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

Landslides, the second largest geological hazard after earthquakes, result in significant loss
of life and property. Extracting landslide information quickly and accurately is the basis of …

Landslide susceptibility assessment is an important means of helping to reduce and
manage landslide risk. The existing studies, however, fail to examine the spatially varying …

Abstract Machine learning (ML) has been used for landslide susceptibility analysis for a
while; however, studies using real-time earthquake induced landslide data are barely used …

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 …

The southwestern foothills of the Himalayan Mountain range have been experiencing a
surge of catastrophic landslides in the last two decades, as a tragic result of the adverse …

This study employs machine learning techniques to map and predict landslide-prone areas
in São Sebastião, Brazil, a region susceptible to landslides due to its steep terrain and …

Abstract The Eocene Kuldana Formation (KF) in the Yadgar area of Pakistan, comprises a
diverse range of sedimentary facies, including variegated red beds of shales, mudstones …

Landslide hazards affect the security of human life and property. Landslide hazard maps are
essential for landslide prevention and mitigation. In this study, the success of machine …

Within the domain of geotechnical engineering, risk assessment is pivotal, acting as the
linchpin for the safety, durability, and resilience of infrastructure projects. While traditional …