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

Attendance time: Wednesday, 10 April 2019, 16:15–18:00 | Hall X3

Chairperson: Mustafa K Koçkar, Tae-Hyuk Kwon
X3.155 |
Study on an early-warning model of landslides in Xinjiang, China
Shouding Li, Linan Liu, Yaheng Bai, Xiao Li, Jianming He, Yue Jiang, Zhanhe Wang, and Wenhui Wei
X3.156 |
Tae-Hyuk Kwon, Shin-Kyu Choi, Hyun Myung, Sungwook Jung, and Yun-Tae Kim
X3.157 |
Shin-Kyu Choi, Tae-Hyuk Kwon, Seung-Rae Lee, Joon-Young Park, and Yun-Tae Kim
X3.158 |
Hwan-hee Lim, Seung-Rae Lee, Tae-Hyuk Kwon, Deuk-Hwan Lee, Joon-Young Park, and Yun-Tae Kim
X3.159 |
Klaus-Peter Keilig, Peter Neumann, Markus Bauer, Kurosch Thuro, and Zurab Menabde
X3.160 |
Antonio Funedda, Francesco Dessì, Maria Teresa Melis, Stefania Da Pelo, Danila Elisabetta Patta, Sandro Pasci, Daniela Pani, and Stefano Loddo
X3.161 |
Jinn-Chyi Chen, Shin-Hsuan Chen, and Wen-Shun Huang
X3.162 |
Victoriia Kurovskaia and Tatyana Vinogradova
X3.163 |
| presentation
Ananta Man Singh Pradhan, Seung-Rae Lee, Tae-Hyuk Kwon, Ji-Sung Lee, and Yun-Tae Kim
X3.164 |
Ba-Quang-Vinh Nguyen, Yun-Tae Kim, Ji-Sung Lee, and Dae-Ho Yun
X3.165 |
Daria Sokolova and Tatyana Vinogradova
X3.167 |
Damage verification of open type steel pipe Sabo dam influenced by a large-scale debris flow disaster through a numerical simulation
Toshiyuki Horiguchi
X3.168 |
| Highlight
Chih-Chung Chung, Yi-Chien Wu, Wei-Hsian Wang, Zhi-Yu Chen, Ping-Ting Chen, and Sheng-Yu Chuang