Landslides and debris flows: Risk management based on practical experience and numerical simulation
Convener: Johannes Huebl | Co-conveners: Giulia Bossi, Yifei Cui, Alessandro Leonardi
| Tue, 09 Apr, 08:30–10:15, 10:45–12:30
Room L1
| Attendance Tue, 09 Apr, 16:15–18:00
Hall X3

Over the last decade, many researchers and practitioners have contributed to consolidating a landslide and debris flows risk management framework, enhancing techniques and pioneering applications to problems that are otherwise difficult to resolve using conventional methods. However, as extreme rainfall events occur with increasing frequency due to climate change, the threat posed by compound geohazards will inevitably increase. Clearly, a new paradigm of mountain hazard mitigation and management is required. Therefore, developing risk analysis models, which could integrate the hazard dynamic process by using both practical experience and numerical simulation, is a key scientific challenge for effective disaster risk reduction. This session focuses on disaster risk analysis and management methods as well as their coherence with the mechanisms of compound hazards, including initiation, transportation, and deposition. The topics of the presentations include but are not limited to:
(a) Advanced methodology of data collection in the field, the improvement and development of sensor technology and the real time data collection of debris flow and landslides hazards for a better dimensioning of mitigation measures.
(b) Numerical simulation of compound geohazards at the local scale and global scale.
(c) Innovative applications remote sensing data for hazard, vulnerability and risk mapping.
(d) Advances in risk analysis methods by integrating new technologies in hazard data retrieving, hazard simulation and vulnerability assessment of elements at risk.
(e) Optimizing the engineering design for current hazard mitigation and control structure and develop new techniques for disaster control.
Additionally, we welcome submissions concentrating on big data processing, machine learning related to vulnerability, and resilience of the elements at risk.