Landslide Zones Research Articles

Lithological Interpretation of Landslide Zone of Arjuna Road Kalongan Village East Ungaran Use Geomagnetic Method

Arjuna Road in Kalongan Village, East Ungaran, which connects Semarang Regency with Demak Regency, has been cut off due to a landslide. Lithology as an internal controlling factor of landslide occurrence is important to be investigated. Research on the disaster has been conducted to determine the magnetic field anomaly and to interpret subsurface lithology using the geomagnetic method. The research was conducted on the landslide zone at Arjuna Road and its surrounding area using the geomagnetic method with looping data acquisition. The research area includes residential areas and plantation areas of residents with a research area of 1.3 × 0.8 km2 with a total of 104 measurement points. Through the results and analysis of the research, it can be determined that the magnetic field anomalies in the research area range from 17.1 - 882.2 nT where the landslide zone dominantly has a high anomaly indicating the closer the area is to bedrock, and for residential areas and local plantations, the magnetic field anomalies vary from low to high depending on the topography and sediment thickness in the area. In addition, the subsurface lithology in the study area consists of clay soil, claystone, tuff, and volcanic breccia. The impermeable nature of the tuff and the water-low permeable clay soil causes the boundary between the clay soil and tuff layers to become slippery when water seepage occurs, resulting the landslides.

Spatial assessment employing fusion logistic regression and frequency ratio models to monitor landslide susceptibility in the upper Blue Nile basin of Ethiopia: Muger watershed

At the global level, landslides are a dreadful hazard that restricts socio-economic and ecological balances. Recent human activities in hilly areas, coupled with geological predispositions, have potentially exacerbated landslide frequency and magnitude. However, the impacts of these factors on landslide occurrences in the upper Blue Nile basin of Ethiopia remain largely unexplored. This study aims to identify landslide triggers, quantify landslide-susceptible zones, and validate the landslide models. Topographic parameters, geology, hydrology, and land use-land cover inventories were utilized to generate a landslide susceptibility map. The factors were analyzed using a combination of logistic regression (LR) and frequency ratio (FR) models. The area under the curve (AUC) under the receiver operating characteristic (ROC) was used to compare the performance of the models. The result indicates that about 185 sq. km (40.2%) of the total falls under high to very-high susceptible landslide zones, and 92 sq. km (20%) falls under moderate susceptibility. Yet, 183.1 sq. km (40.2%) of the total is classified in the low-to-no landslide hazard zones. The LR and FR model validation demonstrated an average predictive performance of 75 and 81.45%, indicating good precision. The landslide evaluation can help policymakers identify LSH zones for early warning systems and mitigation purposes.

Four years of landslide observation with anthropogenic loading as an additional trigger - Analysis of seasonal and annual variability of physical parameters

Determining the structure and evolution of landslides is crucial for geophysical hazard assessment. In this study, we employed an approach integrating the methodologies of multi-channel analysis of surface waves (MASW) and electrical conductivity to image temporal and spatial changes within a landslide in southern Poland. The area, located in the Outer Carpathians, experiences significant climate fluctuations, compounded by anthropogenic activities such as recreational skiing requiring artificial snow.Our combined seismic and electrical methods techniques reveal the landslide's susceptibility to environmental factors on both annual and seasonal scales. Additional analysis, including data clustering and remote sensing, identifies three distinct landslide zones with varying vulnerability to natural and anthropogenic influences.While focusing on a specific area, our approach has global applicability to similar mass movements. This research addresses a gap in understanding time-dependent geophysical observations of moisture-driven landslides, providing valuable insights for hazard identification and mitigation strategies.

