Abstract
Landslides are the most recurrent and prominent natural hazard in many areas of the world which cause significant loss of life and damage to properties. By generating landslide susceptibility maps, the hazard zones can be identified in order to produce an early warning system to reduce the damage. In this study, the predictive abilities of two statistical models, Logistic regression (LR) model and Geographically Weighted logistic regression (GWLR) model, were compared. As a case study, a data set collected for nine relevant causative factors over the period from 1986 to 2014 was taken from Badulla district, Sri Lanka, which is highly affected by landslides. The performance of each model was tested by using the Area under the curve (AUC) value of Receiver operating characteristic curve (ROC), and the GWLR model was selected as the best fitted model. The probabilities obtained for each pixel in the study area using the selected model were classified into three classes (Low, Medium and High) based on standard deviation method in GIS software. Finally, a landslide susceptibility map was generated related to the above three classes, from which high risk areas can be identified to take necessary actions.
Highlights
Landslide is defined as a “movement of mass of soil or rock down a slope” (Courture, 2011), and it is a common natural hazard in many areas of the world
This study reveals a significant positive relationship between elevation and slope, and a significant negative relationship among the variables distance to roads with landslide occurrences
Two statistical models were fitted to predict the probability of landslide occurrence for each location in the study area
Summary
Landslide is defined as a “movement of mass of soil or rock down a slope” (Courture, 2011), and it is a common natural hazard in many areas of the world. Landslide susceptibility analysis and mapping are important to identify the landslide hazard zones, and risk management can be Slope curvature is another triggering factor, which measures the rate of change of slope. Plan curvature is the rate of change of slope perpendicular to slope gradient which influences convergence and divergence of flow In this case, a positive value implies that the surface is upwardly convex whereas a negative value indicates the surface is upwardly concave. A positive value implies that the surface is upwardly convex whereas a negative value indicates the surface is upwardly concave Both profile and plan curvature are two key factors responsible for landslides since these factors affect the Ceylon Journal of Science 46(4) 2017: 27-41 acceleration and deceleration of flow which influences erosion
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