English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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.

Landslide susceptibility assessment using statistical models: A case study in Badulla district, Sri Lanka