Landslide Risk Research Articles

Characterizing the evolution of the Daguangbao landslide nearly 15 years after the 2008 Wenchuan earthquake by InSAR observations

The Daguangbao landslide (DGBL), the largest landslide triggered by the 2008 Ms 8.0 Wenchuan earthquake, has received much attention, but its long-term post-earthquake evolution and driving force of activity are still poorly understood. As the evolutionary behavior of the DGBL is complicated by the influence of mainshock, aftershocks and rainfall, it is of great significance to study the dynamics of the landslide. In this study, a systematic and comprehensive framework for assessing the long-term stability and risk of co-seismic landslides was proposed. Based on ALOS-1 and Sentinel-1 data, time-series InSAR technology was used to reveal the nearly 15-year post-seismic evolution characteristics of the DGBL at different stages, followed by the prediction of the stabilization time, the estimation of the landslide thickness and risk assessment. The first stage was identified as three years after the earthquake (2008–2011). During this stage, ALOS-1 results show that the deformation of DGBL was intense (300 mm/year) with uneven spatial distribution, and an aftershock (Ms 5.3), along with increased rainfall, triggered its acceleration in 2009. The second stage was the period from 2014 to 2022. For this stage, we used the mass conservation approach to invert the thickness of the DGBL, revealing that a new sliding surface and thickness center had formed following the co-seismic failure in 2008. Sentinel-1 time series results indicated that the DGBL remains active even 15 years after the Wenchuan earthquake, but the deformation of DGBL has significantly slowed down (50 mm/year). The stabilization time for different segments of DGBL was predicted to range from 2027 to 2040 according to an exponential model. Beyond the overall trend of recovery, seasonal movements (including localized acceleration in 2021) closely related to rainfall remained evident, but the impact of aftershocks on the DGBL was severely weakened over time. UAV and field survey results suggested that the risk of localized debris flows at DGBL still exists. Our study improves our understanding of the long-term evolutionary pattern of DGBL and provides an important reference for post-earthquake landslide risk assessment and disaster prevention.

Landslide risk assessment in mining areas using hybrid machine learning methods under fuzzy environment

Landslide risk assessment in coal mining economic cities faces challenges due to inadequate knowledge of indicator systems and modeling techniques, despite its crucial significance in geological disaster management. This study addresses this gap by proposing a novel framework encompassing data preparation, susceptibility analysis, consequence analysis, and final risk assessment. The primary objective is to reduce the complexity and inherent uncertainties associated with landslide risk assessment in these specific urban environments. First, two comprehensive databases for the indication system were established. One database focuses on landslide susceptibility, considering the region’s mining disturbances and disaster-prone environments. The other evaluates the effect of consequences while accounting for the pertinent risk variables. Second, a hybrid machine learning model called Geo-FR-SVM was developed by combining the GeoDetector, Frequency Ratio method, and Support Vector Machine model to improve landslide susceptibility mapping. Integrating the susceptibility map with the consequence map created using fuzzy set theory and fuzzy analytical hierarchy process methodologies resulted in the final landslide risk map. Our analysis revealed that elevation, distance to rivers and roads, groundwater level, and coal mining subsidence were the key factors influencing landslide occurrence in the Xishan mine area. The majority of the study area exhibits low landslide risk. However, higher consequence zones were primarily concentrated in highly populated residential regions and coal mine industrial zones in the east-central and western urban areas. Additionally, they are mainly distributed along the roads in the center region. With comparatively large percentages of landslide high-risk and extremely high-risk regions (4.596%, 3.855%, 3.625%, and 3.188%, respectively) at Tunlan Mine, Xiqu Mine, Zhenchengdi Mine, and Baijiazhuang Mine, these findings highlight the need for these mines to prioritize potential landslide identification and risk mitigation strategies. This framework offers broad applicability to other coal mining economic cities, serving as a valuable foundation for landslide risk management decision-making.

