Submit to Special Issue Submit Abstract to Special Issue Review for Sustainability Edit a Special Issue

Journal Menu

Journal Browser

Special Issue "Risk Assessment of Landslides Based on Multi-Source Data and Machine Learning"

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

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

Share This Special Issue

Special Issue Editors

Dr. Luqi Wang

E-Mail Website
Guest Editor

School of Civil Engineering, Chongqing University, Chongqing 400045, China
Interests: landslide susceptibility; slope stability; rock mechanics

Dr. Lin Wang

E-Mail Website
Guest Editor

School of National Safety and Emergency Management, Beijing Normal University, Beijing, China
Interests: landslide risk assessment, geotechnical reliability analysis, machine learning

Dr. Yankun Wang
E-Mail Website
Guest Editor

School of Geosciences, Yangtze University, Wuhan, China
Interests: landslide stability analysis and prediction

Dr. Ting Xiao

E-Mail Website
Guest Editor

School of Geosciences and Info-Physics, Central South University, Changsha, China
Interests: landslide risk assessment; landslide early warning system

Dr. Zhiyong Liu

E-Mail Website
Guest Editor

Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai, China
Interests: soil dynamics; tunnelling engineering; rock rheology

Special Issue Information

Dear Colleagues,

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
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. 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.

Keywords

the evolution process of landslides
monitoring of landslides
numerical simulation of landslides
deformation of landslides
landslide susceptibility

Published Papers (5 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Open AccessArticle

Study on Early Identification of Landslide Perilous Rocks Based on Multi-Dynamics Parameters

and

Sustainability 2023, 15(7), 6296; https://doi.org/10.3390/su15076296 - 06 Apr 2023

Viewed by 848

Abstract

The dynamics parameters cause sudden change during the damage of the structural plane of landslide perilous rocks, and these can be easily accessed. Therefore the changes in dynamics parameters can effectively achieve early identification, stability evaluation, and monitoring and pre-alarming of the perilous rocks. Seven kinds of dynamic indexes, such as pulse indicator, margin index, the center of gravity frequency, root mean square frequency, impact energy, relative energy of the first frequency band, and damping ratio, are introduced and the early identification of landslide perilous rock is achieved based on the support vector machines (SVM) model, improved by particle swarm optimization algorithm. A laser vibrometer collected seven dynamic indexes of two rock masses on the reservoir bank slope in Baihebao Reservoir, China. Based on the particle group optimization algorithm optimization support vector (PSO–SVM) perilous rocks recognition model, and seven dynamic indicators, the stability of two rock masses was recognized with high efficiency and accuracy. The identification results were consistent with the landslide perilous rock identification results based on natural vibration frequency, and the results verify the accuracy of the PSO–SVM perilous rocks identification model. The results show that the sensitivity order of each identification index is: root mean square frequency > margin index > relative energy of the first frequency band > center of gravity frequency > impact energy > pulse indicator > damping ratio. The accuracy of the multi-dynamics parameters landslide perilous rock mass identification model can be improved by selecting appropriate dynamic indexes with good sensitivity. The research results have high theoretical significance and application value for early identification of landslide perilous rocks, stability evaluation, and safety monitoring, and early warning. Full article

(This article belongs to the Special Issue Risk Assessment of Landslides Based on Multi-Source Data and Machine Learning)

► Show Figures

Figure 1

Open AccessArticle

Special Characteristics and Stability Analysis of Bank Slope Deposits with Special Geotechnical Structures in High and Cold Valleys

Shuyu Wu

Daru Hu

and

Tao Wen

Sustainability 2023, 15(7), 6090; https://doi.org/10.3390/su15076090 - 31 Mar 2023

Viewed by 404

Abstract

Due to the special internal and external dynamic action of the Qinghai-Tibet Plateau, the high and cold valleys are typically characterized by high-steep terrain, dry and cold climate, lithologic diversity, complex geological structure, and frequent occurrence of earthquakes. In this study, the types of special geotechnical structures of bank slope deposits in high and cold valleys are summarized based on field investigation, field and laboratory tests, and numerical simulation. These special deposits include colluvial-deluvial deposits, terrace deposits, early debris flow deposits, and landslide deposits. The formation mechanism, physical and mechanical properties, and stability analysis of these deposits were studied. The results show that the formation mechanism of various deposits is different, which is closely related to the intense geological tectonic action, the weathering and unloading action intensified by freezing and thawing cycles, and the special rock and soil structure in the high and cold valleys. Different material compositions have obvious effects on the physical and mechanical properties of the deposits, thus affecting the stability and deformation characteristics of the deposits. Under natural and saturated conditions, the stability of different types of the deposits is different, which is mainly related to the special geotechnical structure of various deposits. Compared with that before the reservoir impoundment, the stability factor of various deposits after the reservoir impoundment is significantly reduced. The performances can be provided as a reference for evaluating the stability of bank slope deposits in high and cold valleys. Full article

