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Recent Advances in Modeling, Assessment, and Mitigation of Landslide Hazards

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

Deadline for manuscript submissions: closed (10 August 2024) | Viewed by 10894

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Dr. Saeid Moussavi Tayyebi

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Guest Editor

Department of Applied Mathematics, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: numerical modeling in geotechnical engineering; landslides; smooth particle hydrodynamics; computational methods
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Prof. Dr. Manuel Pastor

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Guest Editor

Department of Applied Mathematics, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: applied and computational mathematics; fluid mechanics; landslides; geotechnical engineering; finite element method; numerical modeling; soil mechanics; geology; slope stability; constitutive modeling
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Special Issue Information

Dear Colleagues,

In this Special Issue, we embark on a comprehensive exploration of the dynamic field of landslide research. Landslides, natural geohazards with profound implications for both human settlements and the environment, continue to demand our attention in an ever-changing world. Our understanding of these complex phenomena has evolved considerably over time, driven by technological innovations, enhanced modeling techniques, and an increasing recognition of the imperative for effective mitigation strategies.

Landslides are emblematic of the intricate interplay between geological, climatic, and anthropogenic factors, presenting a formidable challenge to researchers, engineers, and policymakers alike. As we confront the realities of a changing climate and ongoing human interventions in our landscapes, the need to grasp landslide mechanisms, employ susceptibility mapping, and execute comprehensive risk assessments has never been more critical. This Special Issue aims to illuminate innovative solutions, novel methodologies, and the power of interdisciplinary collaboration as we strive to address the enduring threat of landslides.

We envision this collection of articles not only as a valuable resource for researchers, practitioners, and policymakers, but also as a catalyst for fostering collaboration and innovation in the realm of landslide hazard management. By advancing our knowledge and sharing best practices, we collectively work towards minimizing the devastating consequences of landslides, ultimately forging more resilient communities in the face of this persistent geological threat.

Dr. Saeid Moussavi Tayyebi
Prof. Dr. Manuel Pastor
Guest Editors

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Keywords

numerical methods and its applications
reliability and risk analysis
continuous and discontinuous models
GIS, remote sensing, and machine learning
landslide susceptibility modeling and mapping
monitoring techniques
early warning techniques and disaster management systems
landslide mitigation techniques

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Published Papers (9 papers)

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Research

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21 pages, 5149 KiB

Open AccessArticle

Physical Vulnerability and Landslide Risk Assessment in Tegucigalpa City, Honduras

by Ginés Suárez and María José Domínguez-Cuesta

Appl. Sci. 2024, 14(19), 9114; https://doi.org/10.3390/app14199114 - 9 Oct 2024

Viewed by 647

Abstract

Quantitative disaster risk studies for slow-moving rotational and translational landslides in small regions (e.g., cities and watersheds) are very scarce. The limitations of risk modeling associated with these hazards include (i) the lack of data for physical modeling, (ii) methodological restrictions on estimating landslide intensity with statistical models and determining the temporal probability of landslides, and (iii) the absence of characterizations of the physical vulnerability of exposed assets. The present study combines and updates different methodologies to overcome these limitations for quantitative landslide disaster risk estimation, creating a novel methodological approach that was applied in a pilot study in Tegucigalpa city, Honduras. Tegucigalpa, the capital city of Honduras, has the highest number of recorded landslides in the country. In a previous study, landslides were found to be mainly concentrated in areas with colluvium and residual soils. As an input for the disaster risk assessment, this study generated landslide risk vulnerability functions based on empirical data. The application of the proposed methodology allowed us to estimate the average annual loss (AAL) caused by landslides in the study area—a key disaster risk metric that is lacking in other landslide disaster risk studies—enabling comparisons with disaster risk estimates associated with other hazards. In particular, the AAL value obtained for the study region was USD 7.26 million. Full article

(This article belongs to the Special Issue Recent Advances in Modeling, Assessment, and Mitigation of Landslide Hazards)

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19 pages, 12548 KiB

Open AccessArticle

Comparison of Different Numerical Methods in Modeling of Debris Flows—Case Study in Selanac (Serbia)

by Jelka Krušić, Manuel Pastor, Saeid M. Tayyebi, Dragana Đurić, Tina Đurić, Mileva Samardžić-Petrović, Miloš Marjanović and Biljana Abolmasov

