|
Landslide susceptibility assessment using frequency ratio model at Ossey watershed area in Bhutan |
|---|---|
| รหัสดีโอไอ | |
| Creator | 1. Thongley Thongley 2. Chaiwiwat Vansarochana |
| Title | Landslide susceptibility assessment using frequency ratio model at Ossey watershed area in Bhutan |
| Publisher | Faculty of Engineering, Khon Kaen University |
| Publication Year | 2564 |
| Journal Title | Engineering and Applied Science Research |
| Journal Vol. | 48 |
| Journal No. | 1 |
| Page no. | 56-64 |
| Keyword | Landslide susceptibility mapping, Frequency ratio model, Area under curve, Kappa index |
| URL Website | https://www.tci-thaijo.org/index.php/easr/index |
| Website title | Engineering and Applied Science Research |
| ISSN | 2539-6161 |
| Abstract | Landslide is one of the most frequent disaster at Ossey watershed area in Bhutan causing inconvenience to the local people, financial losses and claiming lives of the people every year. This study aimed at developing the landslide susceptibility map (LSM) based on its severity at Ossey watershed area in Bhutan. During the landslide inventory, a total of 164 landslide locations were identified using the sentinel-2 interpretation, google earth image and field survey which was divided into training and validation dataset. Training and validation dataset comprise of 70% (115 locations) and 30% (49 locations) of the total landslide, respectively. Twelve factors were considered for this study which includes altitude, aspect, curvature, slope gradient, topographic wetness index, stream power index, normalized difference vegetation index, proximity to road, proximity to river, lithology, rainfall data, and land cover map. The landslide susceptibility map was developed using the frequency ratio model. The kappa index was used for the checking reliability of the model and area under curve (AUC) of the receiver operating characteristics (ROC) curve was used for validation of the LSM. The kappa indexes were 0.4261 and 0.5510 for training and validation dataset respectively indicating the LSM is reliable as the kappa values fall under the scale of moderately reliable. The AUC are 0.7916 and 0.8742 for the success rate and prediction rate respectively indicating the LSM is accurate enough for the engineering application. The final LSM is classified into five classes using equal interval classifier as the data distributions are close to the normal. The final LSM could be useful for the future researchers, planners, decision makers and engineers for the future developmental activities. |
