Assessment of Landslide Susceptibility and Hazard in the Big Data Era

Dear Colleagues,

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
Guest Editors

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• 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