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 …

Landslide hazard assessment is an important step towards landslide hazard and risk
management. There are several methods of Landslide Hazard Zonation (LHZ) viz. heuristic …

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

Hazards and disasters have always negative impacts on the way of life. Landslide is an
overwhelming natural as well as man-made disaster that causes loss of natural resources …

Coupling machine learning algorithms with spatial analytical techniques for landslide
susceptibility modeling is a worth considering issue. So, the current research intend to …

Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices,
and can cause social upheaval and loss of life. As a result, many scientists study the …

This study performs a GIS-based landslide susceptibility assessment using a convolutional
neural network, CNN, in a study area of the Gorzineh-khil region, northeastern Iran. For this …

Nowadays, a number of machine learning prediction methods are being applied in the field
of landslide susceptibility modeling of the large area especially in the difficult hilly terrain. In …

The purpose of the present study is to compare the prediction performances of three different
approaches such as decision tree (DT), support vector machine (SVM) and adaptive neuro …