Climate change has an effect on ecosystems and people all around the globe and causes
land degradation. A long‐term decline in biological output, loss of ecological integrity, and a …

Slow-moving landslides are common in the Three Gorges reservoir (TGR) region. It may
evolve into massive landslide hazards due to reservoir level fluctuation and rainfall. The …

(1) Background: The aim of this paper was to study landslide susceptibility mapping based
on interpretable machine learning from the perspective of topography differentiation.(2) …

The application of artificial intelligence (AI) and big data in geohazard investigations has
gained popularity due to the development of machine learning algorithms and data …

Geotechnical engineering data are usually small-sample and high-dimensional, which
brings a lot of challenges in predictive modeling. This paper uses a typical high-dimensional …

The variation in Tunnelling boring machine (TBM) equipment and geological information of
tunnels result in substantial differences in real-time TBM tunnelling data. This variation …

Currently, researchers commonly use convolutional neural network (CNN) models for
landslide remote sensing image recognition. However, with the increase in landslide …

Mining is one of the most daunting occupations gain the sector since it entails risk at any
point in the operation. In its operation, the main focus is on slope stability. To avoid slope …

Slope failures lead to catastrophic consequences in numerous countries, so accurate slope
stability evaluation is critical in geological disaster prevention and control. In this study, the …

Landslide susceptibility mapping is an integral part of geological hazard analysis. Recently,
the emphasis of many studies has been on data-driven models, notably those derived from …