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Special Issue "Risk Assessment of Landslides Based on Multi-Source Data and Machine Learning"
A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Hazards and Sustainability".
Deadline for manuscript submissions: 31 December 2023 | Viewed by 3278
Special Issue Editors
Interests: landslide susceptibility; slope stability; rock mechanics
Interests: landslide risk assessment, geotechnical reliability analysis, machine learning
Interests: landslide stability analysis and prediction
Interests: landslide risk assessment; landslide early warning system
Interests: soil dynamics; tunnelling engineering; rock rheology
Special Issue Information
Landslides are one of the most common geological disasters and are usually induced by rainfall, earthquakes, and human activities. Today, with the dramatic change in global climate, landslides occur more frequently. In this context, the accurate and efficient completion of landslide risk assessment is of great significance for regional sustainable development, since misjudgment of landslide risks can lead to disastrous consequences. For example, the Vajont landslide on October 9, 1963, caused nearly 2000 deaths. This is because a fatal error occurred in the stability of the reservoir bank under the complex mechanical environment, which led to disastrous consequences.
The risk assessment of landslides involves a lot of research fields. Generally, the evolution mechanism of landslides has always been the key to determining the risk level. Detailed site investigation will help integrate the overall process of landslides effectively. For landslides with progressive deformation, multi-source monitoring data can be used to further analyze the development trend of landslides. With the development of new monitoring technology, a high-precision, long-time series of information can be obtained, such as ground and deep deformation, pore water pressure, temperature, humidity, stress, etc. Reliable risk assessment can be linked with the fusion and mining of massive multi-source monitoring data. Machine learning has a strong nonlinear processing ability and has been used in landslide risk assessment by more and more researchers. Moreover, with the continuous updating of calculation methods, many numerical simulation methods are often introduced to analyze the evolution process of landslides, especially the chain reaction of secondary disasters. Any relevant studies that are conducive to determining the risk of landslides are welcome.
Dr. Luqi Wang
Dr. Lin Wang
Dr. Yankun Wang
Dr. Ting Xiao
Dr. Zhiyong Liu
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- the evolution process of landslides
- monitoring of landslides
- numerical simulation of landslides
- deformation of landslides
- landslide susceptibility