Landslide Risk Research Articles

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.

Landslide risk assessment combining kernel extreme learning machine and information value modeling-A case study of Jiaxian Country of loess plateau, China

Landslide risk mapping can be an effective reference for disaster mitigation and land use planning, but the modelling process involves multidisciplinary knowledge which leads to its complexity. In this study, Jiaxian County in Shaanxi Province on the Loess Plateau of China, served as the study area, primarily characterized by Quaternary loess-covered geomorphology, with an average rainfall of about 400 mm annually. Soil erosion and human engineering activities have contributed to significant slope failures, posing threats to local residents and infrastructure. A reasonable inventory of landslides in the region was established by field survey combined with aerial imagery, allowing for characterization of their development and spatial distribution. Nine thematic maps related to landslide occurring and three vulnerability maps were prepared as influencing factors for landslide risk assessment. Subsequently, landslide susceptibility and hazard were evaluated using a kernel extreme learning machine (KELM) and information value (IV) model, followed by map validation. A decision table was then employed to generate the landslide risk map. The results of landslide hazard mapping showed that the historical landslide events were mainly developed in the central part of the study area, particularly concentrated near the developed river network. Integration of overall risk elements suggested that landslide risks in the study area were generally at a low level. Besides, a total of 0.25 % and 2.05 % of the areas were classified as having very high and high landslide risk levels, respectively, where 65.11 % of inventory landslides occurred. Therefore, the proposed procedure is a valuable tool for assessing landslide risk in Jiaxian Country.

Multi-hazard assessment for flood and Landslide risk in Kalimantan and Sumatra: Implications for Nusantara, Indonesia's new capital

Situated within the Ring of Fire and characterized by a tropical climate and high seismic activity, Indonesia is uniquely vulnerable to natural disasters such as floods and landslides. These events pose significant threats to both the population and infrastructure. This study predicts areas exposed to flood and landslide risk by considering various environmental factors related to climate, topography, and land use. The predictive performance of three machine learning models—naïve Bayes, k-nearest neighbors, and random forest (RF)—was evaluated by comparing the AUC, RMSE, and R2 values of each model. Ultimately, the RF model, which demonstrated the highest accuracy, was used to prioritize disaster impact factors and generate hazard maps. The results identified the interaction of rainfall, land use, and slope aspect as the most critical determinants of hazard occurrence. The predicted hazard maps revealed that approximately 26.7 % of the study area was vulnerable to either floods or landslides, with 16.8 % of the area experiencing both. The new capital of Nusantara showed a relatively higher multi-hazard risk than did the overall study area and protected zones, with 22.1 % of the hazard area vulnerable to both flooding and landslides. In single hazard zones, areas classified as at risk for floods had a higher mean probability of experiencing both hazards (43 %), as compared to areas classified as at risk for landslides (22 %). As a result, urban planners and relevant stakeholders can now utilize the hazard maps developed in this study to prioritize infrastructure reinforcement and disaster risk areas, integrating land use planning with risk assessment to mitigate the impact of disasters. By employing these strategies, Indonesia and other countries facing similar challenges can now enhance their disaster preparedness and response capabilities in new capital regions and other areas, ultimately planning for more sustainable urban development.

Geotechnical Evaluation of Landslide Risks in Bali's Tourism Zones – A Case Study from Candidasa, Bali, Indonesia

Geotechnical Evaluation of Landslide Risks in Bali's Tourism Zones – A Case Study from Candidasa, Bali, Indonesia

GIS Based Spatial Analysis of Landslide Hazard in Tehsil Balakot, KP, Pakistan

The Himalayan region is highly susceptible to landslides due to intense rainfall and seismic activity, posing significant risks to slope stability. Due to landslides, Tehsil Balakot valley has suffered substantial damage to infrastructure, roads, and the tourism sector. This research aims to develop a landslide susceptibility map and identify hazard-prone areas to support sustainable development and mitigate risks in Balakot valley. The study employs a weighted overlay analysis using primary and secondary data raster layers, including slope, aspect, precipitation, and seismic raster maps. The slope and aspect maps are derived from a 30-meter ASTER digital elevation model, while precipitation maps are generated using Inverse Distance Weighted (IDW) interpolating data from the Pakistan Meteorological Department. Seismic data is acquired from the Geological Survey of Pakistan (GSP). The resulting landslide susceptibility map classifies the region into five hazard classes: very high, high, medium, low, and very low. The landslide susceptibility maps provide valuable insights for identifying areas at varying levels of landslide risk.

