Quality assessment (QA) for landslide susceptibility maps (LSMs) is essential to increase
their usability. A QA approach based on the landslide activity after a triggering event can be …

In the international literature, although considerable amount of publications on the landslide
susceptibility mapping exist, geomorphology as a conditioning factor is still used in limited …

Landslide susceptibility assessment is a complex challenge explored by various scientists,
but not fully resolved. In this study, we produced the landslide susceptibility map of a large …

The aim of this study is to make a comparison of the performances of two machine-learning
algorithms that support vector machine (SVM) and random forest (RF) for landslide …

Landslides pose a serious risk to human life and the natural environment. Here, we compare
machine learning algorithms including the generalized linear model (GLM), mixture …

Landslide susceptibility maps provide crucial information that helps local authorities, public
institutions, and land-use planners make the correct decisions when they are managing …

The concept of leveraging the predictive capacity of predisposing factors for landslide
susceptibility (LS) modeling has been continuously improved in recent work focusing on …

Landslide susceptibility mapping (LSM) has been widely used as an important reference for
development and construction planning to mitigate the potential social‐eco impact caused …

Landslide susceptibility maps (LSMs) rely on statistical association for weighting the impact
of each predictive factor class. However, the effects of using different training datasets and …

Landslides are a major natural hazard that can significantly damage infrastructure and
cause loss of life. In South Korea, the current landslide susceptibility mapping (LSM) …