Geospatial Intelligence for Landslide Susceptibility and Risk Analysis: Insights from NH31A and East Sikkim Himalaya Settlements

Slope instability is a serious concern in the Sikkim Himalayas. The town and numerous road segments along National Highway 31A were ravaged by multiple landslides that occurred in the nearby region. A bivariate statistical method known as frequency ratio (FR), information value (IV), and certainty factor (CF) analysis was employed in this work to examine landslide risk assessment (LRA) and landslide susceptibility zonation (LSZ) maps in the Rorachu watershed. This study represents the first comprehensive analysis of landslide risk in the populated areas of East Sikkim and along NH31A, offering a deeper understanding of the risks involved and contributing to the enhancement of local resilience against landslide hazards. A total of 153 different landslide locations were mapped using Google Earth and GIS software; 30% (46) of these locations were used to validate the models, and 70% of these (107) served as training data for the FR, IV, and CF models. The thirteen landslide causative factors (geology, soil, elevations, slope, curvature, drainage density (DD), road density (RD), rainfall, normalized difference vegetation index (NDVI), land use land cover (LULC), topographic position index (TPI), stream power index (SPI), and topographic wetness index (TWI)) were extracted from a spatial database for LSZ mapping. Landslides were most prevalent on slopes (35° to 50°), heights (2,500–4,100 m), and rainfall (2000–2,500 mm and 3,000–3,300 mm). The area under the curves (AUC) for the FR, IV, and CF models are 0.925 (92.50%), 0.846 (84.60%), and 0.868 (86.20%), respectively. The prediction rates are shown by the AUCs for the FR, IV, and CF models, which are 0.828 (82.8%), 0.750 (%), and 0.836 (83.60%), respectively. According to the landslide risk assessment (LRA), the FR (20.75%), IV (40.91%) and CF (18.78%) models showed high risk on Highway 31A, while the FR (9.05%), IV (38.59%) and CF (20.90%) models showed high risk in densely populated areas. These landslide risk and vulnerability maps can be used to develop land use planning strategies that can save lives and are useful for planners and mitigation measures. Special attention should be paid to urbanization, highway construction, and deforestation.

Morphometric analysis and LULC change dynamics of Nayar watershed for the sustainable watershed management

The morphometric analysis of the watersheds is essential for the conservation of natural resources, including soil, water, and vegetation. The Morphometric analysis defines the linear, areal and relief aspects of the watershed. It involves the comprehensive analysis of various factors such as drainage network, surface water flow, and other topographical features. The aim of this study is to develop a sustainable watershed management strategy for the Nayar watershed based on morphometry and Land Use Land Cover (LULC) change dynamics. The Nayar watershed has a total area of 1956.33 km2. The multispectral satellite imagery, Digital Elevation Model (DEM) data, and Survey of India (SOI) Toposheets were used for understanding the topographical and morphological characteristics along with the LULC change dynamics. The findings of this research conclude that the Nayar watershed has parallel and dendritic drainage patterns with high relief. According to the LULC change dynamics, the Nayar watershed's area change comprising 57.60 km2 and 57.15 km2, are from Agricultural Land to Wasteland and from Forest Cover to Wasteland, respectively. This shift in the hilly region will increase the risks of landslides and erosion. Furthermore, the change of 110.03 km2 area of Forest Cover to Agricultural Land raises further challenges due to loss of natural vegetation in long run. In summary the change in LULC of Nayar watershed is vulnerable to risk of natural calamities like landslide, erosion. This work would be helpful to many experts and decision-makers for sustainable watershed management and natural resource management.

Rainfall characteristics and magnitude control the volume of shallow and deep-seated landslides: Inferences from analyses using a simple runoff model

Landslide volume plays a pivotal role in controlling landslide movement and potential damage. Although rainfall is widely recognized as one of the most important factors underlying landslide occurrence worldwide, its impact on landslide volume has been investigated only for individual landslide types. In this study, we show that rainfall characteristics and magnitude control the volume produced by both shallow and deep-seated landslides. A total of ten shallow and deep-seated landslides in Japan were compiled with volume, occurrence time, and rainfall data. Rainfall characteristics that triggered landslides were identified using the Soil Water Index and the three-layer tank model, which is a simple runoff model, and magnitude was quantified based on lag time. A strong positive correlation was found between lag time and landslide volume, indicating that landslide volume increases with increasing magnitude of rainfall to induce landslides. This study is the first attempt to suggest a relationship between rainfall magnitude and the volume produced by shallow and deep-seated landslides systematically and will promote the development of landslide risk management strategies.

