxiang Zhao; Ali Mansourian
• Department/s: Centre for Advanced Middle Eastern Studies (CMES); Division of Water Resources Engineering; MECW: The Middle East in the Contemporary World; LTH Profile Area: Water; Centre for Geographical Information Systems (GIS Centre); Dept of Physical Geography and Ecosystem Science; LU Profile Area: Nature-based future solutions; BECC: Biodiversity and Ecosystem services in a Changing Climate
• Publishing year: 2026
• Language: English
• Pages: 13-24
• Publication/Series: Water Scarcity Management : Towards the Application of Artificial Intelligence and Earth Observation Data
• Links: http://dx.doi.org/10.1016/B978-0-443-26722-2.00012-X
• Document type: Book chapter
• Publisher: Elsevier
• Topic: Meteorology and Atmospheric Sciences
• Keywords: Dust storm; heterogeneous discriminant analysis; machine learning; mixture discriminant analysis; mixture learning; Tigris and Euphrates
• Status: Published
• ISBN/ISSN/Other: ISBN: 9780443267239; ISBN: 9780443267222