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Applied Sciences
Special Issues
Novel Technology in Landslide Monitoring and Risk Assessment
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Novel Technology in Landslide Monitoring and Risk Assessment

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

Deadline for manuscript submissions: 20 June 2024 | Viewed by 3633

Special Issue Editors

Prof. Dr. Wen Nie

E-Mail Website
Guest Editor
School of Resources and Environmental Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
Interests: landslides monitoring; early warning system; machine learning; machine vision; vulnerability assessment
Prof. Dr. Yanlong Chen

E-Mail Website1 Website2
Guest Editor
State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining & Technology, Xuzhou 221116, China
Interests: rock
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wenbin Jian

E-Mail Website
Guest Editor
1. Zijin School of Geology and Mining, Fuzhou University, Fuzhou, China
2. Engineering Research Center of Geological Engineering, Fuzhou University, Fuzhou, China
Interests: risk assessment; landslide disaster prevention

Special Issue Information

Dear Colleagues,

The monitoring and assessment of landslide hazards is important in landslide hazard and risk management. The current advances in information technology improve monitoring technology and risk assessment methods for landslides. Therefore, the core objective of this Special Issue is to bring together knowledge in various fields of slope engineering, monitoring techniques, risk assessment, and artificial intelligence to provide an interdisciplinary forum for the exchange of ideas, communication on issues that cross disciplinary barriers, and dissemination of novel technologies used in landslide monitoring and risk assessment. Contributions may cover scientific computing, statistical modeling methods, and big data applications, including (but not limited to):

  • Design and development of monitoring systems;
  • Remote sensing;
  • Application of GIS technologies;
  • Artificial intelligence, machine learning and deep learning;
  • New technologies, experts and intelligent systems;
  • Establishment of landslide databases;
  • Spatial and temporal prediction techniques for landslides;
  • Sensor networks;
  • Monitoring data processing and analysis;
  • Risk assessment;
  • Cloud computing.

Prof. Dr. Wen Nie
Prof. Dr. Yanlong Chen
Prof. Dr. Wenbin Jian
Guest Editors

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. 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 2400 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.

Keywords

  • landslide
  • monitoring and early warning systems
  • artificial intelligence, machine learning and deep learning
  • risk assessment
  • internet of things
  • GIS
  • remote sensing
  • database
  • spatio-temporal prediction
  • data processing

Published Papers (4 papers)

