Citation

Rashidi Shikhteymour S, Borji M, Bagheri-Gavkosh M, Azimi E, Collins TW. Appl. Geogr. 2023; 158: e103035.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.apgeog.2023.103035

PMID

unavailable

Abstract

Hazardous flooding occurs across most climate zones. Owing to the lack of appropriate infrastructures and applicable predictive methods, flooding in arid and semi-arid regions may be especially damaging. Based on a study of Abarkuh County, Iran, we introduce an integrated approach for identifying high-priority flood risk areas using machine learning (ML) and multi-criteria decision-making (MCDM) methods, which is transferable to other (semi)arid regions.

RESULTS indicate that among the ML models we examined--including classification and regression tree (CART), mixture discriminant analysis (MDA), and support vector machine (SVM)--the SVM model performs best. We estimate that 75% of the study area is subject to high or very flood hazard. Our application of the Jackknife technique identifies precipitation, vegetation, and drainage density as the most important conditional factors for regional flood hazards. Our analytical network process (ANP)-decision making trial and evaluation laboratory (DEMATEL) results reveal that population density and agricultural area density have the greatest influence on flood vulnerability.

RESULTS integrating SVM and ANP-DEMATEL flood hazard and vulnerability maps indicate that 6% of the study area is at high or very high flood risk. Application of this approach can assist local authorities in identifying priority areas for flood management interventions.

Language: en

Keywords

Arid and semi-arid regions; Flood; Hazard; Risk zone; Vulnerability