Landslides: A Review from the Southern Western Ghats of India

ABSTRACT Landslides are the most unpredictable catastrophic events in mountainous and hilly regions. South Western Ghats one among of the regions in India that have experienced recurring landslides due to increase in pre-and post-monsoon rains. Landslides often cause damage to buildings, roads, natural vegetation, even result in the loss of human lives in South Western Ghats with indirect impact on the socioeconomic status of the country. It is crucial to depict the current trend technologies and their performance in landslide studies, offering valuable insights into mitigation strategies. This article highlights on prevalent techniques for mapping landslide zones, along with major conditional factors and assess the performance of these techniques in landslide susceptibility mapping of the South Western Ghats. Landslide studies of the South Western Ghats reveal 20% of the research work with the Landsat 8 OLI for mapping landslides, 13.3% of the studies using the higher spatial resolutions 2.5m and 10m of Cartosat DEM, the rest of the studies were based on the spatial resolution of 12.5m and 30m from ALOS PALSAR, ASTER and SRTM DEM. About 26% landslide studies have employed quantitative approaches primarily the frequency ratio of bivariate. Subsequently machine learning techniques such as random forest and support vector machine have gained prominence, accounting for 33% landslide studies. The literature depicts precipitation as a significant triggering factor and slope as the major conditional factor in the occurrence of landslides within the South Western Ghats. Prioritized densely populated areas in landslide studies have increased attention to smaller geographical areas and forest regions in the South Western Ghats to protect both inhabitants and ecosystems.

Incorporating Effects of Slope Units and Sliding Areas into Seismically Induced Landslide Risk Modeling in Tectonically Active Mountainous Areas

Traditional Newmark models estimate earthquake-induced landslide hazards by calculating permanent displacements exceeding the critical acceleration, which is determined from static factors of safety and hillslope geometries. However, these studies typically predict the potential landslide mass only for the source area, rather than the entire landslide zone, which includes both the source and sliding/depositional areas. In this study, we present a modified Newmark Runout model that incorporates sliding and depositional areas to improve the estimation of landslide chain risks. This model defines the landslide runout as the direction from the source area to the nearest river channel within the same slope unit, simulating natural landslide behavior under gravitational effects, which enables the prediction of the entire landslide zone. We applied the model to a subset of the Minjiang Catchment affected by the 1933 MW 7.3 Diexi Earthquake in China to assess long-term landslide chain risks. The results indicate that the predicted total landslide zone closely matches that of the Xinmo Landslide that occurred on 24 June 2017, despite some uncertainties in the sliding direction caused by the old landslide along the sliding path. Distance-weighted kernel density analysis was used to reduce the prediction uncertainties. The hazard levels of the buildings and roads were determined by the distance to the nearest entire landslide zone, thereby assessing the landslide risk. The landslide dam risks were estimated using the kernel density module for channels blocked by the predicted landslides, modeling intersections of the total landslide zone and the channels. High-risk landslide dam zones spatially correspond to the locations of the knickpoints primarily induced by landslide dams, validating the model’s accuracy. These analyses demonstrate the effectiveness of the presented model for Newmark-based landslide risk estimations, with implications for geohazard chain risk assessments, risk mitigation, and land use planning and management.

Improving ML-based landslide susceptibility using ensemble method for sample selection: a case study of Kangra district in Himachal Pradesh, India.

Developing effective strategies to predict areas susceptible to landslides and reducing risk is vital. This involves using ensemble methods to meet the precise prediction and addressing challenges like data limitation. Recent studies have highlighted the potential of using ensemble methods to enhance the prediction of landslide susceptibility maps (LSM). Ensemble methods present a sampling of landslides and non-landslide points from high and low susceptible areas, respectively. Extensive research has explored their application in machine learning processes, particularly in classification-related problems. This study delves into strategies of ensemble as a promising method in future landslide applications. The proposed method was tested considering Kangra district of Himachal Pradesh as study area where three datasets were prepared consisting of presence and absence points. Dataset 1 consisted of initial landslide and randomly generated non-landslide points. In dataset 2, additional landslide points obtained from the very high susceptibility of initial LSM were supplemented with initial landslide data, while the non-landslide points were generated randomly from the study area. Finally, dataset 3 was composed of the landslide points as in dataset 2, and the non-landslide points were obtained from the very low susceptible areas of initial LSM. These datasets are used with random forest (RF) and support vector machine (SVM), thereby preparing six landslide susceptibility maps. To analyze the applicability of the proposed method, we have used metrics such as AUC-ROC, precision, recall, F-score, accuracy and Mathew's correlation coefficient (MCC). The AUC for dataset 1 with SVM and RF is 0.89, which increased to 0.898 and 0.952 for datasets 2 and 3 with SVM and 0.937 and 0.954 with RF. Among all the methods, the precision and recall values were highest for dataset 3 with SVM as well as RF. Hence, based on several accuracy metrics, we conclude that when the landslides and non-landslides samples were sampled from very high and very low susceptible areas respectively, the LSM performed better than all the other methods. Sampling landslides from very high susceptible areas only (dataset 2) does not perform well thereby committing misclassification error. The study demonstrated that the landslide and non-landslide data were obtained from very high and very low susceptibility; the predictive capability of the LSM increased significantly. Thus, the results demonstrate the effectiveness of the proposed ensemble approach in providing precise delineation of landslide zones, facilitating informed decision-making for land and hazard management.

