A scientometric study on Artificial Intelligence for landslide prediction: Trends, collaborations, and advances
Main Article Content
Abstract
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.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
The papers are published in the open access format, being freely available to any user, under a CC-BY Creative Commons license.
By submitting this manuscript for evaluation, the authors are aware of the CC-BY Creative Commons license. In the case of manuscript approval, the author responsible for the manuscript (corresponding author), hereinafter referred to as CEDENTE (ASSIGNOR), hereby assigns and transfers to CPRM-Serviço Geológico do Brasil, holder of the JGSB, free of charge, on his behalf and on behalf from all co-authors, the right of the first publication. This includes the rights of editing, publication, translation into another language and reproduction by any process, worldwide, today and in the future.
After the first publication by the JGSB, authors hold the copyright without restrictions and are allowed to disclose and distribute their work through personal website pages and institutional repositories.
At the same time, the ASSIGNOR declares that the content of the manuscript is of sole responsibility of the authors and that this content does not infringe the copyrights and/or other property rights of third parties, that is, that any contents of the manuscript and its attachments, if taken from other publications, are duly referenced and, when necessary, the permissions for publications of such contents were requested by the authors from the copyright holders; that the disclosure of images (if any) has been authorized and that it assumes full moral and/or patrimonial responsibility, due to its content, before third parties.
References
Alamah, Z., AlSoussy, I., & Fakih, A. (2023). The Role of International Research Collaboration and Faculty Related Factors in Publication Citations: Evidence from Lebanon. In Economies (Vol. 11, Issue 3). https://doi.org/10.3390/economies11030090
AlShebli, B., Memon, S. A., Evans, J. A., & Rahwan, T. (2024). China and the U.S. produce more impactful AI research when collaborating together. Scientific Reports, 14(1), 1–23. https://doi.org/10.48550/arXiv.2304.11123
Binu, K. E., Anoopkumar, L. T., Sunil, M., Jose, M., & Preetha, K. G. (2024). Dynamic Landslide Prediction, Monitoring, and Early Warning with Explainable AI: A Comprehensive Approach. 2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC), 960–965. https://doi.org/10.1109/ICAAIC60222.2024.10575330
Blasch, E., Pham, T., Chong, C.-Y., Koch, W., Leung, H., Braines, D., & Abdelzaher, T. (2021). Machine Learning/Artificial Intelligence for Sensor Data Fusion–Opportunities and Challenges. IEEE Aerospace and Electronic Systems Magazine, 36(7), 80–93. https://doi.org/10.1109/MAES.2020.3049030
Cheung, R. W. M., Li, H. W. M., & Chu, E. K. H. (2023). Exploratory Study of using Artificial Intelligence for Landslide Predictions. Proceedings of The HKIE Geotechnical Division 43rd Annual Seminar: Towards a Smart-Green-Resilient Geo-Future for World-Class City Development" (GDAS2023), 215–229. https://doi.org/10.21467/proceedings.159
Collection, S. C. (2023). Introducing the Journal Citation Indicator. In Clarivate.
Gould, K. A. (2023). Journal Citation Reports 2023: Understanding Bibliometric Data. Dimensions of Critical Care Nursing : DCCN, 42(5), 245–247. https://doi.org/10.1097/DCC.0000000000000603
He, R., Zhang, W., Dou, J., Jiang, N., Xiao, H., & Zhou, J. (2024). Application of artificial intelligence in three aspects of landslide risk assessment: A comprehensive review. Rock Mechanics Bulletin, 3(4), 100144. https://doi.org/https://doi.org/10.1016/j.rockmb.2024.100144
IPCC. (2023). Section 4: Near-Term Responses in a Changing Climate. In Climate Change 2023: Synthesis Report. https://doi.org/10.59327/IPCC/AR6-9789291691647
Jakab, M., Kittl, E., & Kiesslich, T. (2024). How many authors are (too) many? A retrospective, descriptive analysis of authorship in biomedical publications. Scientometrics, 129(3), 1299–1328. https://doi.org/10.1007/s11192-024-04928-1
Jiang, S., Ma, J., Liu, Z., & Guo, H. (2022). Scientometric Analysis of Artificial Intelligence (AI) for Geohazard Research. Sensors, 22(20). https://doi.org/10.3390/s22207814
Kainthura, P., & Sharma, N. (2022). Hybrid machine learning approach for landslide prediction, Uttarakhand, India. Scientific Reports, 12(1), 20101. https://doi.org/10.1038/s41598-022-22814-9
