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.
Highlights
Mass movements are one of the major geological hazards in Nepal; almost 80% of the total area of the country is prone to landslides [1]
The slope angle is considered as the most critical factor of the slope stability assessment in any landslide susceptibility mapping (LSM) study [54]. The elevation is another crucial factor of LSM as most geomorphological and geological processes are controlled by this topographical factor [55]
Landslide susceptibility analysis was carried out using the analytical hierarchy process (AHP), frequency ratio (FR) and hybrid spatial multi-criteria evaluation (SMCE) models in the reLgainodnsalifdfeecsteudscbeypttihbeilGityoraknhaalyesairsthwqausackaer.ried out using the AHP, FR and hybrid SMCE models in the region affected by the Gorkha earthquake
Summary
Mass movements are one of the major geological hazards in Nepal; almost 80% of the total area of the country is prone to landslides [1]. The Gorkha earthquake resulted in the deaths of more than 9000 people and is estimated to affect about eighty-five million people in Nepal and neighboring countries [3]. The estimates of previous studies as to the number of landslides triggered by the Gorkha earthquake range between 3000 and 25,000 [4,5,6,7,8,9]. In most of the studies related to the Gorkha earthquake, the emphasis was on the development of a landslide inventory while using this information to develop a landslide susceptibility zonation for the earthquake-affected region was largely neglected. There has been extensive work done on the preparation of coseismic landslide inventories for susceptibility evaluations worldwide. Due to advancements in geographic information systems (GIS) and Earth observation (EO) technologies, including semi-automatic and machine learning techniques, the preparation of a coseismic landslide inventory is becoming more attainable and less time-consuming
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