Database, Mechanism and Risk Assessment of Slope Geologic Hazards

Topic Information

Dear Colleagues,

The slope geo-disaster is a significant hazard in mountainous areas. With an extreme climate and tectonic events (i.e., rainfall, wildfire, earthquake, and snow or ice melting) becoming frequent, slope failures are becoming more and more common throughout the world. Landslides, debris flows, and avalanches are the three main sub-categories of slope instabilities. They cause serious casualties and economic loss by burying buildings and farmlands, blocking rivers, destroying roads and railways, and inducing fires. Thus, slope instability is the hot topic in earth science research. So far, the most effective way to explore the temporal and spatial distribution laws and cause mechanisms of slope failures has been based on disasters that have already happened. Though lots of related research has been published, it is necessary to keep our eyes on different kinds of slope failures in various places. This topic focuses on slope geo-disasters and collects articles on disaster detection and mapping, database compiling, cause mechanisms, susceptibility, and risk mapping. Topics of interest include, but are not limited to, the following:

• New techniques to detect slope instability (including landslides, debris flows, and avalanches);
• Database of slope instability hazards related to extreme events (e.g., rainfalls, earthquakes, or wildfires) or mountainous areas;
• Characteristics and mechanisms of slope instabilities;
• Numerical modeling and the whole life-circle analyses of large slope failure(s);
• Susceptibility mapping and risk assessment of slope failures;
• Post-failure evolution and prediction of slope geo-disasters temporally and spatially.

Prof. Dr. Chong Xu
Dr. Yingying Tian
Dr. Xiaoyi Shao
Dr. Zikang Xiao
Dr. Yulong Cui
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Big Data and Cognitive Computing BDCC	3.7	7.1	2017	25.3 Days	CHF 1800
Data data	2.2	4.3	2016	26.8 Days	CHF 1600
Environments environments	3.5	5.7	2014	22.8 Days	CHF 1800
Geosciences geosciences	2.4	5.3	2011	23.5 Days	CHF 1800
Remote Sensing remotesensing	4.2	8.3	2009	23.9 Days	CHF 2700

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Published Papers (10 papers)

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32 pages, 30650 KiB  

Open AccessArticle

A Novel Strategy Coupling Optimised Sampling with Heterogeneous Ensemble Machine-Learning to Predict Landslide Susceptibility

by Yongxing Lu, Honggen Xu, Can Wang, Guanxi Yan, Zhitao Huo, Zuwu Peng, Bo Liu and Chong Xu

Remote Sens. 2024, 16(19), 3663; https://doi.org/10.3390/rs16193663 - 1 Oct 2024

Viewed by 1646

Abstract

The accuracy of data-driven landslide susceptibility prediction depends heavily on the quality of non-landslide samples and the selection of machine-learning algorithms. Current methods rely on artificial prior knowledge to obtain negative samples from landslide-free regions or outside the landslide buffer zones randomly and [...] Read more.

The accuracy of data-driven landslide susceptibility prediction depends heavily on the quality of non-landslide samples and the selection of machine-learning algorithms. Current methods rely on artificial prior knowledge to obtain negative samples from landslide-free regions or outside the landslide buffer zones randomly and quickly but often ignore the reliability of non-landslide samples, which will pose a serious risk of including potential landslides and lead to erroneous outcomes in training data. Furthermore, diverse machine-learning models exhibit distinct classification capabilities, and applying a single model can readily result in over-fitting of the dataset and introduce potential uncertainties in predictions. To address these problems, taking Chenxi County, a hilly and mountainous area in southern China, as an example, this research proposes a strategy-coupling optimised sampling with heterogeneous ensemble machine learning to enhance the accuracy of landslide susceptibility prediction. Initially, 21 landslide impact factors were derived from five aspects: geology, hydrology, topography, meteorology, human activities, and geographical environment. Then, these factors were screened through a correlation analysis and collinearity diagnosis. Afterwards, an optimised sampling (OS) method was utilised to select negative samples by fusing the reliability of non-landslide samples and certainty factor values on the basis of the environmental similarity and statistical model. Subsequently, the adopted non-landslide samples and historical landslides were combined to create machine-learning datasets. Finally, baseline models (support vector machine, random forest, and back propagation neural network) and the stacking ensemble model were employed to predict susceptibility. The findings indicated that the OS method, considering the reliability of non-landslide samples, achieved higher-quality negative samples than currently widely used sampling methods. The stacking ensemble machine-learning model outperformed those three baseline models. Notably, the accuracy of the hybrid OS–Stacking model is most promising, up to 97.1%. The integrated strategy significantly improves the prediction of landslide susceptibility and makes it reliable and effective for assessing regional geohazard risk. Full article

