Landslides are the most frequent natural disaster in Nepal. As such, the scientific study of the Nepal landslides has been in progress for several years, but no significant achievement has been made in preventing landslides and mitigating disaster damage yet. As one important aspect of dealing with landslides is understanding their distribution pattern based on geological and geomorphological formations, this paper addresses these issues, and attempts to present a scenario of large-scale landslide distribution along the road corridors of major highways in central Nepal. As a result of landslide mapping using aerial photographs, topographical maps and field verification, the following points were understood: (1) the distribution of large-scale landslides is relatively dense over the area close to tectonic thrusts; (2) slate and phyllite zones have a greater ratio of landslide distribution; and (3) topographies with a mean slope angle of about 27–36 degrees have denser distribution of large-scale landslides.

This study is an efficiency comparison between four methods for the production of landslide susceptibility maps (LSMs), which include random forest (RF), artificial neural network (ANN), and logistic regression (LR) as the machine learning (ML) techniques and frequency ratio (FR) as a statistical method. The study area is located in the Southern Hiroshima Prefecture in western Japan, a locality known to suffer from rainfall-induced landslide disasters, the most recent one in July 2018. The landslide conditioning factors (LCFs) considered in this study are lithology, land use, altitude, slope angle, slope aspect, distance to drainage, distance to lineament, soil class, and mean annual precipitation. The rainfall LCF data comprise XRAIN (eXtended RAdar Information Network) radar records, which are novel in the task of LSM production. The accuracy of the produced LSMs was calculated with the area under the receiver operating characteristic curve (AUROC), and an automatic hyperparameter tuning and result comparison system based on AUROC scores was utilized. The calculated AUROC scores of the resulting LSMs were 0.952 for the RF method, 0.9247 for the ANN method, 0.9016 for the LR method, and 0.8424 for the FR. It is also noteworthy that the ML methods are substantially swifter and more practical than the FR method and allow for multiple and automatic experimentations with different hyperparameter settings, providing fine and accurate outcomes with the given data. The results evidence that ML techniques are more efficient when dealing with hazard assessment problems such as the one exemplified in this study. Although the conclusion that the RF method is the most accurate for LSM production as found by other authors in the literature, ML method efficiency may vary depending on the specific study area, and thus the use of an automatic multi-method LSM production system with hyperparameter tuning such as the one utilized in this study is advised. It was also found that XRAIN radar-acquired mean annual precipitation data are effective when used as an LCF in LSM production.