Intelligent Landslide Early Warning: From Multi-Source Sensing to AI-Driven Forecasting
A special issue of Geosciences (ISSN 2076-3263). This special issue belongs to the section "Natural Hazards".
Deadline for manuscript submissions: 31 August 2026 | Viewed by 34
Special Issue Editors
Interests: geological disaster mechanism and monitoring and early warning; landslide risk assessment
Special Issues, Collections and Topics in MDPI journals
Interests: landslide geological hazard prediction and forecasting; geotechnical stability assessment
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Landslides pose significant risks to infrastructure and communities worldwide, often triggering a cascade of secondary hazards such as landslide-dammed lakes, debris flows, and landslide-induced waves, which can amplify disaster impacts. Understanding their mechanisms and improving early warning systems requires integrating multi-source monitoring technologies, physical model tests, and artificial intelligence, and recent advances in remote sensing, sensor networks, high-performance computing, and AI-driven data analysis have revolutionized landslide and secondary hazard investigations, enabling more accurate predictions and dynamic risk assessments. This Special Issue seeks to showcase cutting-edge research and innovative methodologies in landslide and secondary disaster monitoring, physical and numerical modeling, and AI applications.
We invite contributions on experimental studies, case studies, and novel technological approaches that enhance our understanding of landslide processes and improve hazard mitigation strategies, and we particularly encourage interdisciplinary contributions bridging geosciences, data science, and engineering domains.
Dr. Ting Xiao
Dr. Linwei Li
Guest Editors
Dr. Jizhixian Liu
Guest Editor Assistant
Manuscript Submission Information
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Keywords
- landslide monitoring
- multi-source sensing
- model test
- artificial intelligence
- early warning system
- risk assessment
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