(This article belongs to the Topic Database, Mechanism and Risk Assessment of Slope Geologic Hazards)

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25 pages, 36124 KiB  

Open AccessArticle

Study of Earthquake Landslide Hazard by Defining Potential Landslide Thickness Using Excess Topography: A Case Study of the 2014 Ludian Earthquake Area, China

by Pengfei Zhang, Chong Xu, Xiaoli Chen, Qing Zhou, Haibo Xiao and Zhiyuan Li

Remote Sens. 2024, 16(16), 2951; https://doi.org/10.3390/rs16162951 - 12 Aug 2024

Viewed by 1274

Abstract

Influenced by the combined effects of crustal uplift and river downcutting, rivers with significant potential energy are often found in high mountain and canyon areas. Due to the active tectonic movements that these areas have experienced or are currently experiencing, geological hazards frequently [...] Read more.

Influenced by the combined effects of crustal uplift and river downcutting, rivers with significant potential energy are often found in high mountain and canyon areas. Due to the active tectonic movements that these areas have experienced or are currently experiencing, geological hazards frequently occur on the mountains flanking the rivers. Therefore, evaluating the susceptibility and risk of earthquake landslides in river segments of these high mountain and canyon areas is of great importance for disaster prevention and mitigation, as well as for the safe construction and operation of hydropower stations. Currently, a major challenge in the study of landslide susceptibility and hazard is determining the thickness of potential landslide bodies. The presence of excess topography reflects the instability of the disrupted slopes, which is also a fundamental cause of landslides. This study takes the example of the Ludian earthquake in 2014, focusing on the IX and VIII intensity zones, to extract the excess topography in the study area and analyze its correlation with seismic landslides. The correlation between the critical acceleration value and the excess topography was validated using the Spearman’s rank correlation coefficient, resulting in a correlation coefficient of −0.771. This indicates a strong negative correlation between the excess topography and critical acceleration, with significant relevance. The landslide susceptibility distribution obtained by setting the potential landslide thickness based on the excess topography and proportion coefficient showed an ROC curve analysis AUC value of 0.829. This is higher than the AUC value of 0.755 for the landslide susceptibility result using a uniform potential landslide thickness of 3 m, indicating the higher model evaluation accuracy of this approach. Earthquake landslide hazard predictions for rapid post-earthquake assessments and earthquake landslide hazard zoning for pre-earthquake planning were made using actual seismic ground motion and a 2% exceedance probability in 50 years, respectively. Comparing these with the 10,559 coseismic landslides triggered by the Ludian earthquake and evaluating the seismic landslide development rate, the results were found to be consistent with reality. The improved model better reflects the control of excess topography and rock mechanics properties on the development of earthquake landslide hazards on high steep slopes. Identifying high-risk seismic landslide areas through this method and taking corresponding preventive and protective measures can help plan and construct safer hydropower and other infrastructure, thereby enhancing their disaster resistance. Full article

(This article belongs to the Topic Database, Mechanism and Risk Assessment of Slope Geologic Hazards)

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19 pages, 47532 KiB  

Open AccessArticle

Potential Controlling Factors and Landslide Susceptibility Features of the 2022 Ms 6.8 Luding Earthquake

by Siyuan Ma, Xiaoyi Shao and Chong Xu

Remote Sens. 2024, 16(15), 2861; https://doi.org/10.3390/rs16152861 - 5 Aug 2024

Cited by 4 | Viewed by 1285

Abstract

On 5 September 2022, a Ms 6.8 earthquake struck Luding County, Ganzi Tibetan Autonomous Prefecture, Sichuan Province, China. This seismic event triggered over 16,000 landslides and caused serious casualties and infrastructure damages. The aim of this study is to perform the detailed landslides [...] Read more.