Appl. Sci. 2024, 14(19), 9059; https://doi.org/10.3390/app14199059 - 8 Oct 2024

Viewed by 965

Abstract

Flow-type landslides are not typical in this region of the Balkans. However, after the Tamara cyclone event in 2014, numerous such occurrences have been observed in Serbia. This paper presents the initial results of a detailed investigation into debris flows in Serbia, comparing findings from two programs: RAMMS DBF and Geoflow SPH. Located in Western Serbia, the Selanac debris flow is a complex event characterized by significant depths in the initial block and entrainment zone. Previous field investigations utilized ERT surveys, supplemented by laboratory tests, to characterize material behavior. Approximately 450,000 m³ of material began to flow following an extreme precipitation period, ultimately traveling 1.2 km to the deposition zone. For validation purposes, ERT profiles from both the deposition zone and the source area were utilized, with particular attention given to areas where entrainment was substantial, as this had a significant impact on the final models. The first objective of this research is to conduct a detailed investigation of debris flow using field investigations: geophysical (ERT) and aerial photogrammetry. The second objective is to evaluate the capacity of two debris flow propagation models to simulate the reality of these phenomena. The GeoFlow-SPH code overestimated the maximum propagation thickness in comparison to the RAMMS model. The numerical results regarding final depths closely align, especially when considering the estimated average depth in the deposition zone. The results confirm the necessity of using multiple simulation codes to more accurately predict specific events. Full article

(This article belongs to the Special Issue Recent Advances in Modeling, Assessment, and Mitigation of Landslide Hazards)

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15 pages, 21944 KiB

Open AccessArticle

Comparative Study of Rapid Assessment Methods for Earthquake-Triggered Landslides Based on the Newmark Model—A Case Study of the 2022 Luding Ms6.8 Earthquake

by Huanyu Li, Dongping Li, Jingfei Yin, Haiqing Sun, Min Li and Chenbing Dai

Appl. Sci. 2024, 14(17), 7500; https://doi.org/10.3390/app14177500 - 25 Aug 2024

Viewed by 888

Abstract

Earthquake-triggered landslides represent a significant seismic-related disaster, posing threats to both the lives and property of individuals in affected areas. Furthermore, they can result in road and river blockages, as well as other secondary disasters, significantly impacting post-earthquake rescue efforts. Efficient, accurate, and rapid assessment of high-risk landslide zones carries important implications for decision making in disaster response and for mitigating potential secondary disasters. The high-intensity zones VII to IX of the Luding Ms6.8 earthquake on 5 September, 2022, were used as a case study here. Based on the simple Newmark model, the difference method and the cumulative displacement method were employed to assess earthquake-triggered landslides. The assessment results from both methods demonstrated that the areas posing an extremely high risk of earthquake-triggered landslides were predominantly situated on the western side of the Xianshuihe Fault. Verification using actual landslide data showed that both methods had high predictive accuracy, with the difference method slightly outperforming the cumulative displacement method. Moreover, this study recommends determining threshold values for each landslide risk interval having physical meanings using previous data on strong earthquakes when utilizing the difference method to assess the risk of earthquake-triggered landslides. Full article

(This article belongs to the Special Issue Recent Advances in Modeling, Assessment, and Mitigation of Landslide Hazards)

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19 pages, 7493 KiB

Open AccessArticle

Construction and Optimization of Landslide Susceptibility Assessment Model Based on Machine Learning

by Xiaodong Wang, Xiaoyi Ma, Dianheng Guo, Guangxiang Yuan and Zhiquan Huang

Appl. Sci. 2024, 14(14), 6040; https://doi.org/10.3390/app14146040 - 10 Jul 2024

Viewed by 957

Abstract

The appropriate selection of machine learning samples forms the foundation for utilizing machine learning models. However, in landslide susceptibility evaluation, discrepancies arise when non-landslide samples are positioned within areas prone to landslides or demonstrate spatial biases, leading to differences in model predictions. To address the impact of non-landslide sample selection on landslide susceptibility predictions, this study uses the western region of Henan Province as a case study. Utilizing historical data, remote sensing interpretation, and field surveys, a sample dataset comprising 834 landslide points is obtained. Ten environmental factors, including elevation, slope, aspect, profile curvature, land cover, lithology, topographic wetness index, distance from river, distance from faults, and distance from road, are chosen to establish an evaluation index system. Negative sample sampling areas are delineated based on the susceptibility assessment outcomes derived from the information value model. Two sampling strategies, whole-region random sampling (I) and partition-based random sampling (II), are employed. Random Forest (RF) and Back Propagation Neural Network (BPNN) models are used to forecast and delineate landslide susceptibility in the western region of Henan Province, with prediction accuracy evaluated. The model prediction accuracy is ranked as follows: II-BPNN (AUC = 0.9522) > II-RF (AUC = 0.9464) > I-RF (AUC = 0.8247) > I-BPNN (AUC = 0.8068). Under the Receiver Operating Characteristic (AUC) curve and accuracy, the II-RF and II-BPNN models exhibit increases in the region by 12.17% and 15.61%, respectively, compared to the I-RF and I-BPNN models. Moreover, the II-BPNN model shows improvements over the I-BPNN model with increases in AUC and accuracy by 14.54% and 16.52%, respectively. This indicates enhancements in model performance and predictive capability. In terms of recall and specificity, the II-RF and II-BPNN models demonstrate increases in recall by 15.09% and 17.47%, respectively, and in specificity by 15.80% and 14.99%, respectively. These findings suggest that the optimized models have better predictive capabilities for identifying landslide and non-landslide areas, effectively reducing the uncertainty introduced by point data in landslide risk prediction. Full article

