The Artificial Intelligence Models for Landslide Hazard Assessment
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".
Deadline for manuscript submissions: closed (30 September 2019) | Viewed by 3602
Special Issue Editors
Interests: engineering geology; landslide hazard and risk assessment; artificial intelligence models; applied hydrogeology; cultural heritages and natural risk; applied geomorphology
Special Issues, Collections and Topics in MDPI journals
Interests: engineering geology; applied hydrogeology; landslide hazard and risk assessment; artificial intelligence models; applied geomorphology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
We would like to invite you to participate in this Special Issue, which will focus primarily on the study and the application of the artificial intelligence methods for landslide hazard assessment and mapping in anthropic areas (urban areas, roads, communication infractures, etc.).
Landslides are geomorphological phenomena due to natural processes and human activities that play a key role in the landscape evolution, mainly in mountainous and hilly environments, and represent a serious hazard in many areas of the world because of its social and economic effects. Landslides are responsible both for direct and indirect damages, may cause loss of life and property, and damage natural resources and hamper infrastructure such as roads, bridges, and communication lines. Additionally, landslides represent one of the greater types of natural disasters that have occurred worldwide in recent years. This trend is expected to continue in the future, due to increased unplanned urbanization and development, continued deforestation, and increased regional precipitation in landslide-prone areas due to climatic change.
The assessement of landslide susceptibility and hazard has been recognized by the scientific community as one of the most significant tools in the landslide and monitoring studies for landslide risk evaluation and its management and mitigation.
Over the last decades, several methods, both qualitative and quantitative, for landslide hazard assessment and mapping have been developed, such as geomorphological approach, heuristic approach, probabilistic and deterministic approach, statistical analysis, and multicriteria decision-making models.
In the last years, artificial intelligence (AI) models are increasingly being utilized as promising tools to evaluate and map landslide hazards. These models are based on innovative methods and techniques, such as artificial neural networks, fuzzy logic, neuro-fuzzy logic, genetic algorithms, machine learning, and expert systems.
This Special Issue aims to collect original contributions on the assessment of landslide hazard in anthropized areas, especially in urban areas or communication lines (roads, railway axes, etc.) or civil engineering works (dams, underground works, pipelines, etc.), using models and methods based on artificial intelligence applications.
Prof. Dr. Francesco Sdao
Assist. Prof. Dr. Filomena Canora
Guest Editors
Manuscript Submission Information
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Keywords
- landslide hazard assessment
- artificial intelligence methods
- GIS
- management and mitigation of landslide risk
- urban areas
- roads and transportation corridors
