Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya

Abstract

Landslide susceptibility maps are vital for disaster management and for plan-ning development activities in the mountainous country like Nepal. In the present study, landslide susceptibility assessment of Mugling-Narayanghat road and its surrounding area is made using bivariate (certainty factor and index of entropy) and multivariate (logistic regression) models. At first, a landslide inventory map was prepared using earlier reports and aerial photographs as well as by carrying out field survey. As a result, 321 landslides were mapped and out of which 241 (75 %) were randomly selected for building landslide susceptibility models, while the remaining 80 (25 %) were used for validating the models. The effectiveness of landslide susceptibility assessment using GIS and statistics is based on appropriate selection of the factors which play a dominant role in slope stability. In this case study, the following landslide conditioning factors 2 were evaluated: slope gradient; slope aspect; altitude; plan curvature; lithology; land use; distance from faults, rivers and roads; topographic wetness index; stream power index; and sediment transport index. These factors were prepared from topographic map, drainage map, road map, and the geological map. Finally, the validation of landslide susceptibility map was carried out using receiver operating characteristic (ROC) curves. The ROC plot estimation results showed that the susceptibility map using index of entropy model with AUC value of 0.9016 has highest prediction accuracy of 90.16 %. Similarly, the susceptibility maps produced using logistic regression model and certainty factor model showed 86.29 and 83.57 % of prediction accuracy, respectively. Furthermore, the ROC plot showed that the success rate of all the three models performed more than 80 % accuracy (i.e. 89.15 % for IOE model, 89.10 % for LR model and 87.21 % for CF model). Hence, it is concluded that all the models employed in this study showed reasonably good accuracy in predicting the landslide susceptibility of Mugling-Narayanghat road section. These landslide suscepti-bility maps can be used for preliminary land use planning and hazard mitigation purpose.

GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran

“…where Y IOE is the sum of all the classes; i is the number of particular parametric map; z is the number of classes within parametric map with the greatest number of classes; m i is the number of classes within particular parametric map; C is the value of the class after secondary classification; and W j is the weight of a parameter (Bednarik et al 2010;Devkota et al 2013). The result of this summation represents the various levels of the landslide susceptibility (Constantin et al 2011).…”

Application Of Logistic Regression Method To Produce Landslide Susceptibility Map: A Case Study Of Tetouan Mazari, Morocco

“…It can be beneficial for either continuous or discrete, or any combination of both types, and they do not necessarily have normal distributions. The LR has been proven more efficient than other methods such as certainty factor, likelihood ration, artificial neural networks, and multi-criteria decision analysis for landslides susceptibility assessment [19].…”

Application of GIS Technology and Sensor Technology in Tunnel Deformation Mapping

“…As a new technology, the satellite positioning network RTK technology, with its unique advantages, can obtain the three-dimensional coordinates of high precision and high sampling frequency all day long. The sampling frequency of the receiver has reached 10-20HZ, and the positioning accuracy can reach millimeter level, which is widely used in the field of deformation monitoring, especially in the application of tunnel deformation monitoring [3][4][5]. The GIS technology combines the spatial data and the attribute data management as a whole.…”