On 5 September 2022, a Ms 6.8 earthquake struck Luding County, Ganzi Tibetan Autonomous Prefecture, Sichuan Province, China. This seismic event triggered over 16,000 landslides and caused serious casualties and infrastructure damages. The aim of this study is to perform the detailed landslides susceptibility mapping associated with this event based on an updated landslide inventory and logistic regression (LR) modeling. Firstly, we quantitatively assessed the importance of different controlling factors using the Jackknife and single-variable methods for modeling landslide occurrence. Subsequently, four landslide susceptibility assessment models were developed based on the LR model, and we evaluated the accuracy of the landslide susceptibility mappings using Receiver Operating Characteristic (ROC) curves and statistical measures. The results show that ground motion has the greatest influence on landslides in the entire study area, followed by elevation, while distance to rivers and topographic relief have little influence on the distribution of landslides. Compared to the NEE plate, PGA has a greater impact on landslides in the SWW plate. Moreover, the AUC value of the SWW plate significantly decreases for lithological types and aspect, indicating a more pronounced lithological control over landslides in the SWW plate. We attribute this phenomenon primarily to the occurrence of numerous landslides in Permian basalt and tuff in the SWW plate. Otherwise, the susceptibility results based on four models indicate that high-susceptibility areas predicted by different models are distributed along both sides of seismogenic faults and the Dadu Rivers. Landslide data have a significant impact on the model prediction results, and the model prediction accuracy based on the landslide data of the SWW plate is higher. Full article

(This article belongs to the Topic Database, Mechanism and Risk Assessment of Slope Geologic Hazards)

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18 pages, 15194 KiB  

Open AccessArticle

Evaluating Coseismic Landslide Susceptibility Following the 2022 Luding Earthquake: A Comparative Analysis of Six Displacement Regression Models Integrating Epicentral and Seismogenic Fault Distances within the Permanent-Displacement Framework

by Tianhao Liu, Mingdong Zang, Jianbing Peng and Chong Xu

Remote Sens. 2024, 16(14), 2675; https://doi.org/10.3390/rs16142675 - 22 Jul 2024

Viewed by 1211

Abstract

Coseismic landslides have the potential to cause catastrophic disasters. Thus, it is of crucial importance to conduct a comprehensive regional assessment of susceptibility to coseismic landslides. This study rigorously interprets 13,759 coseismic landslides triggered by the 2022 Luding earthquake within the seismic zone. [...] Read more.

Coseismic landslides have the potential to cause catastrophic disasters. Thus, it is of crucial importance to conduct a comprehensive regional assessment of susceptibility to coseismic landslides. This study rigorously interprets 13,759 coseismic landslides triggered by the 2022 Luding earthquake within the seismic zone. Employing the Newmark method, we systematically assess the susceptibility to coseismic landslides through the application of six distinct displacement regression models. The efficacy of these models is validated against the actual landslide inventory using the area under the receiver operating characteristic (ROC) curve. A hazard map of coseismic landslides is generated based on the displacement regression model with the highest degree of fit. The results show that Moxi Town, Detuo Town, the flanks of the Daduhe River, Wandonghe River, Hailuogou River, and Yanzigou River are high-susceptibility areas for coseismic landslides. This study explores factors influencing model fit, revealing that the inclusion of the epicentral distance and the distance to the seismogenic fault in displacement prediction enhances model performance. Nevertheless, in close proximity to fault zones, the distance to the seismogenic fault exerts a more significant influence on the spatial distribution density of coseismic landslides compared to the epicentral distance. Conversely, in regions situated further from fault zones, the epicentral distance has a greater impact on the spatial distribution density of coseismic landslides compared to the distance to the seismogenic fault. These findings contribute to a nuanced understanding of coseismic landslide susceptibility and offer valuable insights for future Newmark method-based coseismic landslide displacement calculations. Full article

(This article belongs to the Topic Database, Mechanism and Risk Assessment of Slope Geologic Hazards)

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15 pages, 6850 KiB  

Open AccessArticle

Detailed Landslide Traces Database of Hancheng County, China, Based on High-Resolution Satellite Images Available on the Google Earth Platform

by Junlei Zhao, Chong Xu and Xinwu Huang

Data 2024, 9(5), 63; https://doi.org/10.3390/data9050063 - 29 Apr 2024

Cited by 6 | Viewed by 1658

Abstract

Hancheng is located in the eastern part of China’s Shaanxi Province, near the west bank of the Yellow River. It is located at the junction of the active geological structure area. The rock layer is relatively fragmented, and landslide disasters are frequent. The [...] Read more.

