A Comparative Study of Statistics-Based Landslide Susceptibility Models: A Case Study of the Region Affected by the Gorkha Earthquake in Nepal

Abstract

As a result of the Gorkha earthquake in 2015, about 9000 people lost their lives and many more were injured. Most of these losses were caused by earthquake-induced landslides. Sustainable planning and decision-making are required to reduce the losses caused by earthquakes and related hazards. The use of remote sensing and geographic information systems (GIS) for landslide susceptibility mapping can help planning authorities to prepare for and mitigate the consequences of future hazards. In this study, we developed landslide susceptibility maps using GIS-based statistical models at the regional level in central Nepal. Our study area included the districts affected by landslides after the Gorkha earthquake and its aftershocks. We used the 23,439 landslide locations obtained from high-resolution satellite imagery to evaluate the differences in landslide susceptibility using analytical hierarchy process (AHP), frequency ratio (FR) and hybrid spatial multi-criteria evaluation (SMCE) models. The nine landslide conditioning factors of lithology, land cover, precipitation, slope, aspect, elevation, distance to roads, distance to drainage and distance to faults were used as the input data for the applied landslide susceptibility mapping (LSM) models. The spatial correlation of landslides and these factors were identified using GIS-based statistical models. We divided the inventory into data used for training the statistical models (70%) and data used for validation (30%). Receiver operating characteristics (ROC) and the relative landslide density index (R-index) were used to validate the results. The area under the curve (AUC) values obtained from the ROC approach for AHP, FR and hybrid SMCE were 0.902, 0.905 and 0.91, respectively. The index of relative landslide density, R-index, values in sample datasets of AHP, FR and hybrid SMCE maps were 53%, 58% and 59% for the very high hazard classes. The final susceptibility results will be beneficial for regional planning and sustainable hazard mitigation.

Impact of Spatial Resolution of Digital Elevation Model on Landslide Susceptibility Mapping: A Case Study in Kullu Valley, Himalayas

“…Distance to roads is significant for landslide occurrence [4]. The factor was divided into four buffers zones, which signifies the influence of landslides caused by roads.…”

“…where ni is the percentage of the area of landslides in each susceptible class and Ni is the percentage of the landslide in each susceptible class [4]. To find the areas that are exposed to landslide susceptibility is critical for spatial managers in order to plan for sustainable risk management and mitigation measures for landslide impacts.…”

“…One of the most geodynamically active domains is the Himalayas, which is highly susceptible to landslides and other natural hazards [3]. The study area is in the middle Himalayas, positioned in the intercontinental collision zone where the Indian plate is subducting under the Eurasian plate [4]. The Himalayan orogeny that is tectonically the most dynamic mountainous region across the globe is highly vulnerable to landslides and, its associated hazards.…”

Landslide Susceptibility Assessment for Cameron Highlands using Analytical Hierarchy Process

“…LSMoffers information on the probability of landslides in an area due to local terrain circumstances and these techniques can be divided based on the chosen method or characteristics, which is qualitative and quantitative methods [6].…”

“…Qualitative methods denote levels of susceptibility using specialist understanding in descriptive terms, while quantitative methods study the correlation between landslide and causative factor to predict probabilities of occurrence. [6], [7].The qualitative method limitation is that the precision depends on the understanding of the professionals conducting the research, while quantitative methods investigate the correlation between landslide and causative factor to predict the probability of occurrence using weighting causative factors of landslide [7].…”