Mapping dust storms and drought susceptibility: a multihazard approach for reducing infrastructure risk

Soheila Pouyan (), Mojgan Bordbar () and Hamid Reza Pourghasemi ()
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Soheila Pouyan: Shiraz University
Mojgan Bordbar: University of Campania “Luigi Vanvitelli”
Hamid Reza Pourghasemi: Shiraz University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 18, No 29, 21505-21529

Abstract: Abstract Developing multihazard susceptibility maps is a practical approach for improving environmental planning and supporting crisis management efforts. In this study, the Khuzestan Province in Iran was selected as the case area to evaluate susceptibility to two major environmental hazards: dust storms and drought. First, a dust storm susceptibility map was generated using four machine-learning algorithms: support vector machine (SVM), boosted regression tree (BRT), random forest (RF), and maximum entropy (MaxEnt). Second, a drought susceptibility map was created based on the standardized precipitation index (SPI). These two maps were then combined to produce a multihazard susceptibility map that reflected the spatial overlap of both hazards. Among the models, the RF algorithm yielded the highest predictive performance with an area under the curve (AUC) value of 0.94. The results showed that the southern and southwestern areas of the province were the most susceptible to dust storms. For drought, the SPI results indicated that the southern, southwestern, and western parts of the province experienced drought conditions, whereas the eastern and northwestern regions were more humid. In the final stage, the susceptibility of urban infrastructure and agricultural land to these two hazards was assessed. The findings revealed that 29.88% of industrial zones and 8.89% of residential areas fell within the “very high” susceptibility class for dust storms. The outcomes of this study offer valuable input for crisis planning and land management, providing a province-scale multihazard framework aimed at minimizing potential environmental stress on infrastructure.

Keywords: Machine learning; Natural hazards; Khuzestan Province; Random forest (RF); Two-susceptibility maps (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11069-025-07650-0

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