(This article belongs to the Special Issue Recent Advances in Modeling, Assessment, and Mitigation of Landslide Hazards)

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13 pages, 7462 KiB

Open AccessArticle

Assessment of Landslide Susceptibility in the Moxi Tableland of China by Using a Combination of Deep-Learning and Factor-Refinement Methods

by Zonghan He, Wenjun Zhang, Jialun Cai, Jing Fan, Haoming Xu, Hui Feng, Xinlong Luo and Zhouhang Wu

Appl. Sci. 2024, 14(12), 5042; https://doi.org/10.3390/app14125042 - 10 Jun 2024

Viewed by 1024

Abstract

Precisely assessing the vulnerability of landslides is essential for effective risk assessment. The findings from such assessments will undoubtedly be in high demand, providing a solid scientific foundation for a range of critical initiatives aimed at disaster prevention and control. In the research, authors set the ancient core district of Sichuan Moxi Ancient Town as the research object; they conduct and give the final result of the geological survey. Fault influences are commonly utilized as key markers for delineating strata in the field of stratigraphy, and the slope distance, slope angle, slope aspect, elevation, terrain undulation, plane curvature, profile curvature, mean curvature, relative elevation, land use type, surface roughness, water influence, distance of the catchment, cumulative water volume, and the Normalized Vegetation Index (NDVI) are used along roads to calculate annual rainfall. With the purpose of the establishment of the evaluation system, there are 17 factors selected in total. Through the landslide-susceptibility assessment by the coupled models of DNN-I-SVM and DNN-I-LR nine factors had been selected; it was found that the Area Under the Curve (AUC) value of the Receiver Operating Characteristic Curve (ROC) was high, and the accuracy of the model is relatively high. The coupler, DNN-I-LR, gives 0.875 of an evaluation accuracy of AUC, higher than DNN-I-SVM, which yielded 0.860. It is necessary to note that, in this region, compared to the DNN-I-SVM model, the DNN-I-LR coupling model has better fitting and prediction abilities. Full article

(This article belongs to the Special Issue Recent Advances in Modeling, Assessment, and Mitigation of Landslide Hazards)

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17 pages, 2515 KiB

Open AccessArticle

Influence of Rock Slide Geometry on Stability Behavior during Reservoir Impounding

by Christian Zangerl, Heidrun Lechner and Alfred Strauss

Appl. Sci. 2024, 14(11), 4631; https://doi.org/10.3390/app14114631 - 28 May 2024

Viewed by 689

Abstract

Assessing the stability behavior of deep-seated rock slides in the surroundings of large dam reservoirs requires an understanding of the geometry, the kinematics, the groundwater situation, and the rock mass and shear zone properties. This study focuses on the influence of rock slide geometry on stability evolution during initial reservoir impounding. Therefore, nine different rock slide models, mainly taken from published case studies with a well-explored geometry, were analyzed. Based on the assumption that the rock slides are close to limit equilibrium in a no-reservoir scenario, reservoir impounding causes a reduction in the factor of safety (FoS). The results show a large impact of the water level for rotational slides where the majority of the rock mass is located at the lower part of the slope. This results in a maximum reduction in the FoS of up to 12%. In contrast to this, translational rock slides are less affected by reservoir impounding. The stability analysis shows that the change in FoS is strongly controlled by the kinematics of the rock slide and the geometry near the foot of the slope. Consequently, a comprehensive in situ investigation of the geometry and kinematics is necessary in order to reliably assess the influence of initial reservoir impounding. Full article

(This article belongs to the Special Issue Recent Advances in Modeling, Assessment, and Mitigation of Landslide Hazards)

