Landslide Studies Research Articles

Elevation and Slope as Key Determinants of Landslide-Prone Areas in Murang’a County, Kenya

Landslide studies in Kenya have received less interest as opposed to other regions of the world. Murang’a County in Central Kenya presents a unique case of geographical interest not only due to its positioning within the alpine Aberdare Ranges but also because it has experienced serious, deadly and repeated landslide disasters. This study seeks to ascertain whether elevation and slope are key determinants of landslide in the county. Primary data collection instruments were Household (HH) questionnaires where a sample size of 393 HH was sampled at a confidence level of 95%. Secondary data for elevation and slope were derived from satellite imagery. A significant 95.5% of the respondents said that slope was a major landslide causal/trigger factor. A significant number of people (r=0.806) who had migrated to their current locations described steepness as a major causal/trigger factor. Elevation factor was mentioned by 90.2% of the respondents as a key factor contributing to the occurrence of landslides. The study concludes that elevation and slope are key and significant landslide causal/trigger factors in Murang’a County. The study recommends that people living in areas delineated as ‘high-risk zones’ should be advised to relocate to safer grounds to avert huge losses from potential landslide disasters

Landslide Studies in the Context of Disaster Management in Bangladesh—A Systematic Literature Review

Landslides and their resulting impacts on property and human life have become an ongoing challenge in the hilly regions of Bangladesh. This study aims to systematically review diverse landslide studies in Bangladesh, particularly focusing on landslide disaster management (LDM) from 2008 to 2023, encompassing the pre-disaster, syn-disaster, and post-disaster phases. Several key attributes of landslide studies were considered, including general trends, data types, study scales, contributing factors, methodologies, results, and validation approaches, to investigate challenges and subsequently identify research gaps. This study evaluated 51 research articles on LDM using a systematic literature review (SLR) technique that adhered to the Preferred Reporting Item for Systematic Reviews and Meta-Analyses (PRISMA) framework. Our finding revealed that articles on LDM were dominated by the pre-disaster (76%) and the syn-disaster phases (12%), with the post-disaster phase (12%) receiving equal attention. The SLR revealed a growing number of studies since 2020 that used data-driven methods and secondary spatial data, often focused on medium-scale analyses (district level) that, however, often lacked field-based validation. From the factors examined in various landslide studies, topographical and hydrological factors were found to be the most significant attributes in assessment. This study identified key challenges, such as insufficient landslide inventories including poor site accessibility and a lack of high-resolution geological, soil, and rainfall data. It also highlighted critical research gaps, including the need for advanced technologies in susceptibility mapping for national hazard atlas, the investigation of underexplored causative factors, effective early warning systems, detailed post-event characterization, health impact assessment, risk-sensitive land use planning, and interactive web portals for landslide prone areas. This study would thus aid researchers in understanding the depth of existing knowledge and provide insights into how landslides fit into broader disaster management frameworks, facilitating interdisciplinary approaches.

Slope stability analysis of unsaturated colluvial slopes based on case studies of rainfall-induced landslides

Landslides in colluvial soils under rainfall have been identified as a significant problem due to their loose, heterogeneous nature and low shear strength. Evaluation of the stability of colluvial slopes under rainfall conditions is challenging. This study investigated two landslide failure case studies of colluvial soils to understand the failure patterns using finite element (FE) and limit equilibrium (LE) slope stability analysis methods under unsaturated conditions. Transient seepage conditions due to rainfall infiltration and failure were analysed using hydromechanical models. Here, a FE fully coupled hydromechanical model and a sequential coupling of a FE hydrological and LE mechanical model were used to evaluate the failure of variably saturated slopes. Results from the case studies revealed that the failure occurred due to the rise in the groundwater table in both cases. It was evident that there can be significant disparities in the pore water pressure profiles with the fully coupled and sequentially coupled analysis. The dynamic capability of the two models can also affect the interplay between the hydrological and mechanical aspects. When the thickness of the colluvium layer is large, the failure could potentially occur as a deep-seated failure along the boundary of overburden and the bedrock surface due to the large driving force. However, when the thickness is small, failure can occur along the colluvium-weathered rock surface. The outcomes from the study will contribute to mitigate the uncertainty of failure prediction of landslides in colluvial soils.