Hancheng is located in the eastern part of China’s Shaanxi Province, near the west bank of the Yellow River. It is located at the junction of the active geological structure area. The rock layer is relatively fragmented, and landslide disasters are frequent. The occurrence of landslide disasters often causes a large number of casualties along with economic losses in the local area, seriously restricting local economic development. Although risk assessment and deformation mechanism analysis for single landslides have been performed for landslide disasters in the Hancheng area, this area lacks a landslide traces database. A complete landslide database comprises the basic data required for the study of landslide disasters and is an important requirement for subsequent landslide-related research. Therefore, this study used multi-temporal high-resolution optical images and human-computer interaction visual interpretation methods of the Google Earth platform to construct a landslide traces database in Hancheng County. The results showed that at least 6785 landslides had occurred in the study area. The total area of the landslides was about 95.38 km², accounting for 5.88% of the study area. The average landslide area was 1406.04 m², the largest landslide area was 377,841 m², and the smallest landslide area was 202.96 m². The results of this study provides an important basis for understanding the spatial distribution of landslides in Hancheng County, the evaluation of landslide susceptibility, and local disaster prevention and mitigation work. Full article

(This article belongs to the Topic Database, Mechanism and Risk Assessment of Slope Geologic Hazards)

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23 pages, 42549 KiB  

Open AccessArticle

Quick Extraction of Joint Surface Attitudes and Slope Preliminary Stability Analysis: A New Method Using Unmanned Aerial Vehicle 3D Photogrammetry and GIS Development

by Qiyu Li, Xin Yao, Renjiang Li, Zhenkai Zhou, Chuangchuang Yao and Kaiyu Ren

Remote Sens. 2024, 16(6), 1022; https://doi.org/10.3390/rs16061022 - 14 Mar 2024

Cited by 1 | Viewed by 1600

Abstract

The present study proposes a preliminary analysis method for rock mass joint acquisition, analysis, and slope stability assessment based on unmanned aerial vehicle (UAV) photogrammetry to extract the joint surface attitude in Geographic Information Systems (GIS). The method effectively solves the difficulties associated [...] Read more.

The present study proposes a preliminary analysis method for rock mass joint acquisition, analysis, and slope stability assessment based on unmanned aerial vehicle (UAV) photogrammetry to extract the joint surface attitude in Geographic Information Systems (GIS). The method effectively solves the difficulties associated with the above issues. By combining terrain-following photogrammetry (TFP) and perpendicular and slope surface photogrammetry (PSSP), the three-dimensional (3D) information can be efficiently obtained along the slope characteristics’ surface, which avoids the information loss involved in traditional single-lens aerial photography and the information redundancy of the five-eye aerial photography. Then, a semi-automatic geoprocessing tool was developed within the ArcGIS Pro 3.0 environment, using Python for the extraction of joint surfaces. Multi-point fitting was used to calculate the joint surface attitude. The corresponding attitude symbols are generated at the same time. Finally, the joint surface attitude information is used to perform stereographic projection and kinematic analysis. The former can determine the dominant joint group, and the latter can obtain the probability of four types of failure, including planar sliding, wedge sliding, flexural toppling, and direct toppling. The integrated stability evaluation method studied in this paper, which combines a 3D interpretation of UAV and GIS stereographic projection statistical analysis, has the advantages of being efficient and user-friendly, and requires minimal prior knowledge. The results can aid in the geological surveys of slopes and guide engineering practices. Full article

(This article belongs to the Topic Database, Mechanism and Risk Assessment of Slope Geologic Hazards)

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14 pages, 11098 KiB  

Open AccessArticle

Inventory of Landslides in the Northern Half of the Taihang Mountain Range, China

by Xuewei Zhang, Chong Xu, Lei Li, Liye Feng and Wentao Yang

Geosciences 2024, 14(3), 74; https://doi.org/10.3390/geosciences14030074 - 10 Mar 2024

Cited by 7 | Viewed by 1933

Abstract

The Taihang Mountains are a critical mountain range and geographical boundary in eastern China. Landslide disasters are particularly common in this region and usually cause serious casualties and property damage. However, previous landslide inventories in the region are limited and lack comprehensive landslide [...] Read more.

