Landslide prediction, monitoring and early warning: a concise review of state-of-the-art

• Published in: Geosciences journal (Seoul, Korea) Vol. 21; no. 6; pp. 1033 - 1070
• Main Authors: Chae, Byung-Gon, Park, Hyuck-Jin, Catani, Filippo, Simoni, Alessandro, Berti, Matteo
• Format: Journal Article
• Language: English
• Published: Seoul The Geological Society of Korea 01.12.2017; 한국지질과학협의회
• Subjects: Earth and Environmental Science; Earth Sciences; 지질학; landslide monitoring; landslide susceptibility; early warning; runout
• ISSN: 1226-4806; 1598-7477
• DOI: 10.1007/s12303-017-0034-4

Landslide is one of the repeated geological hazards during rainy season, which causes fatalities, damage to property and economic losses in Korea. Landslides are responsible for at least 17% of all fatalities from natural hazards worldwide, and nearly 25% of annual casualties caused by natural hazards in Korea. Due to global climate change, the frequency of landslide occurrence has been increased and subsequently, the losses and damages associated with landslides also have been increased. Therefore, accurate prediction of landslide occurrence, and monitoring and early warning for ground movements are very important tasks to reduce the damages and losses caused by landslides. Various studies on landslide prediction and reduction in landslide damage have been performed and consequently, much of the recent progress has been in these areas. In particular, the application of information and geospatial technologies such as remote sensing and geographic information systems (GIS) has greatly contributed to landslide hazard assessment studies over recent years. In this paper, the recent advances and the state-of-the-art in the essential components of the landslide hazard assessment, such as landslide susceptibility analysis, runout modeling, landslide monitoring and early warning, were reviewed. Especially, this paper focused on the evaluation of the landslide susceptibility using probabilistic approach and physically based method, runout evaluation using volume based model and dynamic model, in situ ground based monitoring techniques, remote sensing techniques for landslide monitoring, and landslide early warning using rainfall and physical thresholds.