A scientometric study on Artificial Intelligence for landslide prediction: Trends, collaborations, and advances

This scientometric study analysed 1,834 publications (2009–2023) on artificial intelligence applications in landslide prediction, revealing rapid growth (26.66% annual citation rate) with an average of 45.80 citations per paper. Despite the increased number of publications, the citation impact has not increased proportionally. The top 25 authors contributed 50.87% of the total output, while international collaborations (42.42% of papers) drove progress, with China and Vietnam as key contributors. Funded research (1,307 papers) generated 59,863 citations. Geology dominated the discipline, although agriculture achieved the highest citation impact. Q1 journals (e.g. Catena and Landslides) outperformed Q2 venues (e.g. Remote sensing) in terms of citations. Among the 227 highly cited papers (12.38% of the total), the average citation count was 215.1. The core themes included machine learning, landslide susceptibility, and deep learning. The findings highlight AI’s multidisciplinary potential of AI but underscore the need for enhanced international collaboration, explainable AI for model transparency, and strategies to mitigate citation biases to maximise research impact.