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Applied Sciences
Special Issues
Recent Advances in Modeling, Assessment, and Mitigation of Landslide Hazards
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Recent Advances in Modeling, Assessment, and Mitigation of Landslide Hazards

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

Deadline for manuscript submissions: 10 August 2024 | Viewed by 5618

Special Issue Editors

Dr. Saeid Moussavi Tayyebi

E-Mail Website
Guest Editor
Department of Mathematics and Computers Applied to Civil and Naval Engineering, ETS Ingenieros de Caminos, Canales y Puertos, Universidad Politécnica de Madrid, Calle del Profesor Aranguren, 3, 28040 Madrid, Spain
Interests: geotechnical engineering; soil mechanics; landslides; geotechnics; natural disasters; slope stability; numerical modeling in geotechnical engineering; smoothed-particle hydrodynamics
Prof. Dr. Manuel Pastor

E-Mail Website
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 modelling

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • 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

Published Papers (6 papers)

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Research

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19 pages, 7493 KiB  
Article
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 493
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 [...] Read more.
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  
Article
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 647
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, [...] Read more.
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  
Article
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 442
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 [...] Read more.
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  
Article
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 559
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, [...] Read more.
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  
Article
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 721
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.
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, 0.5 s, and 1.0 s) and three return periods (475, 975, and 2475 years). First, joint distributions of magnitude (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  
Review
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 1059
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 [...] Read more.
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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