Special Issue "Assessment of Landslide Susceptibility and Hazard in the Big Data Era"
Deadline for manuscript submissions: 31 December 2020.
Interests: landslide hazard; monitoring and modelling of basin scale surface processes; natural hazards; applications of remote sensing to landslide studies; scaling processes in geomorphology
Interests: landslide hazard; monitoring and modelling of basin scale surface processes; natural hazards; applications of remote sensing to landslide studies; oil & gas environmental impact and risk; surface monitoring in open pit mines; scaling processes in geomorphology; machine learning applied to land surface processes
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Interests: landslide and debris flow hazard assessment, field monitoring of landslides, slope hydrology, landslide triggering and propagation, InSAR.
Landslides have been recognized as a major threat to lives and properties in most mountainous regions of the world. Statistics from the Center for Research on Epidemiology of Disasters (CRED) show that landslides are responsible for at least 17% of all fatalities from natural hazards worldwide (Lacasse and Nadim, 2009). Therefore, various studies have been performed to predict landslide occurrences and reduce the damage caused by landslides. Assessments of landslide susceptibility and hazards are generally carried out by scientists from fields such as engineering geology, geomorphology, geophysics, hydrology, soil science, and geography. However, at present, this rapidly growing field is becoming multi-disciplinary as different technologies are integrated in order to achieve a better understanding of landslide initiation mechanisms and processes. Recently, much of the progress has been made by new advancements in technology, such as machine learning, UAVs, satellite images, and simulation models. Most of these new techniques bring a massive volume of both structured and unstructured data, and require specialized algorithms and methods to produce fruitful results. Data science is changing a number of scientific disciplines, offering new opportunities for discovering new knowledge and providing effective tools to simulate complex phenomena. This revolution has just begun, and there is a growing interest in the application of data science methods for landslide studies, both to develop black-box prediction models and to support the classical physics-based methods.
This Special Issue of Applied Sciences aims to encourage researchers to address the recent progress in the field of landslide susceptibility and hazard assessment, taking advantage of the new opportunities in the Big Data Era in topics including, but not limited to, the following:
- Analysis of Big Data coming from high-frequency monitoring networks;
- Machine learning methods for the assessment of landslide initiation;
- Big Data assimilation in landslide numerical models;
- Data mining of large data storages from remote sensing;
- Model validation through unstructured data mining, and social media data mining.
Prof. Hyuck-Jin Park
Prof. Filippo Catani
Prof. Alessandro Simoni
Prof. Matteo Berti
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 papers will be 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. Applied Sciences 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 1800 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.
- Landslide inventory mapping
- Landslide hazard recognition and assessment
- Data-driven landslide susceptibility assessment
- Physically based landslide hazard assessment
- Statistical methods for predicting the spatial occurrence of landslides
- Temporal prediction of landslide occurrence
- Data-driven models to predict temporal occurrence of landslides
- Analysis of Big Data from landslide monitoring
- Machine learning for landslide prediction
- Data mining of Earth Observation for landslide prediction
- Big Data assimilation in landslide numerical models
- Model validation through unstructured data mining