Machine Learning Approaches for Mapping and Predicting Landslide-Prone Areas in São Sebastião (Southeast Brazil)

This study employs machine learning techniques to map and predict landslide-prone areas in São Sebastião, Brazil, a region susceptible to landslides due to its steep terrain and intense rainfall. We compared five algorithms: Random Forest, Gradient Boosting, Support Vector Machine, Artificial Neural Network, and k-Nearest Neighbors, using various environmental factors as inputs. The Gradient Boosting model performed best, achieving an AUC-ROC of 0.963 and an accuracy of 99.6%. Slope degree, soil moisture index, and relief dissection emerged as the most influential factors in predicting landslide susceptibility. Analysis of land use and land cover changes between 1985 and 2021 revealed significant increases in forest cover and urban areas, with implications for landslide risk distribution. The resulting susceptibility map shows predominantly low-risk areas with scattered high-risk zones, providing crucial information for targeted risk management. This research demonstrates the effectiveness of machine learning in landslide susceptibility mapping and offers valuable insights for disaster risk reduction and urban planning in coastal mountainous regions.

Landslide Hazard Is Projected to Increase Across High Mountain Asia

AbstractHigh Mountain Asia has long been known as a hotspot for landslide risk, and studies have suggested that landslide hazard is likely to increase in this region over the coming decades. Extreme precipitation may become more frequent, with a nonlinear response relative to increasing global temperatures. However, these changes are geographically varied. This article maps probable changes to landslide hazard, as shown by a landslide hazard indicator (LHI) derived from downscaled precipitation and temperature. In order to capture the nonlinear response of slopes to extreme precipitation, a simple machine‐learning model was trained on a database of landslides across High Mountain Asia to develop a regional LHI. This model was applied to statistically downscaled data from the 30 members of the Seamless System for Prediction and Earth System Research large ensembles to produce a range of possible outcomes under the Shared Socioeconomic Pathways 2‐4.5 and 5‐8.5. The LHI reveals that landslide hazard will increase in most parts of High Mountain Asia. Absolute increases will be highest in already hazardous areas such as the Central Himalaya, but relative change is greatest on the Tibetan Plateau. Even in regions where landslide hazard declines by year 2100, it will increase prior to the mid‐century mark. However, the seasonal cycle of landslide occurrence will not change greatly across High Mountain Asia. Although substantial uncertainty remains in these projections, the overall direction of change seems reliable. These findings highlight the importance of continued analysis to inform disaster risk reduction strategies for stakeholders across High Mountain Asia.

Can satellite InSAR innovate the way of large landslide early warning?

Predicting landslide failure times is an essential component in landslide risk management. Although in-situ sensor-supported landslide early warning systems are still predominantly used, their high cost makes it impractical to monitor all the landslides, thereby posing a major challenge for the effective landslide risk management. Hence, this study investigated this problem from an earth observation perspective and proposed a probabilistic landslide failure time prediction framework integrating Interferometric Synthetic Aperture Radar (InSAR) monitoring information. Accordingly, 30 historical landslides that occurred between 2016 and 2021 in central and western China were collected to evaluate the feasibility of the aforementioned framework. Based on the landslide dataset, the performance of the satellite InSAR technology for landslide failure time prediction is evaluated systematically from an application perspective. It was evident that eleven landslides (36.67 %) were captured by InSAR with accelerated deformation signals before failure, and monitoring data from eight (26.67 %) of them provided enough information for their failure time prediction. Further, a probabilistic method integrating the conventional inverse velocity model and sequential Bayesian updating was proposed to dynamically predict the most likely failure time and related confidence interval. Case studies showed that the proposed method could successfully predict the failure time of the eight landslides, thus demonstrating the feasibility of the framework. Although the current long revisit period of satellites constrains their performance practically, this problem can be solved by advancements in future satellite missions. Thus, we believe that the InSAR era is imminent and will bring substantial values for large landslide early warning.

