AUTHOR=Rahman Ghani , Bacha Alam Sher , Ul Moazzam Muhammad Farhan , Rahman Atta Ur , Mahmood Shakeel , Almohamad Hussein , Al Dughairi Ahmed Abdullah , Al-Mutiry Motrih , Alrasheedi Mona , Abdo Hazem Ghassan 

TITLE=Assessment of landslide susceptibility, exposure, vulnerability, and risk in shahpur valley, eastern hindu kush

JOURNAL=Frontiers in Earth Science

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2022.953627

DOI=10.3389/feart.2022.953627

ISSN=2296-6463

ABSTRACT=This study assessed the landslide susceptibility in Shahpur valley, situated in the eastern Hindu Kush. Here, the landslides are recurrent phenomena that disrupt the natural environment, and almost every year it causes huge property damages and human losses. Landslide susceptibility was assessed by applying Weight of Evidence and (WoE) and Information Value (IV) models. For this, the past landslide areas were identified and mapped on SPOT5 satellite image and was verified from frequent field visits to remove the ambiguities from the initial inventory. Seven landslide contributing factors including surface geology, fault lines, slope aspect and gradient, land use, proximity to roads and streams were identified based on indigenous knowledge and studied scientific literature. The relationship of landslide occurrence with contributing factors were calculated using WoE and IV models. The susceptibility maps were generated based on both WoE and IV models. The results showed that the very high susceptible zone covered an area of 14.49%, 12.84% according to the WoE and IV models respectively. Finally, the resultant maps were validated using the success and prediction rate curves, Seed Cell Area Index (SCAI) and R-index approaches. The success rate curve validated the results 80.34% for WoE and 80.13% for VI model. The calculated prediction rate for both WoE and VI was 83.34% and 85.13%, respectively. The SCAI results showed similar performance of both models in landslide susceptibility mapping. The result shows that the R-index value for very high LS zone was 29.64% in the WoE model and it was 31.21% for IV model. Based on elements at risk, the landslide vulnerability map was prepared that showed high vulnerability to landslide hazards in the lower parts of the valley. Similarly, the hazard and vulnerability maps were combined and the risk map of the study area was generated. According to landslide risk map, the 5.5% of the study area was under high risk while 2% area was in very high-risk zone. It was found from the analysis that for assessing landslide susceptibility both the models are suitable and applicable in the Hindu Kush region.