Particle size characteristics of sliding-zone soil and its role in landslide occurrence: a case study of the Lanniqing landslide in Southwest China

In landslide studies, particle size is a key quantitative indicator, reflecting the formation and development of the sliding zone. It plays a crucial role in understanding the mechanisms and evolutionary processes that lead to landslide occurrences. Precise measurement of particle size is crucial. This study centered on soil samples from the Lanniqing landslide in Southwest China. To begin, seven distinct methods were used to preprocess the soil samples. Next, the particle size frequency distribution was measured using the Mastersizer 2000 laser particle size analyzer. Key parameters, including median particle size, mean particle size, sorting coefficient, skewness, and kurtosis, were then compared and analyzed to determine the most appropriate preprocessing method for evaluating the characteristics of the soil samples. The mechanism of landslide occurrence was subsequently analyzed by examining the particle size characteristics, mechanical properties, and mineral composition of the soil samples. The results suggested that method C provides the most reliable analysis of particle size characteristics in soil samples. The observed coarsening of coarse particles, along with a significant increase in clay content within the sliding zone, indicates that the sliding surface has undergone multiple shear and compression events. The interplay of the upper traffic load and slope cutting at the front edge set the stage for the Lanniqing landslide, prompting the initial development of potential sliding surfaces. Rainfall acts as a catalyst for slope instability. The high clay content, combined with the formation of a low-permeability layer rich in clay minerals on the sliding surface, leads to excessive pore water pressure and mineral lubrication. These factors inherently trigger and accelerate the occurrence of the landslide.

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.

Effects of Wooden Embers Cover on thermo-hydrological response of silty volcanic cover and implications to post-wildfire slope stability

Wildfires striking vegetated hillslopes appear to increase the hazard towards rainfall-induced landslides. One mechanism little investigated in the literature consists in the formation of Wooden Embers Cover (WEC) following the wildfire. This layer has very peculiar thermohydraulic properties and may affect the interaction between the atmosphere and the subsoil. The paper presents an experiment conducted in an outdoor lysimeter filled with pyroclastic silt (SILT) up to 75 cm covered with 5 cm of WEC. Water storage in the SILT layer, soil water content, suction, and temperature were recorded for several years, initially under bare (no-WEC) condition (4 years), then vegetated (no-WEC) condition (5 years) and, finally, with a WEC placed on the top of the SILT (SILT+WEC condition; 3 years). The hydrological effect of the WEC was assessed by comparing the response of the SILT+WEC with the SILT under bare or vegetated conditions. The WEC reduces water losses by evaporation, thus increasing the average water content in the underlying SILT, an effect that is detrimental to slope stability. To discriminate whether the barrier effect was associated with the lower thermal or hydraulic conductivity of the WEC, a numerical simulation was carried out by considering the case of a WEC with its real thermal and hydraulic properties and the case of a fictitious top layer placed on the top of the SILT having the same hydraulic properties of the WEC but the thermal properties of the SILT. It is concluded that the barrier effect of the WEC is mainly associated with its hydraulic properties, i.e. the WEC acts as a capillary barrier. To demonstrate the practical implications of this findings, a case study of rainfall-induced landslide has been reanalysed by simulating the presence of a WEC layer having the same thermohydraulic properties as the material characterised in this study. It is shown that a WEC can substantially reduce the severity of the triggering rainfall event, thus increasing the vulnerability of the slope to rainfall-induced failure.

Reconstruction and verification of mechanism and triggering conditions of the Hermitage landslide at the Vaches Noires cliffs (Normandy)

The Pays d'Auge region is known for its diversity of instability including large and deep-seated landslides. Among them, the “L'Hermitage landslide” is one of the largest historical phenomena in the Vaches Noires area of Normandy, France. As it was triggered during a period when few observations were performed, there is still considerable uncertainty about the conditions under which it was triggered. While it remains an epiphenomenon in the Vaches Noires area, the fact is that the area could be prone to other large landslides. It therefore seems essential and necessary to study these old landslides in order to better understand the evolution of this type of cliff. This article presents a detailed study of this major landslide. The main objective is to reconstruct the evolution of the landslide and verify the conditions that led to its triggering. To this end, a conceptual model of functioning, including a trigger scenario, was proposed using a multidisciplinary approach integrating historical, geomorphological, geological and hydrological data. This scenario was then evaluated and verified by finite-difference numerical modeling (FDM) with FLAC2D®. This approach enabled to propose probable trigger conditions and to identify more precisely the different contributing factors, causes and mechanisms influencing its development.