Quasi-3D Geoelectrical Imaging as A New Application for Landslide Investigations: A Tunnel Case Induced by Blasting Activity

Landslides are a significant hazard in mountainous regions, especially when influenced by construction activities such as tunnel excavation. In this paper, we aim to conduct a slope stability analysis as a result of tunnel blasts using quasi-3D subsurface models based on resistivity values. The study site is a construction area for the Jakarta-Bandung High-Speed Train tunnel, located in a mountainous region undergoing drill-and-blast excavation. This excavation method makes the area susceptible to landslides, which pose a threat to settlements in the Padalarang subdistrict, West Bandung Regency, Indonesia. Data was collected along four lines in 2D, and the dipole-dipole array was used to enhance resolution. Data modeling was carried out using ResIPy v3.2.3 software to create 2D and quasi-3D subsurface models based on resistivity values. The study findings indicate that the study area exhibits three resistivity ranges: low resistivity (0-30 Ωm), medium resistivity (31-49 Ωm), and high resistivity (>50 Ωm). Utilizing quasi-3D imaging, we were able to identify the dimensions and presence of slip surfaces, which can be categorized as shallow (1.5-5 m) and deep (5-20 m) criteria. This study successfully applied the quasi-3D geoelectrical approach in a susceptible environment to detect potential landslide zones.

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

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.

Enhancing landslide inventory mapping through Multi-SAR image analysis: a comprehensive examination of current landslide zones

Enhancing landslide inventory mapping through Multi-SAR image analysis: a comprehensive examination of current landslide zones

Parameter identification by deep learning of a material model for granular media

Classical physical modeling with associated numerical simulation (model-based), and prognostic methods based on the analysis of large amounts of data (data-driven) are the two most common methods used for the mapping of complex physical processes. In recent years, the efficient combination of these approaches has become increasingly important. Continuum mechanics in the core consists of conservation equations that-in addition to the always-necessary specification of the process conditions-can be supplemented by phenomenological material models. The latter are an idealized image of the specific material behavior that can be determined experimentally, empirically, and based on a wealth of expert knowledge. The more complex the material, the more difficult the calibration is. This situation forms the starting point for this work’s hybrid data-driven and model-based approach for mapping a complex physical process in continuum mechanics. Specifically, we use data generated from a classical physical model by the MESHFREE software (MESHFREE Team in Fraunhofer ITWM & SCAI: MESHFREE. https://www.meshfree.eu, 2023) to train a Principal Component Analysis-based neural network (PCA-NN) for the task of parameter identification of the material model parameters. The obtained results highlight the potential of deep-learning-based hybrid models for determining parameters, which are the key to characterizing materials occurring naturally such as sand, soil, mud, or snow. The motivation for our research is the simulation of the interaction of vehicles with sand. However, the applicability of the presented methodology is not limited to this industrial use case. In geosciences, when predicting the runout zones of landslides or avalanches and evaluating corresponding protective measures, the parameterization of the respective material model is essential.

Regional earthquake-induced landslide assessments for use in seismic risk analyses of distributed gas infrastructure systems

Earthquake-induced landslides are associated with significant risks to human lives and infrastructure. Spatially distributed infrastructure systems, such as pipelines, power lines, and transportation networks, are at particular risk to seismic landslides due to their large spatial extent. To conduct a comprehensive seismic landslide risk assessment for these systems, there is the need to evaluate the seismic landslide characteristics (i.e., location, size, displacement, direction) on a broad, regional scale. This paper presents a framework for seismic landslide analysis that provides this information for subsequent risk assessments. The approach computes seismic landslide displacements using a sliding block approach while accounting for the uncertainties in the input variables and displacement models using a logic tree. The computed displacements are then aggregated based on geomorphic landforms to define landslide zones. For each landslide zone, the statistical distributions of landslide features, such as landslide size, displacement level, and direction of movement, are defined. These attributes are presented in a format that can be integrated with fragility models for distributed infrastructure systems to quantify risk on a regional scale. The application of the approach is demonstrated through assessments for gas pipeline networks across the state of California in the United States.

Monitoring Surface Deformations in a Fossil Landslide Zone and Identifying Potential Failure Mechanisms: A Case Study of Gümüşhane State Hospital.

The escalating occurrence of landslides has drawn increasing attention from the scientific community, primarily driven by a combination of natural phenomena such as unpredictable seismic events, intensified precipitation, and rapid snowmelt attributable to climate fluctuations, compounded by inadequacies in engineering practices during site selection. Within the scope of this investigation, contemporary geodetic techniques using the GNSS were employed to monitor structural and surface deformations in and around a hospital edifice situated within an ancient fossil landslide region. Additionally, inclinometer measurements facilitated the determination of slip circle parameters. A subsequent analysis integrated these datasets to scrutinize both the hospital structure and its surrounding slopes. In addition to the finite element method, four different limit equilibrium methods (Bishop, GLE-Morgenstern-Price, Spencer, and Janbu) were used in the evaluation of stability. Since the safety number determined in all analyses was <1, it was determined that the slope containing the hospital building was unstable. The movement has occurred again due to the additional load created by the hospital building built on the currently stable slope, the effect of surface and groundwater, and the improperly designed road route. As a result of geodetic monitoring, it was determined that the sliding speed on the surface was in the N-E direction and was approximately 3 cm, and this situation almost coincided with inclinometer measurements.

Identification of Relict Landslide Parts Based on Morphometric Data to Determine Potential Hazard Zones Combined with Surface Morphodynamics

The destruction of houses and facilities by landslide causes deaths. In this context, the level of destruction and subjective description of the characteristics can be examined through landslide parts determination. Therefore, this study aims to determine potential landslide hazard zone and houses potentially affected. Global Navigation Satellite System (GNSS) and unmanned aerial vehicle (UAV) are morphometric surveys combined with surface morphodynamics to show potential hazard zones of landslide parts. Meanwhile, Data Elevation Model (DEM) is used to delineate relict landslide and the concept is verified by field observation and orthophoto. Morphometric measurements are collected at each slope gradient by GNSS and surface morphodynamics are investigated on the entire relict landslide area by direct observation and orthophoto data. The combination of morphometric and morphodynamic data describes hazard zone of relict landslide. In addition, the integration of orthophoto and landslide hazard zone data is used to determine potentially affected houses. This study was conducted on landslides 1 and 2 with zone classifications of very high, high, low and very low. The results show that there are different conditions and the most hazardous parts of landslides 1 and 2 are the foot and body, respectively. A total of 75 and 50 houses were potentially affected by landslides 1 and 2, respectively. Identification of hazard zones based on landslide parts determines the boundaries of the area affected. The addition of surface activity processes determines the level of hazard in each of parts, while the combination of morphometric and morphodynamic data shows landslide zone.

