In this study, four representative machine learning methods (support vector machine (SVM),
maximum entropy (MaxEnt), random forest (RF), and artificial neural network (ANN)) were …

Landslides are type of natural geohazard interfering with many economical and social
activities and causing serious damages on human life. It is ranked as a great disaster …

Statistical and now machine learning prediction methods have been gaining popularity in
the field of landslide susceptibility modeling. Particularly, these data driven approaches …

Abstract Machine learning faces two difficulties in the evaluation of landslide susceptibility.
One is the objective quantification of evaluation index, and the other is the selection of …

In recent years, increasing efforts have been made to predict the time, location, and
magnitude of future landslides. This study explores the potential application of four state-of …

The main objective of this study is to evaluate and compare the performance of landslide
models using machine learning ensemble technique for landslide susceptibility assessment …

Landslides are devastating natural disasters that seriously threaten human life and property.
Landslide susceptibility mapping (LSM) plays a key role in landslide hazard management …

The selection of nonlandslide samples is a key issue in landslide susceptibility modeling
(LSM). In view of the potential subjectivity and randomness in random sampling, this paper …

The existence of shallow landslide brings huge threats to the human lives and economic
development, as the Lang County, Southeastern Tibet prone to landslide. Landslide …

Landslides are known as the world's most dangerous threat in mountainous regions and
pose a critical obstacle for both economic and infrastructural progress. It is, therefore, quite …