Photomonitoring as a Tool for Monitoring Landslides: A Technology within Everyone’s Reach

Landslides pose significant threats to human lives and infrastructure, making accurate monitoring essential for understanding and preventing these events. Photomonitoring, utilizing optical/multispectral sensors and digital image processing techniques, has emerged as a valuable tool for studying and monitoring landslide deformation processes. This article explores the application of photomonitoring in landslide studies, presenting three diverse case studies showcasing its efficacy and versatility. The technique allows for continuous, automated, and real-time monitoring of landslide events, providing valuable insights into displacement patterns and deformation trends. To enhance its usability, the software IRIS was developed to analyze data acquired from various platforms and sensors. This innovative solution promises to improve landslide management, contributing to better comprehension, prevention, and mitigation strategies, ultimately ensuring the safety of communities and infrastructure in landslide-prone regions.

Image analysis and LSTM methods for forecasting surficial displacements of a landslide triggered by snowfall and rainfall

AbstractLandslide-prone areas, predominantly located in mountainous regions with abundant rainfall, present unique challenges when subject to significant snowfall at high altitudes. Understanding the role of snow accumulation and melting, alongside rainfall and other environmental variables like temperature and humidity, is crucial for assessing landslide stability. To pursue this aim, the present study focuses first on the quantification of snow accumulated on a slope through a simple parameter obtained with image processing. Then, this parameter is included in a slope displacement prediction analysis carried out with long short-term memory (LSTM) neural network. By employing image processing algorithms and filtering out noise from white-shown rocks, the methodology evaluates the percentage of snow cover in RGB images. Subsequent LSTM forecasts of landslide displacement utilize 28-day historical data on rainfall, snow, and slope movements. The presented procedure is applied to the case of a deep-seated landslide in Italy, a site that in winter 2020–2021 experienced heavy snowfall, leading to significant snow accumulation on the slope. These episodes motivated a study aimed at forecasting the superficial displacements of this landslide, considering the presence of snow both at that time and in the following days, along with humidity and temperature. This approach indirectly incorporates snow accumulation and potential melting phenomena into the model. Although the subsequent winters were characterized by reduced snowfall, including this information in the LSTM model for the period characterized by snow on the slope demonstrated a dependency of the predictions on this parameter, thus suggesting that snow is indeed a significant factor in accelerating landslide movements. In this context, detecting snow and incorporating it into the predictive model emerges as a significant aspect for considering the effects of winter snowfall. The method aims to propose an innovative strategy that can be applied in the future to the study of the landslide analyzed in this paper during upcoming winters characterized by significant snowfall, as well as to other case studies of landslides at high altitudes that lack precise snow precipitation recording instruments.

Debris Avalanches in the Northern California Coast Range Triggered by Plate-Boundary Earthquakes

ABSTRACT Determining the timing and cause for ancient hillslope failures is challenging in the western United States, yet it is critical because it can inform ground-motion estimates for large-to-great earthquakes on plate-boundary faults. We identify two preinstumental debris avalanches (3×106 and 4.28×106 m3) in the Coast Ranges of northern California. These debris avalanches are well suited for studying the timing (to the exact year) and trigger, as the densely forested landscape enables effective use of dendrochronology and high-resolution radiocarbon to pinpoint the time of failure, and the legacy of landslide studies in the region provides context for evaluating a seismic trigger as the most likely triggering mechanism. Neither of the debris avalanches was triggered by the largest precipitation event of the instrumental record in 1964 C.E., and the two debris avalanche sites are physiographically suited to experience topographic amplification of seismic shaking. Through a suite of geologic and dendrochronologic evidence, we establish the likely time of failure of the two debris avalanches as 1906 C.E. and 933 C.E. The failure of the younger landslide coincides with the 1906 C.E. San Francisco earthquake on the Pacific/North American plate boundary, whereas the older landslide dates to 933 C.E., within the broad age range (850–966 C.E.) of a likely megathrust earthquake on the Cascadia subduction zone as recorded in coastal marshes in Humboldt Bay, California. If the older landslide was triggered by a subduction earthquake, then the precise age of the 933 C.E. debris avalanche would trim the uncertainty on a Cascadia subduction zone event from 116 yr to 1 yr. Utilizing debris avalanche records from sites suitable to record seismic shaking improves understanding of plate-boundary earthquake timing and extent of shaking.