Landslides hazard, vulnerability and risk mapping in the data-poor region of northern Pakistan

Landslides hazard, vulnerability and risk mapping in the data-poor region of northern Pakistan

Landslide Susceptibility Mapping in Nigeria Using Remote Sensing, GIS, and Machine Learning Models

Landslide situations remain a significant concern in Nigeria, as they pose a great risk to human lives, property, and the natural environment, particularly in regions with steep slopes, heavy rainfall, and unfavorable human interference, including deforestation and urbanization. Nigeria’s diverse geographical structure, ranging from urban areas such as Lagos and Abuja to rural regions, underscores the importance of accurate predictions for risk management and avoidance techniques. This research utilized remote sensing and GIS assessment to analyze landslide susceptibility in several regions of Nigeria. Data related to terrain, vegetation, soil moisture, and ground deformation were gathered using high-resolution satellite imagery from sources such as SPOT, ASTER, differential synthetic aperture radar interferometry (D-InSAR), and Landsat TM. GIS data layers included DEM, LULC, soil and geological maps, as well as hydrological maps and data. Methods applied in this study include logistic regression, STAT-R, frequency ratio, and the Random Forest tree-based model. The research produced detailed landslide susceptibility maps for various regions in Nigeria and identified significant factors such as slope, elevation, land use, precipitation, and access to transportation facilities. The Random Forest model demonstrated the most robust predictive capability. The integration of remote sensing with GIS was particularly significant, enhancing the precision of predictions and improving the efficacy of planning and management strategies. By incorporating remote sensing, GIS, and various machine learning algorithms, the researchers have developed a reliable tool for landslide risk prediction and management in Nigeria. Future research should focus on improving data quality and enhancing the generalizability of results to other regions.

Prediction of Landslide Susceptibility in the Karakorum under the Context of Climate Change

Climate change has recently increased the frequency of landslides in alpine areas. Susceptibility mapping is crucial for anticipating and assessing landslide risk. However, traditional methods focus on static environmental variables to emphasize the spatial distribution of landslides, ignoring temporal dynamics in landslide development in the context of climate change. In this work, we focused on static and dynamic environment factors and utilized the certainty factor-logistic regression (CF-LR) model to assess and predict landslide susceptibility in Taxkorgan County, located in the Karakorum. The assessment and prediction were based on a catalog of climate change-related landslides over the past 20 years, the causative factors, and predicted climatic variables for the Shared Socioeconomic Pathways (SSP1-2.6) scenario. The results indicated that elevation, slope, groundwater, slope length gradient (LS) factor, Topographic Wetness Index (TWI), valley depth, and maximum precipitation were the key causes of slides below the snow line. The key factors causing debris flow above the snow line were elevation, slope, topographic relief, aspect, LS factor, distance to the river, and maximum temperature. The accuracy of slide and debris flow susceptibility was 0.92 and 0.89, respectively. The area of slides with medium, high, and very high susceptibility is 25.5% of the Taxkorgan. In addition, 82.6% of the slides happened in this region, and 49.5% of the entire area is covered by debris flows with medium, high, and very high susceptibility. Moreover, this area accounts for 91.8% of all debris flows. Until 2060, the region’s climate is anticipated to become warmer and wetter. Slides below the snow line will gradually decrease and shift eastward, and debris flows above the snow line will expand. Our findings will contribute to the management of landslide risks at the regional scale.

More than one landslide per road kilometer – surveying and modeling mass movements along the Rishikesh–Joshimath (NH-7) highway, Uttarakhand, India

Abstract. The rapidly expanding Himalayan road network connects rural mountainous regions. However, the fragility of the landscape and poor road construction practices lead to frequent mass movements alongside roads. In this study, we investigate fully or partially road-blocking landslides along National Highway (NH-7) in Uttarakhand, India, between Rishikesh and Joshimath. Based on an inventory of >300 landslides along the ∼250 km long corridor following exceptionally high rainfall during September and October 2022, we identify the main controls on the spatial occurrence of mass-movement events. Our analysis and modeling approach conceptualizes landslides as a network-attached spatial point pattern. We evaluate different gridded rainfall products and infer the controls on landslide occurrence using Bayesian analysis of an inhomogeneous Poisson process model. Our results reveal that slope, rainfall amounts, lithology and road widening are the main controls on landslide occurrence. The individual effects of aggregated lithozones are consistent with previous assessments of landslide susceptibilities of rock types in the Himalayas. Our model spatially predicts landslide occurrences and can be adapted to other rainfall scenarios, thus having potential applications for efficiently allocating efforts for road maintenance. To this end, our results highlight the vulnerability of the Himalayan road network to landslides. Climate change and increasing exposure along this pilgrimage route will likely exacerbate landslide risk along the NH-7 in the future.