InSAR-Driven Dynamic Landslide Hazard Mapping in Highly Vegetated Area

Landslide hazard mapping is important to urban construction and landslide risk management. Dynamic landslide hazard mapping considers landslide deformation with changes in the environment. It can show more details of the landslide process state. Landslides in highly vegetated areas are difficult to observe directly, which makes landslide hazard mapping much more challenging. The application of multi-InSAR opens new ideas for dynamic landslide hazard mapping. Specifically, landslide susceptibility mapping reflects the spatial probability of landslides. For rainfall-induced landslides, the scale exceedance probability reflects the temporal probability. Based on the coupling of them, dynamic landslide hazard mapping further considers the landslide deformation intensity at different times. Zigui, a highly vegetation-covered area, was taken as the study area. The landslide displacement monitoring effect of different band SAR datasets (ALOS-2, Sentinel-1A) and different interpretation methods (D-InSAR, PS-InSAR, SBAS-InSAR) were studied to explore a combined application method. The deformation interpreted by SBAS-InSAR was taken as the main part, PS-InSAR data were used in towns and villages, and D-InSAR was used for the rest. Based on the preliminary evaluation and the displacement interpreted by fusion InSAR, the dynamic landslide hazard mappings of the study area from 2019 to 2021 were finished. Compared with the preliminary evaluation, the dynamic mapping approach was more focused and accurate in predicting the deformation of landslides. The false positives in very-high-hazard zones were reduced by 97.8%, 60.4%, and 89.3%. Dynamic landslide hazard mapping can summarize the development of and change in landslides very well, especially in highly vegetated areas. Additionally, it can provide trend prediction for landslide early warning and provide a reference for landslide risk management.

Landslide susceptibility zonation mapping using geospatial technologies and multi criteria evaluation techniques in the upper Didessa sub-basin, Southwest Ethiopia

ABSTRACT Landslides have a profound impact on landscape geology, resulting in extensive devastation and loss of human lives. Mapping landslide susceptibility is crucial for effective land use planning in mountainous country like Ethiopia. This study was conducted in the upper Didessa sub-basin, southwestern parts of Ethiopia using Geographic Information System (GIS) and multi criteria evaluation (MCE) technique. This study employed a blend of primary data, encompassing field surveys and interviews with experts, as well as secondary data derived from diverse source, such as remote sensing data, digital soil maps, and geological maps. A total of eleven critical factors were employed to assess the triggers of landslides. These factors include slope, aspect, drainage density, topographic wetness index (TWI), stream power index (SPI), topographic ruggedness index (TRI), hypsometric integral, lithology, land use land cover (LULC), soil texture, and distance from roads. The analytical hierarchy process (AHP) method was used to determine the significance of each indicator through pairwise comparison matrix. The study area was categorized into different zones based on the susceptibility to landslides, namely very high, high, moderate, low, and very low. Results revealed that cultivated land had the highest likelihood of experiencing landslides, with a total of nine incidents out of 25, followed by built-up areas with seven landslides. Conversely, dense forests, sparse forests, and grazing land experienced a lower likelihood of landslides. Out of the 11 factors contributing to landslides, 24% of the surveyed region was deemed to have a moderate susceptibility, with 12% and 6% falling into the categories of high and very high susceptibility to landslides, respectively. The findings of this research provide important information for policymakers to develop efficient measures for preventing and reducing the risks of landslides.

Landslide Risk Assessments through Multicriteria Analysis

Natural risks comprise a whole range of disasters and dangers, requiring comprehensive management through advanced assessment, forecasting, and warning systems. Our specific focus is on landslides in difficult terrains. The evaluation of landslide risks employs sophisticated multicriteria models, such as the weighted sum GIS approach, which integrates qualitative parameters. Despite the challenges posed by the rugged terrain in Northern Algeria, it is paradoxically home to a dense population attracted by valuable hydro-agricultural resources. The goal of our research is to study landslide risks in these areas, particularly in the Mila region, with the aim of constructing a mathematical model that integrates both hazard and vulnerability considerations. This complex process identifies threats and their determining factors, including geomorphology and socio-economic conditions. We developed two algorithms, the analytic hierarchy process (AHP) and the fuzzy analytic hierarchy process (FAHP), to prioritize criteria and sub-criteria by assigning weights to them, aiming to find the optimal solution. By integrating multi-source data, including satellite images and in situ measurements, into a GIS and applying the two algorithms, we successfully generated landslide susceptibility maps. The FAHP method demonstrated a higher capacity to manage uncertainty and specialist assessment errors. Finally, a comparison between the developed risk map and the observed risk inventory map revealed a strong correlation between the thematic datasets.

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.