Inversion Study on Landslide Seepage Field Based on Swarm Intelligence Optimization Least-Square Support Vector Machine Algorithm

The permeability coefficient of landslide mass, a key parameter in the study of reservoir landslides, is commonly obtained through in situ and laboratory tests; however, the tests are costly and subject to high variability, leading to potential biases. In this paper, a new method was proposed to inversely estimate the permeability coefficient of landslide layers using monitoring data of groundwater level (GWL). First, the landslide transient seepage simulation was conducted to generate sample data for permeability coefficients and GWL during a reservoir operation cycle. Second, using GWL data as input and permeability coefficient data as output, the least-square support vector machine (LSSVM) was trained with two optimization algorithms, the particle swarm optimization (PSO) algorithm and the whale optimization algorithm (WOA), to construct the nonlinear mapping relationship between simulated GWL and permeability coefficients. Third, the accurate permeability coefficients for landslide seepage simulation were inverted or predicted based on the monitored GWL. Finally, using the inverted permeability coefficients for landslide seepage simulation, we compared simulation results with actual monitored GWL and achieved good consistency. In addition, this paper compared the inversion effects of three different algorithms: the standard LSSVM, PSO-LSSVM, and WOA-LSSVM. This study showed that these three algorithms had good nonlinear fitting effects in studying landslide seepage fields. Among them, using the inversion values from PSO-LSSVM for landslide seepage simulation resulted in the smallest relative error compared to actual monitoring data. Within a single reservoir operation cycle, the simulated water level changes were also largely consistent with the monitored water level changes. The results could provide a reference to determine landslide permeability coefficients and seepage.

Artificial Intelligence-Based Landslide Studies in Indonesia: A Systematic Review in Recent Years

Abstract Landslide is still a hot topic in geological hazard discussion, including Indonesia. Various methods, including Artificial Intelligence (AI), are used to do research development on landslide topics. Therefore, this paper aims to present a comprehensive review of AI-based landslide studies that focus on specific application area, feature engineering method (FEM), and Digital Elevation Model (DEM) sources used in the studies. This research used a qualitative method with a systematic review approach toward recent landslide studies (2012-2022) that investigated systematically in a synthesis. The exploration resulted in 26 papers from national and international indexed journals or proceedings, which filtered into 13 articles that discuss or mention the specific application area, FEM, and DEM sources. The analysis shows that AI applications in landslide studies are dominated for landslide susceptibility mapping and still a few for other applications. It also shows that almost all AI-based landslide studies chose SRTM as the source of DEM. Regarding FEM, only five articles discussed important landslide factor selection. There are four FEMs that were used in those studies, i.e., variable deduction, certainty factor model, C.45 algorithm, and variable importance ranking. From the deep analysis of those 13 articles, it can be concluded that AI-based landslide studies in Indonesia still need to be developed instead of focusing on landslide susceptibility mapping only. Studies to find effective landslide factors and compatible DEM resources using AI also can be new opportunities for landslide experts.

Study of rainfall-induced landslide using a self-developed tilt monitoring system: a physical and numerical modelling approach

Study of rainfall-induced landslide using a self-developed tilt monitoring system: a physical and numerical modelling approach

Comprehensive risk management of reservoir landslide-tsunami hazard chains: a case study of the Liangshuijing landslide in the Three Gorges Reservoir area

AbstractLandslide-induced tsunamis in reservoirs often pose greater risks than landslides themselves. Existing studies and mitigation practices have prioritized increasing landslide stability to meet safety standards, incurring high costs and lacking sustainability. More than 5000 landslides have developed along the Three Gorges Reservoir banks in China, and some may trigger tsunamis. To effectively and sustainably manage risk of landslide-tsunami hazard chains (LTHC), we propose a new framework (based on quantitative risk analysis) involves decision-making based on risk characteristics and local economies, a strategy for selecting optimal risk control plan through cost–benefit analyses that consider long-term effectiveness and adverse effects, and dynamic management through iterative risk reassessment-recontrol cycles. The Liangshuijing landslide accelerated in 2009, posing a tsunami threat to the Yangtze River and coastal residents. Post-mitigation, the risks under four mitigation plans and combined scenarios were quantitatively evaluated. The mass removal plan reduces total risk by approximately 68%, less than the 80% reduction from the anti-slip pile plan, but with a 30-fold greater benefit–cost ratio (BCR). The surface drainage and monitoring network plans show limited long-term cost-effectiveness due to short measure lifetimes, however, their short-term BCRs exceed those of the mass removal plan due to lower direct costs. According to comprehensive long and short-term analyses, combined plans involving mass removal are the most cost-effective. Our results reveal how to select appropriate strategies for managing LTHC risk, how landslide mitigation controls tsunami risks, and how time-related parameters affect solution effectiveness. Our findings show that partially mitigating risks may provide more cost-effective and sustainable management of LTHCs than eliminating risks to negligible levels.

