Resumen
In recent years, there has been a growing interest in flood susceptibility modeling. In this study, we conducted a bibliometric analysis followed by a meta-data analysis to capture the nature and evolution of literature, intellectual structure networks, emerging themes, and knowledge gaps in flood susceptibility modeling. Relevant publications were retrieved from the Web of Science database to identify the leading authors, influential journals, and trending articles. The results of the meta-data analysis indicated that hybrid models were the most frequently used prediction models. Results of bibliometric analysis show that GIS, machine learning, statistical models, and the analytical hierarchy process were the central focuses of this research area. The analysis also revealed that slope, elevation, and distance from the river are the most commonly used factors in flood susceptibility modeling. The present study discussed the importance of the resolution of input data, the size and representation of the training sample, other lessons learned, and future research directions in this field.