AUTHOR=Islam Fakhrul , Riaz Salma , Ghaffar Bushra , Tariq Aqil , Shah Safeer Ullah , Nawaz Muhammad , Hussain Mian Luqman , Amin Naz Ul , Li Qingting , Lu Linlin , Shah Munawar , Aslam Muhammad 

TITLE=Landslide susceptibility mapping (LSM) of Swat District, Hindu Kush Himalayan region of Pakistan, using GIS-based bivariate modeling

JOURNAL=Frontiers in Environmental Science

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2022.1027423

DOI=10.3389/fenvs.2022.1027423

ISSN=2296-665X

ABSTRACT=Landslide is a recurrent environmental hazard in the hilly region and affects the socioeconomic development in Pakistan. The current study area is the tourism and hydro energy hub of Pakistan but affected by environmental hazard. The applicable technique is required to produce a Landslide Susceptibility Map (LSM) of the Hindu Kush Himalayan, Swat District, Pakistan area to reduce the demographic loss of landslides. Therefore, the current study is conducted to apply three bivariate models, including Weights of Evidence (WOE), Frequency Ratio (FR), and Information Value (IV) for LSM, which have not been explored and applied in the current study area. For this purpose, first, an inventory map of 495 landslides was constructed from both ground and satellite data and randomly divided into training (70%) and testing (30%) datasets. Furthermore, 10 pre-disposing factors maps (elevation, slope, aspect, curvature, fault, rainfall, Land Use Land Cover (LULC), lithology, road, drainage) of landslide were prepared in ArcGIS 10.8. Finally, LSM is generated based on WOE, FR, and IV models and validated the performance of LSM models using the Area Under Receiver Operating Characteristic Curve (AUROC). The Success Rate Curve (SRC) of the WOE, FR, and IV models are 0.67, 0.93, and 0.64, respectively, while the Prediction Rate Curve (PRC) of the three models are 0.87, 0.95, and 0.73, respectively. The validation results of WOE, FR, and IV justified that the FR model is the most reliable technique among all three mentioned models to produce the highest accuracy LSM for the present study area. Policymakers can use the results of current research work to mitigate the loss of landslide hazards.