In recent decades, data-driven landslide susceptibility models (DdLSM), which are based on
statistical or machine learning approaches, have become popular to estimate the relative …

The quality of digital elevation models (DEMs), as well as their spatial resolution, are
important issues in geomorphic studies. However, their influence on landslide susceptibility …

Landslides are major hazards for human activities often causing great damage to human
lives and infrastructure. Therefore, the main aim of the present study is to evaluate and …

Road infrastructure is vital for economic development, connecting various locations.
However, in Morocco, landslides pose recurring challenges to road projects due to factors …

Abstract Machine learning methods require a vast amount of data to train a model. The data
necessary for landslide susceptibility map** is a collection of landslide causative factors …

In this paper, an attempt has been made to implement various machine learning techniques
to predict the factor of safety of a natural residual soil slope and a man-made overburden …

Landslide susceptibility map** (LSM) is very important for hazard risk identification and
prevention. Most of existing neural network models extract a pixel neighborhood feature or a …

The spatial variability in any kind of geomorphic studies based on terrain attributes are the
most important issues. This terrain attributes and their respective characteristics play a …

The main objective of the current study is to apply a random forest (RF) data-driven model
and prioritization of landslide conditioning factors according to this method and its …

In the past decade, advances in machine learning (ML) techniques have resulted in
develo** sophisticated models that are capable of modelling extremely complex multi …