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Landslides, one of the most common mountain hazards, can result in enormous casualties and huge economic losses in mountainous regions. In order to address the landslide hazards effectively, the geological society is required not only to develop in-depth understanding of landslide mechanism but also to quantify its associated risk. In this article, landslide risk assessment is categorised into two types, hard and soft risk assessments, and reviewed separately. The hard approach focuses on the mechanics and numerical simulations of individual landslides, subsequent consequences, and their uncertainty quantifications and probabilistic analyses while the soft approach explores the quantification of disaster risk components such as hazard and vulnerability at different scales of concern. It is hoped that this article can serve as a time capsule to link the preceding and following of landslide risk assessments and shed some light on future studies. [ABSTRACT FROM AUTHOR]

Spatial variability of properties of geo-materials has a significant impact on the stability of landslides. In this paper, the spatial variability of soil properties of a landslide is modelled using random-field generated by the discrete cosine transform. The method developed can be readily applied to determine the probability of failure of the landslide using Monte Carlo simulation. A case study of a landslide with spatially variable properties is performed and discussed. The proposed approach is shown to be effective and efficient for probabilistic stability analysis of landslides modelled using 2-D random-field. [ABSTRACT FROM AUTHOR]

Landslide susceptibility mapping (LSM) research is vital for averting and mitigating regional landslide disasters. Nevertheless, there has been a lack of systematic analysis regarding LSM's developmental drivers. Utilizing SciMAT, a scientometric tool, we analyzed 1661 papers on LSM from the Web of Science core collection database, spanning 1993 to 2022. We employed cluster and thematic evolution analysis in SciMAT to unveil trends. The results indicate a consistent upward trend over the past three decades. LSM modeling methods, geological data, and contributing factors are major focal points. Notably, the evolution of LSM models, with a rising adoption of machine learning and deep learning for risk assessment, emerges as a central knowledge pathway. This study offers valuable insights to scholars by identifying literature gaps and highlighting crucial research directions, facilitating informed decision-making in the realm of LSM. SciMAT tool initially employs cluster analysis and thematic evolution analysis to pinpoint the current popular themes and evolutionary trends of landslide susceptibility. The main trajectory of knowledge lies in the progression of landslide modeling methods. The latest trend in landslide susceptibility research is centered on the use of deep learning techniques, particularly convolutional neural networks, to achieve precise risk zonation. The study aims to highlight critical research directions, predict the future of LSM technology, and provide guidance for scholars. [ABSTRACT FROM AUTHOR]

Landslide susceptibility assessment (LSA) is a significant part of landslide research, which plays an important role in preventing landslide disasters. It has gained an increasing attention in both the academic and practice fields for the past two decades. However, there have been few bibliometric analyses on this topic, although bibliometric analysis can inspire future researchers by exploring the overall characteristics of the published literature. This article aims at collecting and analyzing the information of the abstracts, authors, institutions, countries, journals, funds, and keywords of the recent 4,732 papers published from 1999 to 2021 in Web of Science (www.webofscience.com). In particular, latent Dirichlet allocation (LDA), a machine learning and text analysis method, is utilized to analyze the abstract of each article to identify the hottest research topics related to LSA. The results revealed that: (1) The amount of annual publications related to LSA generally shows an increasing trend, which accounts for about 22% of the total landslide publications in 2021; (2) The author of Pradhan B, the institution of the Chinese Academy of Sciences, the country of China, the journal of Natural Hazards and the funding agency of the National Natural Science Foundation of China, are the productive performers in each aspect of LSA; and (3) Machine learning methods have gained a rapid increase in LSA in recent five years, which have become the most popular research topic. [ABSTRACT FROM AUTHOR]