The present study demonstrates the results of three GIS-based statistical methods for landslide susceptibility mapping (LSM) and their comparison for the Larji-Kullu Tectonic Window (LKTW), Kullu Valley, Higher Himachal Himalayas, and northern India. We used nine geological, geomorphological and topographical conditioning factors: lithology, terrain slope, slope aspect, elevation, and distance to drainage, distance to faults, distance to lineaments, and distance to roads. Our landslide inventory data set with 149 GPS points of landslide locations was generated during a field survey in July 2018. The relationships between the landslide locations and the nine conditioning factors were determined by three GIS-based statistical methods, i.e. the analytical hierarchy process (AHP) the frequency ratio (FR), and the hybrid spatial multi-criteria evaluation (SMCE) method. We also compared and evaluated the performance of the applied methods. 70% of the landslide inventory was used for training the statistical methods, and the remaining 30% was used for validation purpose. The area under the curve (AUC) of the success plot was used for the validation of the results. The SMCE method gives 0.910 accuracy rate compared to the other two other methods, AHP and FR 0.790 and 0.905 accuracy rate, respectively. Resulting LSMs can be useful for risk mitigation and spatial planning purposes in the Kullu district, Himachal Himalayas.
|Number of pages||1|
|Publication status||Published - 20 Apr 2019|
|Event||International Workshop on Climate Change and Extreme Events in the Himalayan Region 2019 - Indian Institute of Technology, Mandi, India|
Duration: 18 Apr 2019 → 20 Apr 2019
|Workshop||International Workshop on Climate Change and Extreme Events in the Himalayan Region 2019|
|Abbreviated title||C2E2 Himalaya 2019|
|Period||18/04/19 → 20/04/19|