Upon the introduction of machine learning (ML) and its variants, in the form that we know
today, to the landslide community, many studies have been carried out to explore the …

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 …

Across the globe, landslides have been recognized as one of the most detrimental
geological calamities, especially in hilly terrains. However, the correct determination of …

The identification of landslide-prone areas is an essential step in landslide hazard
assessment and mitigation of landslide-related losses. In this study, we applied two novel …

Recent landslide detection studies have focused on pixel-based deep learning (DL)
approaches. In contrast, intuitive annotation of landslides from satellite imagery is based on …

The current study aimed at evaluating the capabilities of seven advanced machine learning
techniques (MLTs), including, Support Vector Machine (SVM), Random Forest (RF) …

With the increasing threat of recurring landslides, susceptibility maps are expected to play a
bigger role in promoting our understanding of future landslides and their magnitude. This …

This study introduces four heterogeneous ensemble-learning techniques, that is, stacking,
blending, simple averaging, and weighted averaging, to predict landslide susceptibility in …

Slope failures lead to catastrophic consequences in numerous countries and thus the
stability assessment for slopes is of high interest in geotechnical and geological engineering …

This study developed a deep learning based technique for the assessment of landslide
susceptibility through a one-dimensional convolutional network (1D-CNN) and Bayesian …