Near-surface soil hydrothermal response feedbacks landslide activity and mechanism

Surface moisture has recently been reported to be used in regional-scale landslide early warning. Nevertheless, near-surface multi-depth hydrothermal measurements as a hillslope scale are often less concerned and rarely linked to landslide kinematics. In this paper, we selected two neighboring landslides with different deformation mechanisms as case studies. Using in-situ multi-source sensors, we monitored real-time soil temperature and moisture at specific depths within approximately 1.5 m. The measurements span two complete monsoon seasons, representing concurrent dry and wet hydrological extremes. Statistical Pearson correlation analysis was employed to quantify the relationships between landslide activity and environmental variables such as soil temperature and moisture content. The results indicate that the near-surface soil temperatures and moisture contents contribute to a better understanding of the factors controlling landslide activity, in which variations synergistically reflect hydrothermal interaction and potential deformation mechanisms. These soil temperatures and moisture contents at certain depths (specifically at 20, 50, and even 100 cm) show moderate to strong correlations (with Pearson correlation coefficient values ranging from 0.4 to 0.8) with landslide deformation. In cases where discrete daily rainfall data exhibited unsatisfactory correlations due to their data attributes, soil temperature and moisture effectively served as alternative indicators for rainfall inputs, aiding in the analysis. Overall, this work emphasizes the critical influence of soil moisture and temperature on landslide dynamics. This study also highlights the need for comprehensive monitoring and forecasting strategies that consider a wide range of environmental factors to mitigate landslide risks associated with climate change, particularly in the context of intensified extreme weather events.

Ensemble Predictions of Rainfall-Induced Landslide Risk under Climate Change in China Integrating Antecedent Soil-Wetness Factors

Ensemble Predictions of Rainfall-Induced Landslide Risk under Climate Change in China Integrating Antecedent Soil-Wetness Factors

Considering the effect of non-landslide sample selection on landslide susceptibility assessment

Crafting landslide susceptibility mapping is pivotal for the effective management of landslide risks. However, the influence of non-landslide sample selection on the modeling performance of landslide susceptibility assessment models remains a crucial challenge to overcome. This article employs Huize County as the research area and identifies 12 factors that exert influence. In this study, we utilized Extreme Gradient Boosting and Random Forest algorithms, and four methods (Whole-area random selection method, Buffer method, Frequency Ratio method, and Analysis Hierarchy Process) were employed to select non-landslide samples for constructing the landslide susceptibility assessment model. The findings revealed that the model performance derived from different non-landslide sample selections exhibited significant variations, and the models obtained using the buffer zone method, frequency ratio method, and AHP method performed better than the full-area random selection model. Among the evaluated models, the AHP method for non-landslide sample selection demonstrated the most optimal performance, with an AUC of 92.17% for XGBoost-AHP and 91.64% for RF-AHP. Based on the SHapley Additive explanation (SHAP), the main variables impacting landslide danger in the research area were elevation, NDVI, and peak seismic acceleration. This study provides theoretical support for the selection of non-landslide samples in landslide susceptibility assessments and interpretable AI research.

Assessing landslide susceptibility in Chefchaouen, Morocco: An application of the landslide numerical risk factor method for sustainable urban development and disaster risk management

This research investigates landslide susceptibility in Chefchaouen, Morocco, using the Landslide Numerical Risk Factor (LNRF) method to inform sustainable urban development and disaster risk management. The study incorporates local factors such as geological characteristics, climatic conditions, land use patterns, and gravitational influences within the LNRF framework. The results demonstrate that slope, altitude, geological complexity, and precipitation are key determinants of landslide susceptibility, with complex geology and high rainfall significantly increasing risk. The research highlights the critical need for forest conservation, reforestation, and responsible land management to mitigate the heightened landslide vulnerability caused by human activities such as deforestation, overexploitation, and urban expansion. The study provides valuable insights for enhancing regional planning and natural resource management to reduce landslide risks effectively.

Application of bivariate statistical models to explore landslide susceptibility and risk in the Lish River basin of Darjeeling Himalaya

Application of bivariate statistical models to explore landslide susceptibility and risk in the Lish River basin of Darjeeling Himalaya

Design and construction of tunnels and tunnelling: Understanding the importance of geological conditions, landslide susceptibility and risk assessment

Tunnel engineering is a complex and multidisciplinary field that requires the integration of geological expertise, advanced modeling techniques, and practical engineering solutions. The research compiled in the Special Issue "Tunnels and Tunneling" makes significant contributions to the field by addressing the diverse geological conditions and intricate challenges inherent in tunnel construction. These insights are crucial for enhancing the safety, efficiency, and sustainability of tunnel projects worldwide. The studies in this Special Issue provide a comprehensive understanding of the various challenges and innovative solutions in tunnel engineering. They offer valuable insights and practical guidelines for designing, constructing, and maintaining safe and stable tunnel structures across different geological settings. In addition, geological challenges in specific regions, such as the Three Gorges Reservoir area, the Hengduan Mountains, and the Tibetan Plateau, require tailored approaches. A key theme in many of the comparative studies is the importance of accurate risk assessment to ensure tunnel safety. In regions prone to geological hazards, landslide susceptibility mapping and risk assessment are critical. Innovative approaches, such as machine learning models, are highlighted for their potential to predict and manage landslide risks effectively.