Numerical analysis of buried pipelines under seismic slope instability

Damage to buried pipes under seismic landslide actions has been reported in many post-earthquake reconnaissance. The landslide-pipe problem in the technical literature has been often investigated using simplified analytical methods. However, the analytical methods ignore the real mechanism of pipe response under natural dynamic slope instability. The dynamic slope instability is significantly influenced by its lateral boundary interface (LBI) characteristics. In this study, slope-pipe interaction (SPI) under seismic loading, focusing on the effect of LBI properties, is evaluated by continuum numerical simulation using the SANISAND constitutive model in FLAC3D. The results show that the geometry of the failure mass varies from 2D to 3D by increasing the stiffness at the slope boundaries (from smooth to hard) and the maximum pipe deformation decreases by around 40%. Moreover, the response components of maximum axial stress, bending moment, and shear stress of the pipe occur at the end sections of the buried pipe and near the boundaries of the landslide zone. However, the maximum pipe deflection occurs in the middle section of the pipe. The results of shear force-shear displacement curves demonstrate that the soil-pipe interaction stiffness is variable along the pipe length and can be estimated by a hyperbolic equation.

Investigating the reactivation of historical landslides during the 2022 Luding MS6.8 earthquake

On September 5, 2022, a strong earthquake with a magnitude of MS6.8 struck Luding County in Sichuan Province, China, triggering thousands of landslides along the Dadu River in the northwest-southeast (NW-SE) direction. We investigated the reactivation characteristics of historical landslides within the epicentral area of the Luding earthquake to identify the initiation mechanism of earthquake-induced landslides. Records of the two newly triggered and historical landslides were analyzed using manual and threshold methods; the spatial distribution of landslides was assessed in relation to topographical and geological factors using remote sensing images. This study sheds light on the spatial distribution patterns of landslides, especially those that occur above historical landslide areas. Our results revealed a similarity in the spatial distribution trends between historical landslides and new ones induced by earthquakes. These landslides tend to be concentrated within a range of 0.2 km from the river and 2 km from the fault. Notably, both rivers and faults predominantly influenced the reactivation of historical landslides. Remarkably, the reactivated landslides are characterized by their small to medium size and are predominantly situated in historical landslide zones. The number of reactivated landslides surpassed that of previously documented historical landslides within the study area. We provide insights into the critical factors responsible for historical landslides during the 2022 Luding earthquake, thereby enhancing our understanding of the potential implications for future co-seismic hazard assessments and mitigation strategies.

Landslide susceptibility mapping using ensemble machine learning methods: a case study in Lombardy, Northern Italy

ABSTRACT This study compares the performance of ensemble machine learning methods stacking, blending, and soft voting for Landslide susceptibility mapping (LSM) in a highly affected Northern Italy region, Lombardy. We first created a spatial database based on open data ensuring the accessibility to relevant information for landslide-influencing factors, historical landslide records, and areas with a very low probability of landslide occurrence called ‘No Landslide Zone’, an innovative concept introduced in this study. Then, open-source software was employed for developing five Machine Learning classifiers (Bagging, Random Forests, AdaBoost, Gradient Tree Boosting, and Neural Networks) which were tested at a basin scale by implementing different combinations of training and testing schemes using three use cases. The three classifiers with the highest generalization performance (Random Forests, AdaBoost, and Neural Networks) were selected and combined by ensemble methods. The soft voting showed the highest performance among them. The best model to generate the LSM for the Lombardy region was a Neural Network model trained using data from three basins, achieving an accuracy of 0.93 in Lombardy. The LSM indicates that 37% of Lombardy is in the highest landslide susceptibility categories. Our findings highlight the importance of openness in advancing LSM not only by enhancing the reproducibility and transparency of our methodology but also by promoting knowledge-sharing within the scientific community.

