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

Abstract

This study assessed landslide susceptibility in Shahpur valley, situated in the eastern Hindu Kush. Here, landslides are recurrent phenomena that disrupt the natural environment, and almost every year, they cause huge property damages and human losses. These damages are expected to escalate in the study area due to the high rate of deforestation in the region, population growth, agricultural expansion, and infrastructural development on the slopes. Landslide susceptibility was assessed by applying “weight of evidence” (WoE) and “information value” (IV) models. For this, the past landslide areas were identified and mapped on the SPOT5 satellite image and were 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, and proximity to roads and streams were identified based on indigenous knowledge and studied scientific literature. The relationship of landslide occurrence with contributing factors was calculated using WoE and IV models. The susceptibility maps were generated based on both the WoE and IV models. The results showed that the very high susceptible zone covered an area of 14.49% and 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 at 80.34% for WoE and 80.13% for the IV model. The calculated prediction rate for both WoE and IV 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 the very high LS zone was 29.64% in the WoE model, and it was 31.21% for the IV model. Based on the elements at risk, a 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 the landslide risk map, 5.5% of the study area was under high risk, while 2% of the area was in a 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.

Spatial implementation of frequency ratio, statistical index and index of entropy models for landslide susceptibility mapping in Al-Balouta river basin, Tartous Governorate, Syria

“…In the context of SCAI values, higher values in very low susceptibility and lower values in very high susceptibility indicate higher reliability of the model (Rahman et al 2022). Although the FR model achieved the best reliability according to the SCAI results (Table 5), the other two models also showed a good capability in determining landslide susceptibility in the study area.…”

“…The frequency ratio is one of the well-known and widely used BSM which have been frequently applied for hazard susceptibility mappings, such as flood and landslide (Ali et al 2020;Kincal and Kayhan 2022). FR is a widely utilized method for understanding the potential linking between current landslide events and causative geofactors (Rahman et al 2022). However, FR can be defined through a spatial relationship between dependent and independent variables, where landslide inventory is the dependent variable and landslide conditioning factors are the independent variable.…”

“…Landslides are among the greatest significant issues that governments strive to reduce their destructive impacts on lives, properties and infrastructure, especially in mountainous areas (Dikshit et al 2020;Nakileza and Nedala 2020;Batar and Watanabe 2021;Rahman et al 2022). Many studies confirmed that increasing the landslide events are motivated by the spatial integration between physical and anthropological factors (Razavizadeh et al 2017;Akinci and Yavuz Ozalp 2021;Yamusa et al 2022;Jaafari et al 2022).…”

“…Recently, scientists throughout the world have used many approaches integrated with geographic information systems (GIS) to map landslide susceptibility, including weight of evidence (WoE; Cao et al 2021); evidential belief functions (EBF; Anis et al 2019); multivariate logistic regression model (Li et al 2021a, b;Castro-Miguel et al 2022), information value (IV) and frequency ratio (FR; Rahman et al 2022); generalized additive model (Lin et al 2021); analytical hierarchy process (AHP; Kumar and Anbalagan 2016;Babitha et al 2022); support vector machine (SVM; Naceur et al 2022); generalized additive model (GAM; Chen et al 2017a); digital elevation model and hazard index (Hamza and Raghuvanshi 2017); multicriteria decision analysis (MCDA; Pham et al 2021a).…”

Application of Vertical Electrical Sounding for Subsurface Characterization to Determineslope Instability at Perizie, Nagaland

“…Due to the saturation, besides an increase in pore water pressure, the weight of the weathered materials also increases, disturbing the balance of the slopes and thereby causing instability of the slope materials. [24][25][26][27][28][29] The clay-bearing rocks exhibiting low resistivity on the other hand are the least permeable and considered a waterproof layer, which is capable of acting as the probable layer for the initiation and planeof movement of the weathered materials. This acts as one of the primary factors for the cause and further development of landslides.…”