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.

A stringent failure: Regulators do not use available tools to protect aquatic ecosystems from clearcut forestry impacts in New Zealand

Effective regulation of land-use activities in steepland areas is crucial to protect downstream ecologies and human life as intense rainfall disturbances become more frequent globally. In Aotearoa New Zealand extensive synchronous clear-cutting of Pinus radiata monocultures on steep convergent landforms, and associated earthworks, causes ongoing accelerated erosion, excessive sedimentation, and debris-laden landslides after adverse weather events. This study examines the implementation of national forestry regulations established in 2018, which also enable regional councils to create more stringent rules to protect aquatic ecosystems. All sixteen councils were surveyed in 2021 and 2022 for stringency in planning provisions; and four regions seriously affected by Cyclone Gabrielle in 2023 were resurveyed in 2024. The cyclone caused loss of human life and calamitous damage to housing, infrastructure, and productive land uses from floods exacerbated by clearcut logging debris. All councils had administratively adopted the national regulations into their existing freshwater and coastal resource management plans. Twelve councils retained existing rules that conflicted with the regulations, but these rules do not protect all regionally significant aquatic ecosystems nor minimise landslide risks. No council, except Gisborne, had instigated the expensive and lengthy statutory resource management plan change process, nor taken a ‘strategic and principled’ approach to develop more stringent regulations, such as tougher restrictions on clear-cutting, earthworks, and replanting on steep erosion-prone convergent landforms to protect aquatic ecosystems and vulnerable communities. The government did tighten the management of logging debris after the cyclone, but the national regulations remain ineffective in addressing clear-cut practices maladaptive to intense rainfall and continue to permit replanting on convergent landforms. The regulations need urgent amendment to require councils to develop and implement a strategic and principled approach to stringency to better protect aquatic ecosystems, human life, economic livelihoods, and public infrastructure. Water quality monitoring is also currently inadequate, as no council systematically monitors the effects of forestry activities on sedimentation rates, which inhibits the ability to compare across and between regions. Foresters are not required to monitor water quality, which also stymies assessments of compliance and policy effectiveness. Councils regularly monitor rivers, lakes, and estuaries, but rarely the effects of individual land uses. This means that sediment or other contaminants cannot be parsed to different land use activities, undermining attempts to set catchment limits. It can also take decades at current monitoring levels to defensibly show any water quality improvements after changes to land use regulations. The current environmental limits approach of setting individual water quality attribute targets is highly unlikely to drive changes to maladaptive and ecologically degrading land uses. A new environmental management approach is needed that prohibits or effectively constrains hazardous and adverse activities.

Experimental Investigation on Shear Strength at the Permeable Concrete–Fine-Grained Soil Interface for Slope Stabilization Using Deep Socket Counterfort Drains

In slopes where high pore water pressure exists, deep counterfort drains (also called drainage trenches or trench drains) represent one of the most effective methods for improving stability or mitigating landslide risks. In the cases of deep or very deep slip surfaces, this method represents the only possible intervention. Trench drains can be realized by using panels or secant piles filled with coarse granular material or permeable concrete. If the trenches are adequately “socket” into the stable ground (for example sufficiently below the sliding surface of a landslide or below the critical slip surface of marginally stable slopes) and the filling material has sufficient shear strength and stiffness, like porous concrete, there is a further increase in shear strength due to the “shear keys” effect. The increase in shear strength is due both to the intrinsic resistance of the concrete on the sliding surface and the resistance at the concrete–soil interface (on the lateral surface of the trench). The latter can be very significant in relation to the thickness of the sliding mass, the “socket depth”, and the spacing between the trenches. The increase in shear strength linked to the “shear keys effect” depends on the state of the porous concrete–soil interface. For silty–clayey base soils, it is very significant and is of the same order of magnitude as the increase in shear resistance linked to the permanent reduction on the slip surface in pore water pressure (draining effect). This paper presents the results of an experimental investigation on the shear strength at the porous interface of concrete and fine-grained soils and demonstrates the high significance and effectiveness of the “shear keys” effect.

