A bibliometric analysis of the landslide susceptibility research (1999-2021)

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.

Automated summary

Landslide susceptibility assessment (LSA) is a significant part of landslide research and plays a crucial role in preventing landslide disasters. This article presents a bibliometric analysis of 4,732 papers published from 1999 to 2021 in Web of Science, exploring the characteristics of abstracts, authors, institutions, countries, journals, funds, and keywords. The analysis reveals the increasing trend of LSA publications, identifies the productive performers in the field, and highlights the popularity of machine learning methods in recent years.