The Taihang Mountains are a critical mountain range and geographical boundary in eastern China. Landslide disasters are particularly common in this region and usually cause serious casualties and property damage. However, previous landslide inventories in the region are limited and lack comprehensive landslide cataloguing. To address this gap, the northern half of the Taihang Mountain Range was selected for this study. A landslide database for the area was constructed using multi-temporal high-resolution optical imagery from the Google Earth and human–computer interactive visual interpretation technology. The results indicate that at least 8349 landslides have occurred in the Taihang Mountain Range, with a total landslide area of about 151.61 km². The size of the landslides varies, averaging about 18,159.23 m², with the largest landslide covering 2.83 km² and the smallest landslide only 5.95 m². The significance of this study lies in its ability to enhance our understanding of the distribution of landslides in the northern half of the Taihang Mountains. Furthermore, it offers valuable data references and supports for landslide assessment, early warning systems, disaster management, and ecological protection efforts. Full article

(This article belongs to the Topic Database, Mechanism and Risk Assessment of Slope Geologic Hazards)

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22 pages, 2093 KiB  

Open AccessArticle

Comparison of Rating Systems for Alberta Rock Slopes, and Assessment of Applicability for Geotechnical Asset Management

by Taylor Del Gerhard Wollenberg-Barron, Renato Macciotta Pulisci, Chris Gräpel, Kristen Tappenden and Roger Skirrow

Geosciences 2023, 13(11), 348; https://doi.org/10.3390/geosciences13110348 - 14 Nov 2023

Cited by 1 | Viewed by 2007

Abstract

In 1999, Alberta Transportation and Economic Corridors (TEC) implemented the Geohazard Risk Management Program (GRMP) to identify, assess, monitor, and prioritize the mitigation of risk resulting from geohazard events at specific sites along the provincial highway network. The GRMP was developed to address [...] Read more.

In 1999, Alberta Transportation and Economic Corridors (TEC) implemented the Geohazard Risk Management Program (GRMP) to identify, assess, monitor, and prioritize the mitigation of risk resulting from geohazard events at specific sites along the provincial highway network. The GRMP was developed to address a variety of geohazard types including rockfall hazards that occur at natural and constructed (cut) highway backslopes. An evaluation of various methods for the condition assessment of rockfall geohazards, including TEC’s current GRMP risk rating system, has been completed with the intent of better understanding the suitability of each method as TEC transitions to a formalized GAM program. The GRMP risk rating values for selected rockfall geohazard sites along highway corridors in Alberta were compared to values developed from the results of five established rock mass and rock slope rating systems. The results of this study demonstrate that TEC’s current GRMP risk rating system is a viable tool for the condition assessment and performance monitoring of rockfall geohazards, which could be utilized within a formalized GAM program, further benefitting from years of recorded application in Alberta. Of the other rating systems tested, the rockfall hazard rating system (RHRS) showed a strong correlation with the GRMP risk rating while Q-Slope, the Geological Strength Index (GSI) and Rock Mass Rating (RMR) correlation were marginal but displayed a potential for use as condition assessment tools. The work presented in this paper provides the first evaluation of rock slope rating systems for rockfall hazards along corridors in Alberta, directly comparing them to the slope performance as observed by TEC in a quantitative manner. Full article

(This article belongs to the Topic Database, Mechanism and Risk Assessment of Slope Geologic Hazards)

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21 pages, 15536 KiB  

Open AccessArticle

Analysis of the Controlling Effect of Excess Topography on the Distribution of Coseismic Landslides during the Iburi Earthquake, Japan, on 6 September 2018

by Pengfei Zhang, Hengzhi Qiu, Chong Xu, Xiaoli Chen and Qing Zhou

Remote Sens. 2023, 15(20), 5035; https://doi.org/10.3390/rs15205035 - 20 Oct 2023

Cited by 2 | Viewed by 1564

Abstract

Coseismic landslides cause changes in the hillside material, and this erosion process plays an important role in the evolution of the topography. Previous studies seldom involved research on the influence of excess topography on the occurrences of coseismic landslides. The Iburi earthquake, which [...] Read more.