(This article belongs to the Special Issue Risk Assessment of Landslides Based on Multi-Source Data and Machine Learning)

► Show Figures

Figure 1

Open AccessArticle

Seismic Signal Characteristics and Numerical Modeling Analysis of the Xinmo Landslide

and

Sustainability 2023, 15(7), 5851; https://doi.org/10.3390/su15075851 - 28 Mar 2023

Viewed by 416

Abstract

Due to the high elevation and huge potential energy of high-level landslides, they are extremely destructive and have prominent kinetic-hazard effects. Studying the kinetic-hazard effects of high-level landslides is very important for landslide risk prevention and control. In this paper, we focus on the high-level landslide that occurred in Xinmo on 24 June 2017. The research is carried out based on a field geological survey, seismic signal analysis, and the discrete element method. Through ensemble empirical mode decomposition (EEMD) and Fourier transformation, it is found that the seismic signals of the Xinmo landslide are mainly located at low frequencies of 0–10 Hz, and the dominant frequency range is 2–8 Hz. In addition, the signal time-frequency analysis and numerical simulation calculation results reveal that the average movement distance of the sliding body was about 2750 m, and the average movement speed was about 22.9 m/s. The movement process can be divided into four main stages: rapid start, impact loading, fragmentation and migration, and scattered accumulation stages. We also provide corresponding suggestions for the zoning of high-level landslide geological hazards. Full article

(This article belongs to the Special Issue Risk Assessment of Landslides Based on Multi-Source Data and Machine Learning)

► Show Figures

Figure 1

Open AccessArticle

Prediction of Landslide Displacement Based on the Variational Mode Decomposition and GWO-SVR Model

Chenhui Wang

and

Wei Guo

Sustainability 2023, 15(6), 5470; https://doi.org/10.3390/su15065470 - 20 Mar 2023

Cited by 1 | Viewed by 460

Abstract

Accurate prediction of landslide displacement is an effective way to reduce the risk of landslide disaster. Under the influence of periodic precipitation and reservoir water level, many landslides in the Three Gorges Reservoir area underwent significant displacement deformation, showing a similar step-like deformation curve. Given the nonlinear characteristics of landslide displacement, a prediction model is established in this study according to the variational mode decomposition (VMD) and support vector regression (SVR) optimized by gray wolf optimizer (GWO-SVR). First, the original data are decomposed into trend, periodic and random components by VMD. Then, appropriate influential factors are selected using the grey relational degree analysis (GRDA) method for constructing the input training data set. Finally, the sum of the three displacement components is superimposed as the total displacement of the landslide, and the feasibility of the model is subsequently tested. Taking the Shuizhuyuan landslide in the Three Gorges Reservoir area as an example, the accuracy of the model is verified using the long time-series monitoring data. The results indicate that the newly proposed model achieves a relatively good prediction accuracy with data decomposition and parameter optimization. Therefore, this model can be used for the predict the accuracy of names and affiliations ion of landslide displacement in the Three Gorges Reservoir area. Full article

(This article belongs to the Special Issue Risk Assessment of Landslides Based on Multi-Source Data and Machine Learning)

► Show Figures

Figure 1

Open AccessArticle

Multi-Temporal InSAR Deformation Monitoring Zongling Landslide Group in Guizhou Province Based on the Adaptive Network Method

and

Sustainability 2023, 15(2), 894; https://doi.org/10.3390/su15020894 - 04 Jan 2023

Cited by 1 | Viewed by 736

Abstract

Due to the influence of atmospheric phase delays and terrain fluctuation in complex mountainous areas, traditional PS-InSAR technology often fails to select enough measurement points (MPs) and loses effective MPs during phase unwrapping. To solve this problem, this paper proposes an adaptive network construction algorithm, which combines the permanent scatterer (PS) points with the distributed scatterer (DS) points. Firstly, to ensure the extraction quality of the DS points, the covariance matrix of DS points is estimated robustly. Secondly, based on the traditional Delaunay triangulation network, an adaptive network construction method is proposed, which can adaptively increase edge redundancy and network connectivity by considering the edge length, edge coherence, edge number, and spatial distribution. Finally, a total of 31 RADARSAT-2 SAR images that cover the Zongling landslide group in Guizhou Province were used to prove the effectiveness of proposed method. The results show that the quantity of available DS points can be increased by 23.6%, through the robust estimation of the covariance matrix. In addition, it is demonstrated that the proposed network construction algorithm can balance the number, distribution, and quality of edges in the dense and sparse areas of MPs adaptively. This adaptive network construction approach can maintain good connectivity and avoid losing effective MPs to the greatest extent, especially when the scattering points are far away from the reference points. In short, the proposed algorithm improves the number of effective MPs and accuracy of phase unwrapping. Full article

(This article belongs to the Special Issue Risk Assessment of Landslides Based on Multi-Source Data and Machine Learning)

► Show Figures

Journal Menu

Journal Browser

Special Issue "Risk Assessment of Landslides Based on Multi-Source Data and Machine Learning"

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (5 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI