The role of geology in flood risk assessments: a systematic literature review and a comprehensive bibliometric analysis

Floods have emerged as a critical global issue due to climate change, leading to increased research interest across various fields. However, the complex relationship between floods and geological factors remains insufficiently explored in the literature. This bibliometric analysis addresses this gap by examining the intellectual structure of research on floods and geology through a systematic review of 71 articles published between 1989 and 2024. The study reveals that environmental science dominates the field (44%), followed by earth and planetary sciences (16%), engineering (12%), and computer sciences (7%). Analysis of research terms demonstrates the field's breadth, with hydrology-related keywords comprising 58.4% of total terms, while flood-related and geology-related terms represent 21.9% and 19.7%, respectively. This study was conducted using data from the Scopus database, and co-word, co-citation, and co-author network analysis were performed through VOSviewer software. Key topics, influential publications, citation patterns, and international collaborations were identified and visualized using VOSviewer. The United States leads with 22 publications and 771 citations, followed by China with 15 publications and 117 citations. The analysis identified seven distinct international collaboration clusters, highlighting the global nature of flood research while also revealing geographical disparities in coverage. Notably, previous research demonstrates that integrating geological layers into hydrological models yields results closely matching real flood measurements, even in basins lacking measurement stations. This finding emphasizes the significance of understanding lithological characteristics for enhanced flood risk assessment. The analysis highlights an increasing application of advanced technologies, such as remote sensing, GIS, and machine learning, particularly in post-2020 studies, marking a shift toward data-driven approaches.