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Research

12 pages, 9835 KiB  
Article
Electrical Resistivity Tomography (ERT) Investigation for Landslides: Case Study in the Hunan Province, China
by Mengyu Sun, Jianxin Liu, Jian Ou, Rong Liu and Ling Zhu
Appl. Sci. 2024, 14(7), 3007; https://doi.org/10.3390/app14073007 - 3 Apr 2024
Viewed by 638
Abstract
Electrical resistivity tomography is a non-destructive and efficient geophysical exploration method that can effectively reveal the geological structure and sliding surface characteristics inside landslide bodies. This is crucial for analyzing the stability of landslides and managing associated risks. This study focuses on the [...] Read more.
Electrical resistivity tomography is a non-destructive and efficient geophysical exploration method that can effectively reveal the geological structure and sliding surface characteristics inside landslide bodies. This is crucial for analyzing the stability of landslides and managing associated risks. This study focuses on the Lijiazu landslide in Zhuzhou City, Hunan Province, employing the electrical resistivity tomography method to detect effectively the surrounding area of the landslide. The resistivity data of the deep strata were obtained, and the corresponding geophysical characteristics are inverted. At the same time, combined with the existing drilling data, the electrical structure of the landslide body is discussed in detail. The inversion results reveal significant vertical variations in the landslide body’s resistivity, reflecting changes in rock and soil physical properties. Combined with geological data analysis, it can be concluded that the sliding surface is located in the sandy shale formation. Meanwhile, by integrating various geological data, we can conclude that the landslide is currently in a creeping stage. During the rainy season, with rainfall infiltration, the landslide will further develop, posing a risk of instability. It should be promptly addressed through appropriate remediation measures. Finally, based on the results of two-dimensional inversion, this article constructs a three-dimensional surface morphology of the landslide body, which can more intuitively compare and observe the internal structure and material composition of the landslide body. This also serves as a foundation for the subsequent management and stability assessment of landslides, while also paving the way for exploring new perspectives on the formation mechanisms and theories of landslides. Full article
(This article belongs to the Special Issue Novel Technology in Landslide Monitoring and Risk Assessment)
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14 pages, 5946 KiB  
Article
Automatic Extraction Method of Landslide Based on Digital Elevation Model and Digital Orthophoto Map Data Combined in Complex Terrain
by Zhiwei Qiu, Junfeng Li, Yuemin Wang, Yuan Niu and Hui Qian
Appl. Sci. 2024, 14(7), 2771; https://doi.org/10.3390/app14072771 - 26 Mar 2024
Viewed by 457
Abstract
This study aims to accurately determine the distribution of landslides in the complex terrain of Jiangdingya, Nanyu Township, Zhouqu County, Gansu Province. The digital orthophoto map (DOM) and digital elevation model (DEM) are used to accurately identify landslide areas and analyze associated data. [...] Read more.
This study aims to accurately determine the distribution of landslides in the complex terrain of Jiangdingya, Nanyu Township, Zhouqu County, Gansu Province. The digital orthophoto map (DOM) and digital elevation model (DEM) are used to accurately identify landslide areas and analyze associated data. Based on image-based supervised classification, the influence factor constraint analysis is used to further identify and delineate the landslide area. Three mathematical morphology operations—erosion, dilation, and opening—are then applied to automatically identify and extract landslides. Experimental results demonstrate that achieving an accuracy, precision, and recall of 98.02%, 85.24%, and 84.78% shows that it is possible to better avoid interference caused by complex terrain with rich features. High-resolution DEM and DOM data contain rich spectral and texture information. These data can accurately depict geomorphic features of complex terrain and aid in identifying landslide-prone areas when combined with mathematical morphology processing. This contribution is important for identifying landslides in complex terrain and emergency disaster management. Full article
(This article belongs to the Special Issue Novel Technology in Landslide Monitoring and Risk Assessment)
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23 pages, 7102 KiB  
Article
Research on the Evolution Network Model of the Landslide Disaster Chain: A Case Study of the Baige Landslide
by Feng Gao, Xiu Gao, Chun Yang and Jielin Li
Appl. Sci. 2024, 14(2), 499; https://doi.org/10.3390/app14020499 - 5 Jan 2024
Viewed by 809
Abstract
In the context of Western China’s unique geography, recurrent landslide disasters pose substantial threats to both resident safety and economic stability. The escalating frequency of these incidents emphasizes the critical need for innovative disaster research, particularly focused on the concept of a disaster [...] Read more.
In the context of Western China’s unique geography, recurrent landslide disasters pose substantial threats to both resident safety and economic stability. The escalating frequency of these incidents emphasizes the critical need for innovative disaster research, particularly focused on the concept of a disaster chain. This research aims to enhance disaster preparedness and management strategies with the ultimate goal of minimizing losses. On the basis of predecessors, this study changes the previous analysis forms of single or partial disaster events, innovatively collects all secondary disaster events derived from the landslide disaster chain, and builds an evolutionary network model. In concrete terms, our study concentrates on the Baige landslide within the Qinghai-Tibet Plateau, pinpointing sub-hazard events as crucial disaster nodes within the landslide. By establishing directed connections, we have developed a comprehensive landslide disaster chain evolution network model firmly grounded in the principles of disaster chain dynamics and complex network theory. This model encompasses 31 distinct disaster nodes and 77 connecting edges. To assess the inherent risks in the landslide catastrophe chain, we conducted a thorough analysis considering node access degree and clustering coefficients. Critical nodes driving economic losses, such as floods, debris flows, secondary landslides, and downstream water damage, were identified. Additionally, we isolated vulnerable connections within the evolving network, evaluating the susceptibility of each edge. Our research underscores the significance of proactive measures, including pre-disaster monitoring, early warning systems, and timely post-disaster information dissemination. Implementing these actions can play a pivotal role in mitigating the impact of landslide disasters, preserving lives and sustaining regional prosperity. Full article
(This article belongs to the Special Issue Novel Technology in Landslide Monitoring and Risk Assessment)
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17 pages, 10574 KiB  
Article
Prediction of Landslide Deformation Region Based on the Improved S-Growth Curve Model
by Yuyang Li, Wen Nie, Qihang Li, Yang Zhu, Canming Yuan, Bibo Dai and Qiuping Kong
Appl. Sci. 2023, 13(6), 3555; https://doi.org/10.3390/app13063555 - 10 Mar 2023
Viewed by 1056
Abstract
Quantitative research on and the prediction of a landslide deformation area is an important point to accurately and comprehensively understand the failure mechanism of landslides and the degree of slope failure. This study uses image processing techniques to quantitatively identify the area and [...] Read more.
Quantitative research on and the prediction of a landslide deformation area is an important point to accurately and comprehensively understand the failure mechanism of landslides and the degree of slope failure. This study uses image processing techniques to quantitatively identify the area and volume of deformation regions during rainfall-type landslide destabilization under multifactor conditions. The findings revealed that (1) an increase in rainfall intensity and slope angle, as well as the existence of slope crest, will accelerate the process of slope instability. In our study, when the rainfall intensity was 140 mm/h and the landslide volume reached 35.68%, the landslide failure was the most serious. (2) Slopes with high compaction of subsoil as well as those without perimeter pressure are relatively more damaged. (3) The higher the density of vegetation cover, the stronger the protection ability of the slope, and the higher the wind speed, the greater the failure to the slope. Furthermore, an improved S-growth curve model was proposed to predict landslide volumes in 16 sets of experiments. In detail, the proposed S-growth curve model predicted landslide volumes with an average absolute percentage error of 4.34–16.77%. Compared with the time series analysis moving-average method (average absolute percentage error of 6.39–68.89%), the S-growth curve model not only has higher prediction accuracy but also can describe the three stages of deformation region development from a physical perspective and can be applied to the volume during landslide change prediction. Full article
(This article belongs to the Special Issue Novel Technology in Landslide Monitoring and Risk Assessment)
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