Fatal landslides in Kencho, Shacha & Gozdi villages, Gofa zone, Ethiopia: A detailed investigation (Geological, Geotechnical, geophysical & geospatial) of the July 22, 2024 catastrophe and its socioeconomic repercussions

A landslide is one of the geological hazards that cause the most disaster in densely populated areas. The landslide that occurred in Gezie Gofa woreda, Gofa Zone, Kencho Shacha Gozdi village, killed more than 250 people. Two landslides occurred on July 22, 2024, at 8:30 and 10:40 AM The first landslide killed six people and demolished three houses. The second landslide killed more than 245 people, including those who came to the site to excavate the buried bodies during the first landslide. The primary objective of this research is to evaluate the landslide causative factors, model the landslide susceptibility, and characterize the landslide disaster on socioeconomic effects that occurred on July 22, 2024. The landslide inventory data, field surveys, laboratory analyses, and various geophysical surveys characterized the current and past landslides of the area. The landslide susceptibility model was modeled using a statistical approach in the GIS. environment. The socioeconomic effects were assessed using field surveys and systematic interviews with the victims. The conditioning factors selected for landslide susceptibility modeling are lithology, geological structures, groundwater, slope, land use or land cover, aspect, curvature, and elevation. The major triggering factor of the landslide in the area was heavy rainfall, which occurred on July 21–22, 2024, between 3:00 a.m.-8:30 a.m. The results reveal that the significant conditioning factors of the landslide in the study area are geological structures (both visible and inferred), groundwater, slopes, and human activities. The characterized socioeconomic effects include the destruction of agricultural land, the demolishing of houses, and the loss of human lives, as well as several people evacuated and sheltered under tents and churches. However, the most momentous disaster in the area is the loss of human life. Based on the research results, it would be better to relocate those people living in the high landslide susceptible zones, and all high landslide-prone areas and mountainous terrain in southern Ethiopia should be mapped, and the people should be aware of the landslide risk areas.

Risk and vulnerability analysis of road network in landslide prone areas in Munnar region, India

Catastrophes like landslides have the potential to impair critical transportation infrastructure, particularly road networks. The hilly regions in the state of Kerala in India are particularly prone to hazards and changing climate conditions. During the monsoon season, landslides are common in the Western Ghats, and the intensity of adverse impacts is more severe due to its densely populated regions. The study area of this research is in Idukki district of Kerala, where over 60 % of the land is prone to landslides. The monsoon rains bring with them a slew of disastrous landslides in the region. Most of the roadways in the study area are often disrupted due to landslides. Landslide risk assessment (LRA) is a crucial component of research on adverse impacts of landslide. The use of Geographical information System (GIS) and Multi Criteria Decision Analysis (MCDA) using Analytical Hierarchical Process (AHP) for susceptibility mapping and hazard risk assessment assists in the identification of disaster-prone areas. The data collected by field surveys were used to confirm the study results and the high-risk zones of the region were identified and the risk maps are prepared for the region as well as for the road network in the region. The risk of landslides may be described as the possibility of negative repercussions on road network thereby adversely impacting the inhabitants of the region. The landslide risk assessment of the road network in a hilly region is carried out which gives important insights for risk management and for future planning of resilient development in the region. This study enhances the knowledge for management of road network risk and vulnerabilities in intricate hilly region settings by developing a thorough vulnerability analysis framework. The research advances by giving transportation engineers a useful quantitative tool for identifying the risk and vulnerabilities of road network and directing design strategies and mitigation measures to lessen the possible negative effects of disruptive events like landslides on road infrastructure. The research findings could be an input for policy makers to plan for alternative resilient strategies for landslide risk management in road networks. The rational methodology adopted here could be replicated for carrying out risk and vulnerability assessment in other landslide prone areas.