Effect of Shear Rate on the Residual Strength of Clayey Soils

The residual strength of soils in the shear zones at different shear rates can correspond to the movement of landslides. However, the rate-dependency of residual strength has not been quantified. A laboratory study on the residual strength of clay soils at shear velocities is present in this paper. The samples were extracted from the shear zone of a deep-seated landslide experiencing reactivation movement. The residual strength of samples at step-up shear rates from 0.5mm/min to 10000mm/min was obtained. Test results indicate that the samples at increasing shear rate tend to exhibit positive rate effect. At 10000mm/min the residual strength shows a gain of 2.21~2.68 times above the slow residual value. A model for predicting the residual strength correlating the shear rate is introduced. The different features in the slow and fast residual strength are attributed to soil fabric modifications within the shear zones. This is of practical significance to the stability and kinematic analysis of landslides.

SPATIAL VARIABILITY OF SOIL PHYSICAL PROPERTIES IN SOUTHERN MANKAYAN, BENGUET, PHILIPPINES

Abstract. Hydrothermal alteration in the Mankayan Mineral District in Benguet, Philippines, has had a significant impact on the soil characteristics. Thus, soil characterisation in landslide zones offer useful insights on the nature of slope failure and provide baseline data that may be used with susceptibility mapping and hazard zonations. The study elucidated the physical characteristics of the soils in the southern portion of Mankayan and mapped out the spatial distribution in the area. High sand and low clay content cause low slope stability due to the low water retention in soil, low plasticity, and low shear strength of materials while the resulting porosity led to poor drainage. Results show that the soil's physical properties, whereas most rock units have been affected by alteration, have caused rock deterioration that eventually led to slope failures. The presence of various parent material in the study site influenced the distribution of soil physical properties, specifically the influence of dacite on the Atterberg limits in the central area of southern Mankayan.

Identification of Landslide Prone Areas with Schlumberger Configuration Geoelectric Method, Kalongan Village, East Ungaran in 2023

Landslides are one of the natural disasters that occur in Semarang Regency. The landslide event in Kalongan Village, East Ungaran Subdistrict took place in 2022 to 2023. The dimension of the landslide is large and has the potential to cause damage to infrastructure and settlements, mitigation measures are needed. One of the mitigation measures is to identify the lithology of the landslide area and its surroundings. This study aims to determine the lithology of the landslide area and its surroundings using the Schlumberger configuration geoelectric method. Geoelectric acquisition using a set of naniura geoelectric, with a line variation of 120 to 300 meters. The number of measurement lines is 6 VES located in the landslide zone in the Kaligetas Formation. The results showed that the avalanche zone is composed of Tufan Sandstone with a resistivity of 0.17 Ωm to 334.9 Ωm, Tuff with a resistivity of 10.03 to 26.96 Ωm, and Lava Flow with a resistivity of 1161.91 Ωm to 3040.05 Ωm. Avalanche materials were identified to be associated with lithologies such as tufan sandstone and tuff with a thickness between 5 to 50 m.

Integrated Geotechnical and Electrical Resistivity Tomography to Map the Lithological Variability Involved and Breaking Surface Evolution in Landslide Context: A Case Study of the Targa Ouzemour (Béjaia)

The specific lithology of the southern part of Bejaia city represents a major limitation to urban settlement and expansion. This is partly due to landslides that tend to affect this region. To date, one of these landslides in this region has occurred in the Targa Ouzemour area, where the damage extended approximately six hectares. The main purpose of this study is to identify the failure surfaces characterizing the internal structure of this landslide as well as the significant influence of groundwater on slope instability, which manifests as surface cracking and subsidence. We have combined several geotechnical and geophysical methods, including field observations. The exploitation of the collected geotechnical data from the six (06) boreholes drilled in the landslide zone has allowed for knowledge to be gained on the lithological components, as well as the characterizations of physical and mechanical properties on a range of different types of affected rocks, whereas electrical resistivity tomography (ERT) data allowed an in-depth examination, leading us to reconstruct the landslide geometry and particularly to evaluate the hydrological characteristics of the studied site. Moreover, the resistivity contrast patterns provided more clarity to discern between the various lithological formations that are still stable or actively moving within this landslide. All these findings have contributed to the construction of a characteristic geomodel that highlights the failure surfaces over which displacement is still experienced. Finally, with the evidence of rainfall effects on the deformation and stability of the slope, specific landslide remedial measures were accordingly suggested.