GIS-based flood susceptibility mapping using bivariate statistical model in Swat River Basin, Eastern Hindukush region, Pakistan

Zahid Ur Rahman, Waheed Ullah, Shibiao Bai, Safi Ullah, Mushtaq Ahmad Jan, Mohsin Khan, Muhammad Tayyab

Research output: Contribution to journalArticlepeer-review


Frequent flooding can greatly jeopardize local people’s lives, properties, agriculture, economy, etc. The Swat River Basin (SRB), in the eastern Hindukush region of Pakistan, is a major flood-prone basin with a long history of devastating floods and substantial socioeconomic and physical damages. Here we produced a flood susceptibility map of the SRB, using the frequency ratio (FR) bivariate statistical model. A database was created that comprised flood inventory as a dependent variable and causative factors of the flood (slope, elevation, curvature, drainage density, topographic wetness index, stream power index, land use land cover, normalized difference vegetation index, and rainfall) as independent variables and the association between them were quantified. Data were collected using remote sensing sources, field surveys, and available literature, and all the studied variables were resampled to 30 m resolution and spatially distributed. The results show that about 26% of areas are very high and highly susceptible to flooding, 19% are moderate, whereas 55% are low and very low susceptible to flood in the SRB. Overall, the southern areas of the SRB were highly susceptible compared to their northern counterparts, while slope, elevation, and curvature were vital factors in flood susceptibility. Our model’s success and prediction rates were 91.6% and 90.3%, respectively, based on the ROC (receiver operating characteristic) curve. The findings of this study will lead to better management and control of flood risk in the SRB region. The study’s findings can assist the decision-makers to make appropriate sustainable management strategies for the mitigation of future damage in the study region.
Original languageEnglish (US)
JournalFrontiers in Environmental Science
StatePublished - Jul 6 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-07-28
Acknowledgements: This work was supported by the National Natural Science Foundation of China (Grant Nos 41941017 and 41877522). The authors acknowledge the National Disaster Management Authority (NDMA), Pakistan, and Provincial Disaster Management Authority (PDMA), Khyber Pakhtunkhwa, Pakistan for providing historical and spatial data on flood damages. The authors acknowledge Rabdan Academy, UAE for supporting Article Processing Charges (APC). The authors are thankful to the National Aeronautics and Space Administration (NASA) for TRMM data, Environmental Systems Research Institute (ESRI) for land use land/cover data, and the United States Geological Survey (USGS) for Landsat 8 images, and DEM.


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