Coseismic landslides cause changes in the hillside material, and this erosion process plays an important role in the evolution of the topography. Previous studies seldom involved research on the influence of excess topography on the occurrences of coseismic landslides. The Iburi earthquake, which occurred in Japan on 6 September 2018 and triggered a large number of landslides, provided a research example to explore the relationship between coseismic landslides and excess topography. We used the average slope of the lithology as the threshold slope of the corresponding stratum to calculate the excess topography of the different lithological units. Based on the advanced spaceborne thermal emission and reflection radiometer (ASTER) digital elevation model (DEM) with a resolution of 30 m, a quantitative analysis was conducted on the excess topography in the study area. The results indicate that the excess topography in the study area was mainly distributed in the valleys on both sides of the river, and the thickness of the excess topography on the high and steep ridges was generally greater than that at the foot of the slope, which has a relatively flat topography or a low elevation. In the area affected by the earthquake, approximately 94.66% of the coseismic landslides (with an area of approximately 28.23 m²) developed in the excess topography area, indicating that the distribution of the excess topography had a strong controlling influence on the spatial distribution of the coseismic landslides. The Iburi earthquake mainly induced shallow landslides, but the thickness of the landslide body was much smaller than the excess topography height in the landslides-affected area. This may imply that the excess topography was not completely removed by the coseismic landslides, and the areas where the earthquake landslides occurred still have the possibility of producing landslides in the future. Full article

(This article belongs to the Topic Database, Mechanism and Risk Assessment of Slope Geologic Hazards)

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19 pages, 17130 KiB  

Open AccessArticle

Estimating the Quality of the Most Popular Machine Learning Algorithms for Landslide Susceptibility Mapping in 2018 Mw 7.5 Palu Earthquake

by Siyuan Ma, Xiaoyi Shao and Chong Xu

Remote Sens. 2023, 15(19), 4733; https://doi.org/10.3390/rs15194733 - 27 Sep 2023

Cited by 10 | Viewed by 1766

Abstract

The Mw 7.5 Palu earthquake that occurred on 28 September 2018 (UTC 10:02) on Sulawesi Island, Indonesia, triggered approximately 15,600 landslides, causing about 4000 fatalities and widespread destruction. The primary objective of this study is to perform landslide susceptibility mapping (LSM) associated with [...] Read more.

The Mw 7.5 Palu earthquake that occurred on 28 September 2018 (UTC 10:02) on Sulawesi Island, Indonesia, triggered approximately 15,600 landslides, causing about 4000 fatalities and widespread destruction. The primary objective of this study is to perform landslide susceptibility mapping (LSM) associated with this event and assess the performance of the most widely used machine learning algorithms of logistic regression (LR) and random forest (RF). Eight controlling factors were considered, including elevation, hillslope gradient, aspect, relief, distance to rivers, peak ground velocity (PGV), peak ground acceleration (PGA), and lithology. To evaluate model uncertainty, training samples were randomly selected and used to establish the models 20 times, resulting in 20 susceptibility maps for different models. The quality of the landslide susceptibility maps was evaluated using several metrics, including the mean landslide susceptibility index (LSI), modelling uncertainty, and predictive accuracy. The results demonstrate that both models effectively capture the actual distribution of landslides, with areas exhibiting high LSI predominantly concentrated on both sides of the seismogenic fault. The RF model exhibits less sensitivity to changes in training samples, whereas the LR model displays significant variation in LSI with sample changes. Overall, both models demonstrate satisfactory performance; however, the RF model exhibits superior predictive capability compared to the LR model. Full article

(This article belongs to the Topic Database, Mechanism and Risk Assessment of Slope Geologic Hazards)

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