NH014
Disaster risk cognitive science: Is there anything we can do for future landslide hazards in advance?
Session ID#: 248532
Session Description:
There is growing interest in landslide disasters caused by frequent earthquakes and heavy rains brought about by climate change. What we can do after disaster is limited and it is important to prepare in advance. Disaster risk cognitive science is a new academic field with the mission of mitigating slope disasters in advanced and protecting human lives, properties, and cultural and natural heritage. Landslides have been studied in various research fields, such as geomorphology, geology, geophysics, erosion control, earthquake, but we need a common platform to exchange our research and make discussions to strengthen our activity. This session intends to provide such a place and we invite contributions that report and discuss landslides and related phenomena. We would like to particularly focus on characteristics and mechanisms; investigation and monitoring; forecasting and prediction; disaster management; hazard and risk evaluation; database and data science; and communication with public in general.
Co-Sponsor(s):
Index Terms:
4333 Disaster risk analysis and assessment [NATURAL HAZARDS]
4334 Disaster risk communication [NATURAL HAZARDS]
4339 Disaster mitigation [NATURAL HAZARDS]
4352 Interaction between science and disaster management authorities [NATURAL HAZARDS]
Primary Convener: Koichi Hayashi, Kyoto University, Disaster Prevention Research Institute, Kyoto, Japan
Conveners: Gonghui Wang1, Shintaro Yamasaki2 and Makoto Matsuzawa2, (1)Disaster Prevention Research Institute, Kyoto University, Gokasho, Uji, Japan(2)Kyoto University, Disaster Prevention Research Institute, Kyoto, Japan
Challenges and Applicability of Geophysical Exploration Methods in Investigating Slip Surfaces of Large-Scale Landslides (1905096)
Hayato Ishida1, Kento Shinjo1, Koichi Hayashi2, Shuhei Shiraki3, Yusaku Morita3 and Takahiro Aomatsu3, (1)Nippon Koei Co., Ltd., Osaka, Japan, (2)Disaster Prevention Research Institute, Kyoto University, Kyoto, Japan, (3)Wakayama Prefecture, Higashimuro, Japan
Application and Challenges of a Machine Learning-Based Zero-Order Basin Extraction Method for Preventive Maintenance Against Large-Scale Disasters During Havey Rainfall: A Case Study the 2024 Noto Peninsula Heavy Rain (1905469)
Mitsuru Yabe1, Shunsuke Hamada2, Dai Nobuoka2, Natsumi Itoya3 and Yasunori Katsume1, (1)OYO Corporation, Disaster Prevention and Infrastructure Business Division, Saitama, Japan, (2)OYO Corporation, DX Promotion Headquarters, Tokyo, Japan, (3)OYO Corporation, Disaster Risk Reduction Business Division, Saitama, Japan
A Case Study of Investigation and Analysis on a Highly Active Landslide (1928948)
Kento Shinjo1, Hayato Ishida1, Koichi Hayashi2, Shuhei Shiraki3, Yusaku Morita3 and Takahiro Aomatsu3, (1)Nippon Koei Co., Ltd., Osaka, Japan, (2)Disaster Prevention Research Institute, Kyoto University, Kyoto, Japan, (3)Wakayama Prefecture, Higashimuro, Japan
Topographic and Lithological Controls on Landslide Size and Mobility (1934354)
Suneth Neranjan1, Taro Uchida2, Yosuke Yamakawa2 and Ai Kawakami2, (1)Graduate School of Science and Technology, University of Tsukuba, Tsukuba, Japan, (2)Institute of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan
Analysis of Rainfall-induced Landslide Depth Patterns and the Effect of Slope Angle Using Airborne Laser Survey Data (1935041)
Yuki Kudo1, Taro Uchida2, Yosuke Yamakawa2 and Misa Tsushima1, (1)Graduate School of Science and Technology, University of Tsukuba, Tsukuba, Japan, (2)Institute of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan
Instability of a Rainfall-Induced Landslide Driven by Pore Pressure Generation in the Basal Shear Zone (1935699)
Xuan Kang, BOKU University of Natural Resources and Life Sciences, Vienna, Austria, Wei Wu, Universität für Bodenkultur Wien, Institut für Geotechnik, Wien, Austria and Shun Wang, Wuhan University, Wuhan, China
Understanding Earthquake-Induced Submergence through Lake Biwa Shimosakahama Sengen Site and Global Case Studies (1939326)
Yu Qian1, Shintaro Yamasaki2, Koichi Hayashi2 and Hisashi Nakagawa3, (1)Kyoto University, Graduate School of Science, Kyoto, Japan, (2)Kyoto University, Disaster Prevention Research Institute, Kyoto, Japan, (3)Toyohashi City Museum of Art and History, Aici, Japan
Investigation of Weathered Zone Related to Shallow Landslides Triggered by the 2024 Noto Peninsula Earthquake in Oku-Noto, Japan (1939655)
Makoto Matsuzawa, Kyoto University, Disaster Prevention Research Institute, Kyoto, Japan, Takuma Watakabe, Forestry and Forest Products Research Institute, Kansai Research Center, Kyoto, Japan, Msasato Sato, National Research Institute for Earth Science and Disaster Resilience, Tsukuba, Japan and Forestry and Forest Products Research Institute
Numerical Simulation-Guided Machine Learning for Landslide Risk Assessment (1964826)
Zhuping Sheng1, Yi Liu2, Seok-Jun Kang3 and Oludare Owolabi3, (1)Morgan State University, Baltimore, MD, United States, (2)Morgan State University, Civil and Environmental Engineering, Baltimore, MD, United States, (3)Morgan State University, Civil and Environmental Engineering, Baltimore, United States
Landslide time-to-failure - Comparing rate-and-state friction laws (1969641)
Subhasish Mukherjee1,2, Pathikrit Bhattacharya1,2 and Santanu Misra3, (1)National Institute of Science Education and Research, School of Earth and Planetary Sciences, Khurda, India, (2)Homi Bhabha National Institute, Mumbai, India, (3)Indian Institute of Technology Kanpur, Department of Earth Sciences, Kanpur, India
The coupled evolution of urban growth and landslide risk: Lessons from a century of disasters in Medellín, Colombia (1979391)
Edier Aristizábal1, Ugur Ozturk2, Sara Nieto Ms3, Alexander Guerra sr4, Asaf Aguilar sr4 and Juan David Moreno4, (1)Medellin, Antioquia, COLOMBIA, (2)Universität Potsdam, Potsdam, Brandenburg, GERMANY, (3)Universidad Nacional de Colombia, Medellin, Geociencias y Medio Ambiente, Medellin, Colombia, (4)Departamento Administrativo de Gestión del Riesgo DAGRD, Medellin, Colombia
