In recent years, debris flows are becoming more frequent and larger in magnitude due to global climate change, resulting in the loss of human life and substantial damage to infrastructure. In light of such trends, there is increasing national interest for the development of proactive technologies to prevent and mitigate debris flow disasters. Although many disaster prevention facilities are being built, there are still questions regarding the accuracy and reliability of the methodologies and techniques being utilized for the design of these structures. Therefore, in order to improve existing disaster prevention measures and effectively reduce damage, it is necessary to make scientific and technological strides at each stage of the design process of disaster prevention facilities. This session mainly focuses on methods for the prevention and mitigation of debris flow disasters, including the following topics:
(1) Advanced data collection methods for the collection of site properties such as the utilization of UAV-based LiDAR, spectroscopic techniques, etc.
(2) Prediction techniques that provide quantitative information of debris flow through big data analysis, machine learning models, and numerical modeling
(3) Performance analysis of various types of disaster prevention facilities based on small-scale & large-scaled experiments and numerical simulations
(4) Optimum design of disaster prevention facilities through sensitivity analysis and parametric studies
We also welcome submissions that focus on new techniques and design methodologies related to the 4th industrial revolution.

Debris flow, Disaster prevention facilities, Optimum design, Experimental and numerical studies, Big data, Machine learning techniques

Landslides are one of the most widespread and destructive natural hazards in the world. However, it is possible to reduce hazards caused by the landslides by monitoring and/or early warning systems. Today, lots of systems are available for the purpose and new systems have been developing continuously. The aim of this session is to gain a complete knowledge about the landslide monitoring and early warning systems by introducing different systems used, learning new technologies about the topic, investigating their properties, comparing the techniques and devices.

Keywords: Landslide monitoring systems, early warning systems

Co-organized as GI4.18
Convener: Tae-Hyuk Kwon | Co-conveners: Yun Tae Kim, Anders Solheim, Arzu Arslan Kelam, Mustafa K Koçkar
| Wed, 10 Apr, 08:30–10:00
Room 1.61
| Attendance Wed, 10 Apr, 16:15–18:00
Hall X3

Wednesday, 10 April 2019 | Room 1.61

Chairperson: Tae-Hyuk Kwon, Arzu Arslan Kelam
Session opening
08:30–08:45 |
Jessica Holmes, Jonathan Chambers, Paul Wilkinson, Philip Meldrum, Shane Donohue, David Huntley, and Peter Bobrowsky
08:45–09:00 |
Mathieu Le Breton, Laurent Baillet, Eric Larose, Etienne Rey, Philippe Benech, Denis Jongmans, Fabrice Guyoton, and Michel Jaboyedoff
09:00–09:15 |
Deuk-hwan Lee, Seung-Rae Lee, Joon-Young Park, and Hwan-Hui Lim
09:15–09:30 |
Hervé Vicari, Ashenafi Yifru, Vikas Thakur, and Steinar Nordal
09:30–09:45 |
Paolo Salandin and Stefano Lanzoni
09:45–10:00 |
Yoann Drouillas, Raphael Chochon, Thomas Lebourg, Séverine Bernardie, Nathalie Marçot, Carlo Troisi, Serena Recagno, Bianca Federici, Alessio De Melas, Maurin Vidal, and Romain Besso
Discussion and closing thoughts