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32 pages, 18574 KiB

Open AccessArticle

Analysis of the Occurrent Models of Potential Debris-Flow Sources in the Watershed of Ching-Shuei River

by Ji-Yuan Lin, Jen-Chih Chao and Lung-Kun Yang

Appl. Sci. 2024, 14(9), 3802; https://doi.org/10.3390/app14093802 - 29 Apr 2024

Viewed by 746

Abstract

The areas around the Ching-Shuei River saw numerous landslides (2004–2017) after the Jiji earthquake, profoundly harming the watershed’s geological environment. The 33 catchment areas in the Ching-Shuei River watershed and five typhoon and rainstorm events, with a total of 165 occurrences and non-occurrences, were analyzed, and the training and validation were categorized into 70% training and 30% validation. A landslide disaster is deemed, for the purposes of this research, to have taken place if SPOT satellite images taken before and after an incident show a Normalized Difference Vegetation Index difference larger than 0.25, a slope of less than 30 degrees, and a number of connected grids greater than 10. The analysis was carried out using the instability index method analysis with Rogers regression analysis and artificial neural network. The accuracy rates of neural network, logit regression, and instability index analyses were, respectively, 93.3%, 80.6%, and 70.9%. The neural network’s area under the curve was 0.933, indicating excellent discrimination ability; that of the logit regression analysis was 0.794, which is considered good; and that of the instability index analysis was 0.635, or fair. This suggests that any of the three models are suitable for the danger assessment of large post-earthquake debris flows. The results of this study also provide a reference and evidence for specific sites’ potential susceptibility to debris flows. Full article

(This article belongs to the Special Issue Recent Advances in Modeling, Assessment, and Mitigation of Landslide Hazards)

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17 pages, 14357 KiB

Open AccessArticle

Earthquake-Induced Landslides in Italy: Evaluation of the Triggering Potential Based on Seismic Hazard

by Sina Azhideh, Simone Barani, Gabriele Ferretti and Davide Scafidi

Appl. Sci. 2024, 14(8), 3435; https://doi.org/10.3390/app14083435 - 18 Apr 2024

Viewed by 1084

Abstract

In this study, we defined screening maps for Italy that classify sites based on their potential for triggering landslides. To this end, we analyzed seismic hazard maps and hazard disaggregation results on a national scale considering four spectral periods (0.01 s, 0.2 s, [...] Read more.

M

) and distance (

R

) from hazard disaggregation were analyzed by means of an innovative approach based on image processing techniques to find all modal scenarios contributing to the hazard. In order to obtain the

M

R

scenarios controlling the triggering of earthquake-induced landslides at any computation node, mean and modal

M

R

pairs were compared to empirical curves defining the

M

R

bounds associated with landslide triggering. Three types of landslides were considered (i.e., disrupted slides and falls, coherent slides, and lateral spreads and flows). As a result, screening maps for all of Italy showing the potential for triggering landslides based on the level of seismic hazard were obtained. The maps and the related data are freely accessible. Full article

(This article belongs to the Special Issue Recent Advances in Modeling, Assessment, and Mitigation of Landslide Hazards)

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Review

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21 pages, 3761 KiB

Open AccessReview

Factors Affecting the Stability of Loess Landslides: A Review

by Liucheng Wei, Zhaofa Zeng and Jiahe Yan

Appl. Sci. 2024, 14(7), 2735; https://doi.org/10.3390/app14072735 - 25 Mar 2024

Cited by 1 | Viewed by 1582

Abstract

The stability of loess landslides affects the production and livelihood of the people in its vicinity. The stability of loess landslides is influenced by various factors, including internal structure, collapsibility, water content, and shear strength. The landslide stability of loesses can be analyzed by several geophysical methods, such as seismic refraction tomography (SRT), electrical resistivity tomography (ERT), micro-seismic technology, and ground penetrating radar (GPR). Geotechnical tests (compression and shear tests) and remote sensing techniques (Global Navigation Satellite System (GNSS), Interferometric Synthetic Aperture Radar (InSAR) and airborne 3D laser technology) are used for studying the landslide stability of loesses as well. Some of the methods above can measure parameters (e.g., fractures, water content, shear strength, creep) which influence the stability of loess landslides, while other methods qualitatively indicate the influencing factors. Integrating parameters measured by different methods, minimizing disturbances to landslides, and assessing landslide stability are important steps in studying landslide hazards. This paper comprehensively introduces the methods used in recent studies on the landslide stability of loesses and summarizes the factors which affect the landslide stability. Furthermore, the relationships between different parameters and methods are examined. This paper enhances comprehension of the underlying mechanisms of the stability of loess landslides to diminish disastrous consequences. Full article

(This article belongs to the Special Issue Recent Advances in Modeling, Assessment, and Mitigation of Landslide Hazards)

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