Machine Learning for Landslide Susceptibility
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".
Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 4731
Special Issue Editors
Interests: risk assessment and management: dams, landslides, offshore foundations, tunneling; machine learning in geotechnics; geotechnical design; laboratory and in situ testing; interpretation of soil parameters
Interests: geotechnical uncertainty quantification; inherent spatial variability of soils; bayesian updating of geotechnical systems; geohazard risk assessment; machine learning in geotechnics
Special Issues, Collections and Topics in MDPI journals
Interests: slope stability; site investigation; offshore foundations; georisk; geotechnical hazards; monitoring in geotechnical engineering; machine learning; tunneling; early warning…
Special Issue Information
Dear Colleagues,
Landslides pose a serious risk to population, property, and environment in mountainous regions and even in flat areas worldwide. Landslides have caused massive casualties and significant losses and damage to property. In recent years, machine learning (ML) techniques, including deep learning methods, have increasingly been used to model complex landslides. Analyses so far have demonstrated promising predictive ability compared to traditional, deterministic solutions, and physical model testing.
This Special Issue of Applied Sciences seeks to incorporate the latest developments in machine learning with respect to modeling and prediction of landslide susceptibility, including quantitative and qualitative assessments of the classification, volume (or area) and spatial distribution of landslides, as well as the velocity, intensity, and runout (and consequences) of existing or potential landsliding.
Authors are encouraged to submit their latest research and applications in the broad field of “Applications of Machine Learning for Landslide Susceptibility”. Authors are encouraged to also consider how their models can be disseminated, for example, digitally or by means of equations, so that readers and practitioners can make use of them in their own work.
Dr. Suzanne Lacasse
Dr. Zhongqiang Liu
Prof. Dr. Jinhui Li
Dr. Raymond Cheung
Guest Editors
Manuscript Submission Information
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