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Risk Assessment of Landslides Based on Multi-source Data and Machine Learning

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Hazards and Sustainability".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 11718

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

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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

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

Published Papers (11 papers)

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Research

20 pages, 23201 KiB  
Article
Automatic and Efficient Detection of Loess Landslides Based on Deep Learning
by Qingyun Ji, Yuan Liang, Fanglin Xie, Zhengbo Yu and Yanli Wang
Sustainability 2024, 16(3), 1238; https://doi.org/10.3390/su16031238 - 01 Feb 2024
Viewed by 648
Abstract
Frequent landslide disasters on the Loess Plateau in northwestern China have had a serious impact on the lives and production of the people in the region due to the fragile ecological environment and severe soil erosion. The effective monitoring and management of landslide [...] Read more.
Frequent landslide disasters on the Loess Plateau in northwestern China have had a serious impact on the lives and production of the people in the region due to the fragile ecological environment and severe soil erosion. The effective monitoring and management of landslide hazards is hindered by the wide range of landslide features and scales in remotely sensed imagery, coupled with the shortage of local information and technology. To address this issue, we constructed a loess landslide dataset of 11,010 images and established a landslide detection network model. Coordinate Attention (CA) is integrated into the backbone with the aid of the YOLO model to capture precise location information and remote spatial interaction data from landslide images. Furthermore, the neck includes the Convolutional Block Attention Module (CBAM), which prompts the model to prioritize focusing on legitimate landslide objectives while also filtering out background noise to extract valid feature information. To efficiently extract classification and location details from landslide images, we introduce the lightweight Decoupled Head. This enhances detection accuracy for landslide objectives without excessively increasing model parameters. Furthermore, the utilization of the SIoU loss function improves angle perception for landslide detection algorithms and reduces the deviation between the predicted box and the ground truth box. The improved model achieves landslide object detection at multiple scales with a mAP of 92.28%, an improvement of 4.01% compared to the unimproved model. Full article
(This article belongs to the Special Issue Risk Assessment of Landslides Based on Multi-source Data and Machine Learning)
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13 pages, 28394 KiB  
Article
Comparative Study of Deep Neural Networks for Landslide Susceptibility Assessment: A Case Study of Pyeongchang-gun, South Korea
by Jeong-Cheol Kim and Sunmin Lee
Sustainability 2024, 16(1), 245; https://doi.org/10.3390/su16010245 - 27 Dec 2023
Viewed by 623
Abstract
With an increase in local precipitation caused by extreme climatic phenomena, the frequency of landslides and associated damage has also increased. Therefore, compiling fine-scale landslide susceptibility assessment maps based on data from landslide-affected areas is essential. Deep neural network (DNN) and kernel-based DNN(DNNK) [...] Read more.
With an increase in local precipitation caused by extreme climatic phenomena, the frequency of landslides and associated damage has also increased. Therefore, compiling fine-scale landslide susceptibility assessment maps based on data from landslide-affected areas is essential. Deep neural network (DNN) and kernel-based DNN(DNNK) models were used to prepare landslide susceptibility maps of the mountainous Pyeongchang-gun region (South Korea) within a geographic information system framework. To map landslide susceptibility, datasets of landslide occurrence areas, topography, land use, forest, and soil were collected and entered into spatial databases, and 18 factors were then selected from the databases and used as model inputs. The training and test datasets consisted of 1600 and 400 landslide locations, respectively. The test accuracies of the DNN and DNNK models were 98.19% and 97.53% and 94.11% and 92.22% for the area under the receiver operating characteristic curve and the average precision value of the precision-recall curve, respectively. The location of future landslides can now be quickly and efficiently predicted using remote sensing data at a lower cost and with less labor. The landslide susceptibility maps produced in this study can play a role in sustainability and serve as references for establishing policies for landslide prevention and mitigation. Full article
(This article belongs to the Special Issue Risk Assessment of Landslides Based on Multi-source Data and Machine Learning)
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20 pages, 37452 KiB  
Article
Research on Development Characteristics and Landslide Dam Hazard Prediction of Zhuangfang Landslide in the Upper Reaches of the Nu River
by Yong Di, Yunjie Wei, Weijia Tan and Qiang Xu
Sustainability 2023, 15(20), 15036; https://doi.org/10.3390/su152015036 - 19 Oct 2023
Cited by 1 | Viewed by 635
Abstract
The upper reaches of the Nu River have strong tectonic activities and broken rock mass structures, often causing landslide disasters. The Zhuangfang landslide has apparent signs of surface deformation, and there is a risk of further sliding and blocking of the river. Taking [...] Read more.