Kappi, M., & B, M. (2024). Artificial intelligence and machine learning for disaster prediction : a scientometric analysis of highly cited papers. Natural Hazards. https://doi.org/https://doi.org/10.1007/s11069-024-06616-y
Kappi, M., Chaman Sab, M., & Biradar, B. S. (2021). Measuring research productivity of “Universities with Centre with Potential for Excellence in Particular Area (CPEPA) status” in Karnataka state. DESIDOC Journal of Library & Information Technology, 41(5), 43. https://doi.org/10.14429/djlit.41.5.16507
Kappi, M., Mamdapur, G. M., Biradar, B. S., & Bhovi, S. Y. (2024). Growth and Trends of Geometrical Optics Research Using Scientometrics Indicators. Science & Technology Libraries, 1–14. https://doi.org/10.1080/0194262X.2024.2360975
Korup, O., & Stolle, A. (2014). Landslide prediction from machine learning. Geology Today, 30(1), 26–33. https://doi.org/10.1111/GTO.12034
Kumar, D., Thakur, M., Dubey, C. S., & Shukla, D. P. (2017). Landslide susceptibility mapping & prediction using Support Vector Machine for Mandakini River Basin, Garhwal Himalaya, India. Geomorphology, 295, 115–125. https://doi.org/https://doi.org/10.1016/j.geomorph.2017.06.013
Li, B., Liu, K., Wang, M., He, Q., Jiang, Z., Zhu, W., & Qiao, N. (2022). Global Dynamic Rainfall-Induced Landslide Susceptibility Mapping Using Machine Learning. In Remote Sensing (Vol. 14, Issue 22). https://doi.org/10.3390/rs14225795
Lima, P., Steger, S., Glade, T., & Murillo-García, F. G. (2022). Literature review and bibliometric analysis on data-driven assessment of landslide susceptibility. Journal of Mountain Science, 19(6), 1670–1698. https://doi.org/10.1007/s11629-021-7254-9
Mallikarjuna, B., Kappi, M., & Taj, S. (2024). Bibliometric Analysis of the Journal of Big Data : Trends , Impact , and Future Directions. Shanlax International Journal of Arts, Science and Humanities, 12(1), 100–114. https://doi.org/10.34293/ sijash.v12i1.7762
Meho, L., & Akl, E. (2024). Using Bibliometrics to Detect Unconventional Authorship Practices and Examine Their Impact on Global Research Metrics, 2019-2023. In Preprints. Preprints. https://doi.org/10.20944/preprints202407.0691.v1
Moustafa, K. (2024). Why most journal impact factors are false. OSF Preprints, 1–7. https://doi.org/10.31219/osf.io/8g67q
Nocentini, N., Rosi, A., Segoni, S., & Fanti, R. (2023). Towards landslide space-time forecasting through machine learning: the influence of rainfall parameters and model setting. Frontiers in Earth Science, Volume 11. https://doi.org/10.3389/feart.2023.1152130
P, L., C, M., Mathew, A., & Shekar, P. R. (2025). Machine learning and deep learning-based landslide susceptibility mapping using geospatial techniques in Wayanad, Kerala state, India. HydroResearch, 8, 113–126. https://doi.org/https://doi.org/10.1016/j.hydres.2024.10.001
Serpa, S., Wang, F., & Zhou, L. (2024). Artificial Intelligence as Author of Scientific Publications. Science Insights, 44(1), 1215–1219. https://doi.org/10.15354/si.24.re866
Sharma, Akanksha, Raj Chopra, S., Gajanan Sapate, S., Arora, K., Khalid Imam Rahmani, M., Jha, S., Ahmad, S., Ezaz Ahmed, M., Abdeljaber, H. A. M., & Nazeer, J. (2024). Artificial Intelligence Techniques for Landslides Prediction Using Satellite Imagery. IEEE Access, 12(August), 117318–117334. https://doi.org/10.1109/ACCESS.2024.3446037
Sharma, Amol, Chander, P., Estifanos Lemma, G., & and Sharma, R. (2023). An artificial intelligence based framework to analyze the landside risk of a mountainous highway. Geocarto International, 38(1), 2186494. https://doi.org/10.1080/10106049.2023.2186494
Singha, C., Rana, V. K., Pham, Q. B., Nguyen, D. C., & Łupikasza, E. (2024). Integrating machine learning and geospatial data analysis for comprehensive flood hazard assessment. Environmental Science and Pollution Research, 31(35), 48497–48522. https://doi.org/10.1007/s11356-024-34286-7
Thirugnanam, H. (2023). Deep Learning in Landslide Studies: A Review. In Progress in Landslide Research and Technology (Vol. 1, Issue 2, pp. 247–255). Springer International Publishing. https://doi.org/10.1007/978-3-031-18471-0_20
Vaishya, R., Gupta, B. M., Kappi, M. M., Mamdapur, G. M. N., & Vaish, A. (2024). Global research output and highly-cited publications on prosthetic joint infections: A bibliometric analysis (2003-2022). Journal of Clinical Orthopaedics and Trauma, 50, 102373. https://doi.org/https://doi.org/10.1016/j.jcot.2024.102373
Vaishya, R., Kappi, M. M., Gupta, B. M., Mamdapur, G. M. N., & Vaish, A. (2024). Global Stem Cell Research in Orthopaedics: A Bibliometric Study from 1995 to 2020. Indian Journal of Orthopaedics, 58(7), 876–886. https://doi.org/10.1007/s43465-024-01160-0