Special Issue "Assessment of Landslide Susceptibility and Hazard in the Big Data Era"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences and Geography".

Deadline for manuscript submissions: 31 December 2020.

Special Issue Editors

Prof. Hyuck-Jin Park
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Guest Editor
Department of Energy Resources and Geosystem Engineering, Sejong University, Seoul, 05006, Korea
Interests: landslide hazard; monitoring and modelling of basin scale surface processes; natural hazards; applications of remote sensing to landslide studies; scaling processes in geomorphology
Prof. Dr. Filippo Catani
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Guest Editor
Department of Earth Sciences, University of Florence, Engineering Geology and Geomorphology Research Group, Via La Pira 4, 50121 Firenze, Italy
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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Prof. Alessandro Simoni
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Guest Editor
Department of Biological, Geological and Environmental Sciences (BiGeA), University of Bologna, Bologna, Italy
Interests: landslide and debris flow hazard assessment, field monitoring of landslides, slope hydrology, landslide triggering and propagation, InSAR.
Prof. Matteo Berti
Website
Guest Editor
Dipartimento di Scienze Biologiche, Geologiche e Ambientali - Università di Bologna, Italy
Interests: GeologyHydrologyLandslides

Special Issue Information

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

Manuscript Submission Information

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Keywords

  • 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

Published Papers (2 papers)

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Research

Open AccessArticle
Determination of the Stability of High-Steep Slopes by Global Navigation Satellite System (GNSS) Real-Time Monitoring in Long Wall Mining
Appl. Sci. 2020, 10(6), 1952; https://doi.org/10.3390/app10061952 - 12 Mar 2020
Cited by 1
Abstract
Surface movement and deformation induced by underground coal mining causes slopes to collapse. Global Navigation Satellite System (GNSS) real-time monitoring can provide early warnings and prevent disasters. A stability analysis of high-steep slopes was conducted in a long wall mine in China, and [...] Read more.
Surface movement and deformation induced by underground coal mining causes slopes to collapse. Global Navigation Satellite System (GNSS) real-time monitoring can provide early warnings and prevent disasters. A stability analysis of high-steep slopes was conducted in a long wall mine in China, and a GNSS real-time monitoring system was established. The moving velocity and displacement at the monitoring points were an integrated response to the influencing factors of mining, topography, and rainfall. Underground mining provided a continuous external driving force for slope movement, the steep terrain provided sufficient slip conditions in the slope direction, and rainfall had an acceleration effect on slope movement. The non-uniform deformation, displacement field, and time series images of the slope body revealed that ground failure was concentrated in the area of non-uniform deformation. The non-uniform deformation was concentrated ahead of the working face, the speed of deformation behind the working face was reduced, the instability of the slope body was increased, and the movement of the top of the slope was larger than at the foot. The high-steep slope stability in the mine was influenced by the starting deformation (low stability), iso-accelerated deformation (increased stability), deformation deceleration (reduced stability), and deformation remaining unchanged (improved stability). Full article
(This article belongs to the Special Issue Assessment of Landslide Susceptibility and Hazard in the Big Data Era)
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Open AccessArticle
Spatial Prediction of Landslides Using Hybrid Integration of Artificial Intelligence Algorithms with Frequency Ratio and Index of Entropy in Nanzheng County, China
Appl. Sci. 2020, 10(1), 29; https://doi.org/10.3390/app10010029 - 19 Dec 2019
Cited by 6
Abstract
The main object of this study is to introduce hybrid integration approaches that consist of state-of-the-art artificial intelligence algorithms (SysFor) and two bivariate models, namely the frequency ratio (FR) and index of entropy (IoE), to carry out landslide spatial prediction research. Hybrid integration [...] Read more.
The main object of this study is to introduce hybrid integration approaches that consist of state-of-the-art artificial intelligence algorithms (SysFor) and two bivariate models, namely the frequency ratio (FR) and index of entropy (IoE), to carry out landslide spatial prediction research. Hybrid integration approaches of these two bivariate models and logistic regression (LR) were used as benchmark models. Nanzheng County was considered as the study area. First, a landslide distribution map was produced using news reports, interpreting satellite images and a regional survey. A total of 202 landslides were identified and marked. According to the previous studies and local geological environment conditions, 16 landslide conditioning factors were chosen for landslide spatial prediction research: elevation, profile curvature, plan curvature, slope angle, slope aspect, stream power index (SPI), topographic wetness index (TWI), sediment transport index (STI), distance to roads, distance to rivers, distance to faults, lithology, rainfall, soil, normalized different vegetation index (NDVI), and land use. Then, the 202 landslides were randomly segmented into two parts with a ratio of 70:30. Seventy percent of the landslides (141) were used as the training dataset and the remaining landslides (61) were used as the validating dataset. Next, the evaluation models were built using the training dataset and compared by the receiver operating characteristics (ROC) curve. The results showed that all models performed well; the FR_SysFor model exhibited the best prediction ability (0.831), followed by the IoE_SysFor model (0.819), IoE_LR model (0.702), FR_LR model (0.696), IoE model (0.691), and FR model (0.681). Overall, these six models are practical tools for landslide spatial prediction research and the results can provide a reference for landslide prevention and control in the study area. Full article
(This article belongs to the Special Issue Assessment of Landslide Susceptibility and Hazard in the Big Data Era)
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