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.

This paper investigates the automation of hydraulic fracturing within the context of Industry 4.0, defining intelligent fracturing systems through three components: surface equipment, downhole sensing, and software control systems. The study addresses major advancements such as real-time optimization of fracturing parameters, intelligent allocation of fracturing fluids and materials, risk early warning systems, and fracture monitoring. It identifies four technical challenges: integrating heterogeneous data, developing intelligent decision-making algorithms, adaptive surface equipment adjustments, and multi-machine collaborative control. The paper reviews technological progress, foresees future benchmarks, and predicts the potential for unmanned fracturing operations, including site reconnaissance, autonomous fracturing unit driving, intelligent wellsite layout, fluid mixing, and operation execution. This comprehensive review delineates the developmental framework of intelligent fracturing, outlines key challenges, and forecasts future trends, marking a significant contribution to the field.

Novel Transformer-based deep neural network for the prediction of post-refracturing production from oil wells