The upper reaches of the Nu River have strong tectonic activities and broken rock mass structures, often causing landslide disasters. The Zhuangfang landslide has apparent signs of surface deformation, and there is a risk of further sliding and blocking of the river. Taking the Zhuangfang landslide as an example, this paper analyzes the development characteristics and stability through geological field surveys, a drone aerial survey, field drilling, and GEO5 geotechnical engineering software. Then through the indoor tests and RAMMS numerical simulation software, the parameters of the landslide are determined, and the risk of a landslide dam is analyzed. Our results demonstrated that the Zhuangfang landslide is a large-scale landslide with a volume of about 4.5 × 106 m3. The front edge of the landslide is seriously deformed and is in an under-stable state, with risks of sliding and river blockage. The numerical simulation results showed that the total movement time of the landslide was 130 s, and the landslide entered the Nu River at 55 s. However, the landslide does not completely block the river and cannot form a landslide dam. The study proposed a parameter inversion method to determine the landslide mass parameters based on RAMMS numerical simulation software. The related results of this study can provide a reference for the sustainable development of the ecological environment in the Nu River Basin. Full article
(This article belongs to the Special Issue Risk Assessment of Landslides Based on Multi-source Data and Machine Learning)
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22 pages, 7306 KiB  
Article
The Deformation Characteristics of the Zhuka Fault in Lancang River and Its Influence on the Geostress Field
by Daru Hu, Tao Wen, Shuyu Wu, Wanying Huang and Huanchun Zhu
Sustainability 2023, 15(18), 13473; https://doi.org/10.3390/su151813473 - 08 Sep 2023
Viewed by 572
Abstract
The construction of infrastructure projects such as the Sichuan–Tibet Railway and western cascade hydropower stations has led to the increasing development of ultra-long and deeply buried tunnels in an environment characterized by highly active neotectonic movement, which affects the sustainable development of ecological [...] Read more.
The construction of infrastructure projects such as the Sichuan–Tibet Railway and western cascade hydropower stations has led to the increasing development of ultra-long and deeply buried tunnels in an environment characterized by highly active neotectonic movement, which affects the sustainable development of ecological civilization in Tibet. However, the effects of faults resulting from tectonic activity on the distribution of geostress fields have not been systematically studied. This research focuses on the development characteristics and basic type of the Zhuka fault near the RM hydropower station, aiming to analyze the phenomenon of geostress concentration in the study area. Field investigations have revealed significant high-geostress damage on the downstream slope of the lower dam site, situated on the hanging wall of the Zhuka fault. The results indicate a correlation between these high-geostress phenomena and the Zhuka fault, suggesting the concentration of geostress within a certain range on the hanging wall and outside of the fault zone. Stress concentration primarily depends on the characteristics of fault thrusting and fault morphology. The left-lateral strike-slip and thrusting process of the Zhuka fault, combined with NNW-directed tectonic compression stress and sudden changes in fault strike, contribute to geostress concentration within a specific range of the fault hanging wall. The observed high-geostress damage to the hard rock on the valley slope results from the combined effect of construction stress concentration and fourth-order valley incision stress concentration, which influences site selection for the RM hydropower station, thereby highlighting the role of geostress concentration outside the fault zone in engineering practice. This study provides valuable insights into geostress concentration and its implications for sustainable development in the Sichuan–Tibet region. Full article
(This article belongs to the Special Issue Risk Assessment of Landslides Based on Multi-source Data and Machine Learning)
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17 pages, 14116 KiB  
Article
Analysis of Optimal Buffer Distance for Linear Hazard Factors in Landslide Susceptibility Prediction
by Lu Fang, Qian Wang, Jianping Yue and Yin Xing
Sustainability 2023, 15(13), 10180; https://doi.org/10.3390/su151310180 - 27 Jun 2023
Cited by 1 | Viewed by 963
Abstract
A linear hazard-causing factor is the environmental element of landslide susceptibility prediction, and the setting of buffer distance of a linear hazard-causing factor has an important influence on the accuracy of landslide susceptibility prediction based on machine learning algorithms. A geographic information system [...] Read more.
A linear hazard-causing factor is the environmental element of landslide susceptibility prediction, and the setting of buffer distance of a linear hazard-causing factor has an important influence on the accuracy of landslide susceptibility prediction based on machine learning algorithms. A geographic information system (GIS) has generally been accepted in the correlation analysis between linear hazard-causing factors and landslides; the most common are statistical models based on buffer zone analysis and superposition analysis for linear causative factor distances and landslide counts. However, there is a problem in the process of model building: the buffer distance that is used to build the statistical model and its statistical results can appropriately reflect the correlation between the linear disaster-causing factors and landslides. To solve this problem, a statistical model of landslide density and distance of linear disaster-causing factors