Landslide susceptibility assessment is crucial in predicting landslide occurrence and
potential risks. However, traditional methods usually emphasize on larger regions of …

Abstract Machine learning (ML)-based landslide susceptibility mapping (LSM) has achieved
substantial success in landslide risk management applications. However, the complexity of …

With the increase in climate-change-related hazardous events alongside population
concentration in urban centres, it is important to provide resilient cities with tools for …

Complex and fragile geological conditions combined with periodic fluctuations in reservoir
water levels have led to frequent landslide disasters in the Three Gorges Reservoir area …

The accurate prediction of landslide susceptibility relies on effectively handling landslide
absence samples in machine learning (ML) models. However, existing research tends to …

Landslide susceptibility map (LSM) plays an important role in providing the knowledge of
slopes prone to future landslides. However, the applicability of LSM is often hindered due to …

In the current work, authors reviewed the latest research results in landslide susceptibility
mapping (LSM) using artificial intelligence (AI) methods. Based on an overall review of …

This paper presents a transfer learning approach to the crop classification problem based on
time series of images from the Sentinel-2 dataset labeled for two regions: Brittany (France) …

A data-driven model is presented for accurate prediction of multiaxial fatigue life based upon
the principle of transfer learning (TL). The Tradaboost framework is explored to adjust the …

The interplay between climate change–induced extreme rainfall and slope failure
mechanisms presents a significant challenge. To address this, a new temporal modeling of …