Landslides and slope instabilities are major hazards worldwide, yet the fundamental mechanisms governing their initiation, evolution, and failure dynamics remain active areas of research. These processes are controlled by complex, heterogeneous subsurface properties and dynamic processes operating across broad temporal (seconds to decades) and spatial scales (grain size to entire slopes). Improving the characterization and forecasting of slope instabilities requires innovative frameworks that explicitly account for these multidimensional variabilities.
This session invites contributions presenting novel methodologies, emerging trends, and case studies in the characterization, monitoring, forecasting, and early warning of slope failures. We particularly encourage submissions that demonstrate the integration of geophysical, geotechnical, geological, and remote sensing datasets to build robust landslide models capable of characterizing subsurface structure, identifying controlling processes, and tracking temporal evolution.
Specific topics of interest include:
Multi-method frameworks combining geophysical, geotechnical, and remote sensing techniques.
Applications of machine learning and AI to landslide hazard assessment, forecasting, data integration, and pattern recognition.
Time-lapse geophysical monitoring of subsurface deformation, hydrological processes, and other changes.
Joint or constrained inversion of geophysical and geomechanical parameters using geological data and geophysical imaging.
Integrated monitoring and early warning frameworks combining in situ, subsurface, and remote observations.
Recognizing the inherently interdisciplinary nature of slope stability research, we welcome contributions that bridge disciplinary boundaries and advance integrated approaches to understanding, monitoring, and anticipating slope failure addressing a broad spectrum of instability types, including natural landslides, engineered slopes, and climate-induced failures.