Disturbing the dead: Climate change and the potential relocation of Swedish cemeteries

This study focuses on the contradiction of mobility and immobility in relation to interred human remains. While society generally embraces human mobility, once individuals pass away and are disposed of, mobility ceases to be the norm. Some counties, like Sweden, has a rigid burial legislation that prohibits the re-location of interred human remains. However, both society- and climate-related events are increasingly affecting the eternal rest of the deceased. Utilizing quantitative data on all cemeteries in Sweden, this study aims to assess and analyse the potential magnitude of future large-scale moves of human remains due to expected impacts of climate change, and to put this into a relational context of norms and laws. Results show that climate change poses an apparent risk to cemeteries in Sweden, especially in the south-west of the country, and that this is mainly caused by increased risk of landslides and erosion, as a result of expected increased precipitation. A low estimate state that by the end of this century, some 30.000 interred remains and some 146.000 living survivors will likely be affected by climate-related risks.

Analysis of the timing of evacuation and associated factors among home health care patients during flooding: A single-clinic-based mixed methods study

Home health care patients (HHPs) are vulnerable to flooding and may face evacuation difficulties. Previous studies reported that HHPs and their families are more susceptible to disasters than non-HHPs. Our prior research revealed that many HHPs in Fukui, Japan, were at risk for flooding and landslides, requiring evacuation assistance, yet few were aware of the exact risks. This study analyzed survey data to identify barriers to evacuation during floods and assess the assistance needed among HHPs. A concurrent nested design of mixed-method research was employed. We calculated the percentage of the 87 HHPs at risk likely to delay evacuation and constructed univariable and multivariable logistic regression models to identify factors associated with delayed evacuation, and employed a thematic analysis to identify concerns and needs during evacuation. Family members responded to the questionnaire for patients unable to respond. The study found that 69.0 % of patients delayed evacuation, with 61.7 % incorrectly perceiving disaster risks and 20.0 % considering evacuation unimportant. Those with incorrect risk perceptions were slower to evacuate than those with accurate perceptions (OR: 1.35, 95 % confidence interval: 1.06–6.99, p-value: 0.036). Seven themes about flood preparedness needs were extracted from open-ended statements. Factors contributing to delayed evacuation included assumptions of vertical evacuation due to mobility challenges and concerns about evacuation site environments. Few Japanese HHPs at risk of flooding and landslides would plan an early evacuation, often due to misperceived disaster risks. Specific measures are needed to address these issues, and further investigation is required as flooding may also affect patient supporters.

Study of Earthquake Landslide Hazard by Defining Potential Landslide Thickness Using Excess Topography: A Case Study of the 2014 Ludian Earthquake Area, China

Influenced by the combined effects of crustal uplift and river downcutting, rivers with significant potential energy are often found in high mountain and canyon areas. Due to the active tectonic movements that these areas have experienced or are currently experiencing, geological hazards frequently occur on the mountains flanking the rivers. Therefore, evaluating the susceptibility and risk of earthquake landslides in river segments of these high mountain and canyon areas is of great importance for disaster prevention and mitigation, as well as for the safe construction and operation of hydropower stations. Currently, a major challenge in the study of landslide susceptibility and hazard is determining the thickness of potential landslide bodies. The presence of excess topography reflects the instability of the disrupted slopes, which is also a fundamental cause of landslides. This study takes the example of the Ludian earthquake in 2014, focusing on the IX and VIII intensity zones, to extract the excess topography in the study area and analyze its correlation with seismic landslides. The correlation between the critical acceleration value and the excess topography was validated using the Spearman’s rank correlation coefficient, resulting in a correlation coefficient of −0.771. This indicates a strong negative correlation between the excess topography and critical acceleration, with significant relevance. The landslide susceptibility distribution obtained by setting the potential landslide thickness based on the excess topography and proportion coefficient showed an ROC curve analysis AUC value of 0.829. This is higher than the AUC value of 0.755 for the landslide susceptibility result using a uniform potential landslide thickness of 3 m, indicating the higher model evaluation accuracy of this approach. Earthquake landslide hazard predictions for rapid post-earthquake assessments and earthquake landslide hazard zoning for pre-earthquake planning were made using actual seismic ground motion and a 2% exceedance probability in 50 years, respectively. Comparing these with the 10,559 coseismic landslides triggered by the Ludian earthquake and evaluating the seismic landslide development rate, the results were found to be consistent with reality. The improved model better reflects the control of excess topography and rock mechanics properties on the development of earthquake landslide hazards on high steep slopes. Identifying high-risk seismic landslide areas through this method and taking corresponding preventive and protective measures can help plan and construct safer hydropower and other infrastructure, thereby enhancing their disaster resistance.