"Ensembled transfer learning approach for error reduction in landslide susceptibility mapping of the data scare region".

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 high cost of data collection especially in mountainous region such as Himalayas. Therefore, this study proposes transfer learning approach (TL) to improve the performance of LSM by transferring the information from the data rich region (source) to data scare region (target).

Improving ML-based landslide susceptibility using ensemble method for sample selection: a case study of Kangra district in Himachal Pradesh, India.

Developing effective strategies to predict areas susceptible to landslides and reducing risk is vital. This involves using ensemble methods to meet the precise prediction and addressing challenges like data limitation. Recent studies have highlighted the potential of using ensemble methods to enhance the prediction of landslide susceptibility maps (LSM).

Evaluating causative factors for landslide susceptibility along the Imphal-Jiribam railway corridor in the North-Eastern part of India using a GIS-based statistical approach.

The Northeast part of India is experiencing an increase in infrastructure projects as well as landslides. This study aims to prepare the landslide susceptibility map of Tamenglong and Senapati districts, Manipur, India, and evaluates the state of landslide susceptibility along the Imphal-Jiribam railway corridor. Efficient statistical methods such as frequency ratio (FR), information value (IoV), weight of evidence (WoE), and weighted linear combination (WLC) were used in model preparation.