Bibliometric Analysis Landslide Susceptibility using Artificial Intelligence in Geospatial : A Comprehensive Review

Syarbaini Ahmad; Rafiza Kasbun; Noor Fadzilah Ab Rahman; Khairil Ashraf Elias; Iksal Yanuarsyah

doi:10.37934/sijml.5.1.2745

Authors

Syarbaini Ahmad Department of Computing, Faculty of Multimedia Creative and Computing, Universiti Islam Selangor, Malaysia
Rafiza Kasbun Department of Multimedia Creative, Faculty of Multimedia Creative and Computing, Universiti Islam Selangor, Malaysia
Noor Fadzilah Ab Rahman Department of Computing, Faculty of Multimedia Creative and Computing, Universiti Islam Selangor, Malaysia
Khairil Ashraf Elias Department of Computing, Faculty of Multimedia Creative and Computing, Universiti Islam Selangor, Malaysia
Iksal Yanuarsyah Faculty of Engineering and Science, Ibn Khaldun University of Bogor, Indonesia

DOI:

https://doi.org/10.37934/sijml.5.1.2745

Keywords:

Geospatial, Artificial Intelligence, landslide, spatial

Abstract

This study conducts a bibliometric analysis of landslide susceptibility research utilizing Artificial Intelligence (AI) within geospatial contexts. By examining a comprehensive dataset derived from 497 journal articles published between 2019 and 2024, the analysis aims to identify trends, influential authors, and key publications in the field. The integration of AI techniques, particularly machine learning algorithms, is highlighted for its potential to enhance the understanding and prediction of landslide occurrences. The research underscores the importance of geospatial data in assessing natural hazards and emphasizes the transformative role of advanced technologies in improving risk management strategies. Through citation analysis, keyword co-occurrence, and visualization of research networks, this study provides valuable insights into the evolving landscape of landslide susceptibility research, guiding future investigations and the development of AI-based solutions for effective landslide risk mitigation.