Identification of potential landslide in Jianzha county based on InSAR and deep learning

Landslide disasters have characteristics of frequent occurrence, widespread impact, and high destructiveness, posing serious threats to human lives, property, and the ecological environment. Timely and accurate early identification of landslides remains an urgent issue within the disaster prevention field. This study focuses on Jianzha County, Qinghai Province, integrating PS-InSAR, SBAS-InSAR, and optical remote-sensing techniques to delineate potential landslide-prone areas. Utilizing Google Earth imagery and existing landslide datasets, potential landslide points were identified through a deep learning model. Results indicate the following: (1) In Jianzha County, the variation trend of the average surface velocity monitored by PS-InSAR and SBAS-InSAR technology is consistent, and the deformation monitoring results are reliable. (2) Utilizing the deep learning model, 56 potential landslide points were identified, comprising 39 high-risk points and 17 medium-risk points. By integrating the spatial distribution data of historical geological disaster points, 10 out of 13 previously occurred landslide disaster points were found to be located at the identified high-risk landslide points, achieving a detection accuracy of 76.92%. (3) The spatial distribution of landslide points exhibits clustering, with slopes ranging from 10° to 40°, elevations between 15 and 30 m, and slope orientations predominantly toward the northeast. (4) Landslide formation is correlated with seasonal precipitation concentrations and temperature fluctuations. This method can provide a crucial basis for large-scale surface deformation monitoring and early identification of landslide risks.

Assessing Historical Landslide Risk Management Based on Trigger Magnitude and Consequences: A Case Study from the Rokko Mountains, Kobe, Japan

AbstractLandslides are a common hazard in mountainous regions, and many countries have implemented landslide risk management to mitigate their negative impacts. Assessing the effectiveness of those measures is important to improve technical and political decision-making and to enhance the selection and implementation of effective landslide risk management strategies. Here, we assessed effectiveness in landslide risk management based on the magnitude of rainfall characteristics that triggered landslides (inducing factor) and landslide consequences in the Rokko mountains, Kobe, Japan. The number of check dams was used as an indicator of progress in landslide risk management. For fatal landslide events in 1938 and 1967, rainfall characteristics that triggered landslides were estimated using the three-layer tank model, and their magnitude was quantified by the return period (RP). We then compared these rainfall magnitudes and landslide consequences (i.e., fatalities and completely collapsed houses) between the two events. The RP of the first tank storage layer value, which indicates rainfall characteristics triggering shallow landslides, was higher at landslides in 1967 than in 1938, whereas landslide consequences were less in 1967 than in 1938. 218 units of check dams were intensively constructed by landslide risk management from 1938 to 1967 and reduced the damage from landslides in 1967 that were triggered by higher magnitude rainfall than in 1938. This study also highlighted the importance of focusing on the magnitude of the inducing factor and landslide consequences to assess the effectiveness of landslide risk management at a local scale.

Identifikasi Bidang Gelincir Zona Rawan Longsor di Kawasan Wisata Puncak Taruko Kabupaten Agam Menggunakan Metode Geolistrik Resistivitas

A research has been conducted to identify the surface of rupture plane in Puncak Taruko area, Agam Regency, West Sumatra Province using 2-dimensional resistivity geoelectric method of Wenner-Schlumberger Configuration. Data collection was carried out on three tracks with track lengths of 80, 100 and 100 m and 5 m electrode spacing. The research area is a plateau with a slope of up to 40º which is directly adjacent to the very steep cliffs of Sianok Canyon with a cliff height of 30-100m. Data processing was carried out using Res2dinv software to display a 2-dimensional image of the subsurface layer structure based on the resistivity values measured in the field. In the cross section of the mapping results in the three tracks, 4 rock layers were identified, namely clay, sand, tuff and granite. The interpretation results show that the sliding plane on each track is tuff. For Track 1 with a depth of 3,75-15,9m, for track 2 it is 5,15-19,8 m and a depth of 6,38-19,8 m for track 3. Based on the interpretation of 2D image results, the area of track 1 and 2 has a translational avalanche type and the area of track 3 has a rotational avalanche type. Based on the depth and thickness of the slide field, the greatest risk of landslide is on track 3.