AUTHOR=Rahman Zahid Ur , Ullah Waheed , Bai Shibiao , Ullah Safi , Jan Mushtaq Ahmad , Khan Mohsin , Tayyab Muhammad TITLE=GIS-based flood susceptibility mapping using bivariate statistical model in Swat River Basin, Eastern Hindukush region, Pakistan JOURNAL=Frontiers in Environmental Science VOLUME=11 YEAR=2023 URL=https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2023.1178540 DOI=10.3389/fenvs.2023.1178540 ISSN=2296-665X ABSTRACT=

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.