Debris flow prediction and impact mitigation: A comprehensive review and bibliometric perspective
Year: 2026
Type: article
Abstract: Debris flows are among the most destructive natural hazards in mountainous environments, and their increasing frequency under climate change and rapid land-use transformation has intensified the demand for accurate prediction and effective mitigation strategies. This study presents a comprehensive review of recent advances in debris-flow prediction and impact mitigation through an integrated bibliometric and technical analysis of 892 peer-reviewed publications indexed in Scopus from 2015 to 2025. The bibliometric results reveal a rapid growth in research output over the past decade, with East Asia and Europe emerging as major research hubs. Artificial intelligence, remote sensing, and early-warning systems are identified as dominant research frontiers. The technical synthesis demonstrates a clear methodological transition from traditional empirical and rainfall-threshold models toward hybrid frameworks that integrate physically based simulations, machine learning, and multi-source monitoring data. Deep learning and ensemble models consistently show superior predictive performance in susceptibility mapping, velocity estimation, and early-warning applications, while physically based multi-phase models remain essential for process understanding despite their high data and computational demands. The integration of GIS, UAVs, LiDAR, and real-time sensor networks has further enhanced spatial prediction accuracy and operational warning capability. This review also identifies critical scientific gaps in model transferability, uncertainty quantification, and data availability, and proposes future research directions focused on physics-guided machine learning, probabilistic early-warning systems, and climate-resilient mitigation strategies. The findings provide a structured scientific foundation for advancing next-generation debris-flow prediction and risk-reduction practices.
Source: Multidisciplinary Reviews
Language: English
Subfield: Management, Monitoring, Policy and Law
Field: Environmental Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7164201511