under different single-loop buffer distances was established based on Pearson’s method with 12 environmental factors, such as elevation, topographic relief, and distance from the water system and road, in Ruijin City, Jiangxi Province to obtain the most relevant single-loop buffer distance linear disaster-causing factor combinations; random forest (RF) machine learning models were then used to predict landslide susceptibility. Finally, the Kappa coefficient and the distribution characteristics of the susceptibility index were used to investigate the modeling laws. The analysis results indicate that the prediction accuracy of the most correlated single-loop buffer distance combination reaches 96.65%, the error rate of non-landslide points is 4.2%, and the error of landslide points is 11.3%, which is higher than the same single-loop buffer distance combination, confirming the reasonableness of the method of using correlation to obtain the linear disaster-causing factor buffer distance. Full article
(This article belongs to the Special Issue Risk Assessment of Landslides Based on Multi-source Data and Machine Learning)
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23 pages, 7854 KiB  
Article
Landslide Susceptibility Mapping in Guangdong Province, China, Using Random Forest Model and Considering Sample Type and Balance
by Li Zhuo, Yupu Huang, Jing Zheng, Jingjing Cao and Donghu Guo
Sustainability 2023, 15(11), 9024; https://doi.org/10.3390/su15119024 - 02 Jun 2023
Cited by 6 | Viewed by 1447
Abstract
Landslides pose a serious threat to human lives and property. Accurate landslide susceptibility mapping (LSM) is crucial for sustainable development. Machine learning has recently become an important means of LSM. However, the accuracy of machine learning models is limited by the heterogeneity of [...] Read more.
Landslides pose a serious threat to human lives and property. Accurate landslide susceptibility mapping (LSM) is crucial for sustainable development. Machine learning has recently become an important means of LSM. However, the accuracy of machine learning models is limited by the heterogeneity of environmental factors and the imbalance of samples, especially for large-scale LSM. To address these problems, we created an improved random forest (RF)-based LSM model and applied it to Guangdong Province, China. First, the RF-based LSM model was constructed using rainfall-induced landslide samples and 13 environmental factors and by exploring the optimal positive-to-negative and training-to-test sample ratios. Second, the performance of the RF-based LSM model was evaluated and compared with three other machine learning models. The results indicate that: (1) the proposed RF-based model has the best performance with the highest area under curve (AUC) of 0.9145, based on optimal positive-to-negative and training-to-test sample ratios of 1:1 and 8:2, respectively; (2) the introduction of rainfall and global human modification (GHM) can increase the AUC from 0.8808 to 0.9145; and (3) rainfall and topography are two dominant factors in Guangdong landslides. These findings can facilitate landslide risk prevention and serve as a technical reference for large-scale accurate LSM. Full article
(This article belongs to the Special Issue Risk Assessment of Landslides Based on Multi-source Data and Machine Learning)
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13 pages, 2087 KiB  
Article
Study on Early Identification of Landslide Perilous Rocks Based on Multi-Dynamics Parameters
by Yanchang Jia, Zhanhui Li, Tong Jiang, Yan Li, Shaokai Wang and Guihao Song
Sustainability 2023, 15(7), 6296; https://doi.org/10.3390/su15076296 - 06 Apr 2023
Cited by 1 | Viewed by 1309
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 [...] Read more.
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)
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24 pages, 4330 KiB  
Article
Special Characteristics and Stability Analysis of Bank Slope Deposits with Special Geotechnical Structures in High and Cold Valleys
by Shuyu Wu, Daru Hu and Tao Wen
Sustainability 2023, 15(7), 6090; https://doi.org/10.3390/su15076090 - 31 Mar 2023
Cited by 1 | Viewed by 1070
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 [...] Read more.
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)
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17 pages, 3940 KiB  
Article
Seismic Signal Characteristics and Numerical Modeling Analysis of the Xinmo Landslide
by Longwei Yang, Yangqing Xu, Luqi Wang and Qiangqiang Jiang
Sustainability 2023, 15(7), 5851; https://doi.org/10.3390/su15075851 - 28 Mar 2023
Cited by 2 | Viewed by 1036
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 [...] Read more.
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)
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18 pages, 6370 KiB  
Article
Prediction of Landslide Displacement Based on the Variational Mode Decomposition and GWO-SVR Model
by Chenhui Wang and Wei Guo
Sustainability 2023, 15(6), 5470; https://doi.org/10.3390/su15065470 - 20 Mar 2023
Cited by 5 | Viewed by 1097
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 [...] Read more.
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)
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24 pages, 28254 KiB  
Article
Multi-Temporal InSAR Deformation Monitoring Zongling Landslide Group in Guizhou Province Based on the Adaptive Network Method
by Yu Zhu, Bangsen Tian, Chou Xie, Yihong Guo, Haoran Fang, Ying Yang, Qianqian Wang, Ming Zhang, Chaoyong Shen and Ronghao Wei
Sustainability 2023, 15(2), 894; https://doi.org/10.3390/su15020894 - 04 Jan 2023
Cited by 1 | Viewed by 1321
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 [...] Read more.
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)
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