Effects of improper emergency mitigations on a large-scale landslide triggered by road excavation: A case study of the Fengping landslide, Hubei province, China

Effects of improper emergency mitigations on a large-scale landslide triggered by road excavation: A case study of the Fengping landslide, Hubei province, China

Uncertainty reduction strategies to enhance geotechnical site characterization: a case study of the red roof landslide in Wyoming

This study presents a method for identifying strategic locations to drill additional boreholes by quantifying and reducing subsurface uncertainties in geotechnical site investigations. The case study is the Red Roof landslide site located near milepost 140 on US Highway 26/89 in Teton County, Wyoming. A landslide remediation report had recommended additional boreholes before completion of the project. Two primary sources of uncertainty in geomaterials, namely geological and ground, are evaluated to determine the locations for these additional boreholes. The study aims to enhance site characterization and improve the accuracy of geotechnical assessments by strategically selecting and drilling additional boreholes that will reduce these subsurface uncertainties. The method involves reviewing and collecting the available site investigation data. An extensive geostatistical simulation is conducted based on the available data to identify spatial locations at the site that have uncertain subsurface conditions. Data from the additional boreholes could be utilized in these areas to reduce the uncertainty. This approach aligns with the current multi-phasing of site investigation in engineering practice, where a preliminary investigation is conducted before a detailed investigation. The results show that the locations of additional boreholes identified using this method differ from the ones earlier recommended, showcasing the effectiveness of the methodology in this application. This study also provides insights into effective strategies for reducing uncertainties through strategic borehole placement in similar geotechnical investigations. By quantifying geological and ground uncertainties, the method enables informed decision-making for slope stability analysis and risk assessment, with implications for infrastructure stability and geohazard mitigation.

Enhancing landslide hazard prevention: Mapping vulnerability via considering the effects of human factors

Vulnerability mapping is necessary to reduce the effects of a landslide disaster. This study presents an approach to map vulnerability induced by a landslide hazard. The vulnerability was assessed by improved multi-criteria decision making (MCDM), in which the effects of human factors were incorporated. In the proposed approach, the weights of the indicators and factors were calculated using an integration method, including a grey decision-making trial and evaluation laboratory and the analytical hierarchy process (AHP) to calculate each index and factor's weight. The effect of human factors is expressed as the ability of residents to cope with hazards in a community, which is evaluated by the adaptability index layer. The adaptability index layer includes four factors: gender ratio, age, education and medical resources. VIKOR is used to score and rank the adaptability index layer. The vulnerability is then mapped using geographic information system (GIS). A case study of historical landslides in Heyuan, China, was used for validation. The results demonstrate that 17.91 % of Heyuan has the highest vulnerability, which is higher than that from the traditional method (13.76 %). The model, considering adaptability, can predict areas with high vulnerability more accurately. Management recommendations are proposed via the following four specific aspects: i) urban vulnerability management and planning; ii) inter-regional differences in socio-economic vulnerability; iii) individual perceptions of landslides and iv) enhancing urban planning and strategies in similar cities.

Joint InSAR and discrete element numerical simulation method for landslide identification and monitoring: a case study of the Gongjue landslide, Jinsha River, China

Joint InSAR and discrete element numerical simulation method for landslide identification and monitoring: a case study of the Gongjue landslide, Jinsha River, China

Indicating landslide hazard from tree rings – Ecosystem service provided by an alder forest in the hengduan Mts, Sichuan, China

Landslides are destructive geomorphological processes that cause economic and social losses. This stimulates the development of new tools related to landslide hazard. Recently, trees, their growth rings and dendrochronology have become widely used in landslide studies. Thus, this study aims to explore the potential of trees in providing landslide-monitoring ecosystem services through dendrochronology. In our opinion, establishing such an idea would help promote empirical evidence on the efficiency of tree-ring-based tools to decision-makers. We use the example of a landslide in the Moxi basin, Sichuan, China and present results of dendrochronological analysis of growth eccentricity in 48 Nepalese alder (Alnus nepalensis) trees. This analysis provided data on event timing and magnitudes, average frequency and recurrence interval for reactivation of the study landslide, as well as spatial variability of landslide active. Based on dendrochronological data we were also able to determine the current slope balance and general hazard of landslide reactivation on the study slope. Our study shows that trees and dendrochronology can provide data on the activity of landslides that can complement and improve the results of standard engineering methods. Moreover, dendrochronology itself can provide the full information needed for landslide hazard assessment, monitoring and prediction.

Mass movement susceptibility: the case study of Tongo-Gadima landslide, East Cameroon

Mass movement susceptibility: the case study of Tongo-Gadima landslide, East Cameroon