Fully supervised machine learning models are widely applied for landslide susceptibility
prediction (LSP), mainly using landslide and non-landslide samples as output variables and …

Landslide susceptibility mapping (LSM) research is vital for averting and mitigating regional
landslide disasters. Nevertheless, there has been a lack of systematic analysis regarding …

The expansion of mountainous urban areas and road networks can influence the terrain,
vegetation, and material characteristics, thereby altering the susceptibility of landslides …

Weather-related disasters have caused widespread deaths and economic losses in
developing countries, including Brazil. Frequent floods and landslides in Brazil are mostly …

Landslide susceptibility prediction (LSP) is significantly affected by the uncertainty issue of
landslide related conditioning factor selection. However, most of literature only performs …

Landslide occurrence in Colombia is very frequent due to its geographical location in the
Andean mountain range, with a very pronounced orography, a significant geological …

In landslide susceptibility assessment (LSA), inventory incompleteness impacts the accuracy
of different models to varying degrees. However, this area remains under-researched. This …

Landslides are common natural disasters that cause serious damage to ecosystems and
human societies. To effectively prevent and mitigate these disasters, an accurate …

A massive landslide occurred in Domeshi area, District Muzaffarabad, Pakistan, in two
distinct phases: an initial movement on August 1, followed by complete failure on August 4 …

Changing rainfall patterns on vulnerable hill slopes are a significant factor in increasing
landslide frequency and resulting damages. In the Nilgiris district of the Western Ghats …