A case study on soil slope landslide failure and parameter analysis of influencing factors for safety factor based on strength reduction method and orthogonal experimental design

In civil engineering, stability analysis of slope is one of the main content of design. By using the finite element limit analysis software OptumG2, a landslide geological model is established to simulate the failure process of the landslide in Huadu District, Guangzhou City, China. The analysis focused on the deformation and failure characteristics, as well as the mechanical mechanism of landslide; the landslide mode of homogeneous soil is circular sliding. Additionally, investigating the influencing factors affecting slope stability is crucial in engineering implementation; in which the five influencing factors are considered as follow: slope height, slope gradient, soil cohesion, soil internal friction angle, and soil unit weight, respectively. A stability calculation model for a soil slope is established under 25 working conditions based on strength reduction method and orthogonal experimental design, in which the relationship between the safety factor and slope height, slope gradient, soil cohesion, soil internal friction angle, and soil unit weight is obtained. As the slope height increases from 5m to 45m, the safety factor of soil slope gradually decreases from 2.21 to 0.94; As the slope gradient increases from 20° to 60°, the safety factor of soil slope decreases approximately linearly from 1.80 to 0.95; As the cohesion of soil increases from 10kpa to 30kpa, the safety factor of soil slope increases approximately linearly from 1.04 to 1.60; As the internal friction angle of soil increases from 10° to 30°, the safety factor of soil slope increases approximately linearly from 1.00 to 1.81; As the unit weight of soil increases from 13kN/m3 to 21kN/m3, the safety factor of soil slope decreases approximately linearly from 1.50 to 1.21. The influencing factors affecting the safety factor of soil slope in descending order are slope height, slope angle, soil internal friction angle, soil cohesion, and soil unit weight. The research has reference significance for studying the stability and failure laws of soil slopes and conducting landslide control on soil slopes.

Engineering and environmental assessment of soilbag-based slope stabilisation for sustainable landslide mitigation in mountainous area

Changes in land use significantly impact landslide occurrence, particularly in mountainous areas in northern Thailand, where human activities such as urbanization, deforestation, and slope modifications alter natural slope angles, increasing susceptibility to landslides. To address this issue, an appropriate method using soilbags has been widely used for slope stabilisation in northern Thailand, but their effectiveness and sustainability require assessment. This research highlights the need to evaluate the stability of the soilbag-based method. In this study, a case study was conducted in northern Thailand, focusing on an area characterised by high-risk landslide potential. This research focuses on numerical evaluation the slope stability of soilbag-reinforced structures and discusses environmental sustainability. The study includes site investigations using an unmanned aerial photogrammetric survey for slope geometry evaluation and employing the microtremor survey technique for subsurface investigation. Soil and soilbag material parameters are obtained from existing literatures. Modelling incorporates hydrological data, slope geometry, subsurface conditions, and material parameters. Afterwards, the pore-water pressure results and safety factors are analysed. Finally, the sustainability of soilbags is discussed based on the Sustainable Development Goals (SDGs). The results demonstrate that soilbags effectively mitigate pore-water pressures, improve stability, and align with several SDGs objectives. This study enhances understanding of soilbags in slope stabilisation and introduces a sustainable landslide mitigation approach for landslide-prone regions.

Effect of freeze‒thaw cycles on root-Soil composite mechanical properties and slope stability

Natural disasters such as landslides often occur on soil slopes in seasonally frozen areas that undergo freeze‒thaw cycling. Ecological slope protection is an effective way to prevent such disasters. To explore the change in the mechanical properties of soil under the influence of both root reinforcement and freeze‒thaw cycles and its influence on slope stability, the Baijiabao landslide in the Three Gorges Reservoir area was taken as an example. The mechanical properties of soil under different confining pressures, vegetation coverages (VCs) and numbers of freeze‒thaw cycles were studied via mechanical tests, such as triaxial compression tests, wave velocity tests and FLAC3D simulations. The results show that the shear strength of a root-soil composite increases with increasing confining pressure and VC and decreases with increasing number of freeze‒thaw cycles. Bermuda grass roots and confining pressure jointly improve the durability of soil under freeze‒thaw conditions. However, with an increase in the number of freeze‒thaw cycles, the resistance of root reinforcement to freeze‒thaw action gradually decreases. The observed effect of freeze‒thaw cycles on soil degradation was divided into three stages: a significant decrease in strength, a slight decrease in strength and strength stability. Freeze‒thaw cycles and VC mainly affect the cohesion of the soil and have little effect on the internal friction angle. Compared with that of a bare soil slope, the safety factor of a slope covered with plants is larger, the maximum displacement of a landslide is smaller, and it is less affected by freezing and thawing. These findings can provide a reference for research on ecological slope protection technology.

Game-theoretic optimization of landslide susceptibility mapping: a comparative study between Bayesian-optimized basic neural network and new generation neural network models

Landslide susceptibility mapping is essential for reducing the risk of landslides and ensuring the safety of people and infrastructure in landslide-prone areas. However, little research has been done on the development of well-optimized Elman neural networks (ENN), deep neural networks (DNN), and artificial neural networks (ANN) for robust landslide susceptibility mapping (LSM). Additionally, there is a research gap regarding the use of Bayesian optimization and the derivation of SHapley Additive exPlanations (SHAP) values from optimized models. Therefore, this study aims to optimize DNN, ENN, and ANN models using Bayesian optimization for landslide susceptibility mapping and derive SHAP values from these optimized models. The LSM models have been validated using the receiver operating characteristics curve, confusion matrix, and other twelve error matrices. The study used six machine learning-based feature selection techniques to identify the most important variables for predicting landslide susceptibility. The decision tree, random forest, and bagging feature selection models showed that slope, elevation, DFR, annual rainfall, LD, DD, RD, and LULC are influential variables, while geology and soil texture have less influence. The DNN model outperformed the other two models, covering 7839.54 km2 under the very low landslide susceptibility zone and 3613.44 km2 under the very high landslide susceptibility zone. The DNN model is better suited for generating landslide susceptibility maps, as it can classify areas with higher accuracy. The model identified several key factors that contribute to the initiation of landslides, including high elevation, built-up and agricultural land use, less vegetation, aspect (north and northwest), soil depth less than 140 cm, high rainfall, high lineament density, and a low distance from roads. The study's findings can help stakeholders make informed decisions to reduce the risk of landslides and ensure the safety of people and infrastructure in landslide-prone areas.

Good heavens! Finally a landslide analysis of basal ganglia circuitry in teleosts

Tanimoto et al.1 report essential information on teleostean basal ganglia circuitry. This analysis opens gateways into studying neurophysiology, neuropharmacology, and behavior in zebrafish, guided by this complex functional neural system common to all vertebrates.

Slope stability assessment in the seismically and landslide-prone road segment of Gerese to Belta, Rift Valley, Ethiopia

Slope instability on several sections of the Gerese-Belta route in Southern Ethiopia poses a major risk to infrastructure and safety. This research was aimed at evaluating certain areas of the road susceptible to slope instability. Through intensive fieldwork including geological analysis, surveys, and testing, three crucial slope portions were determined. Both limit equilibrium and finite element calculations demonstrated that these sections are problematic under different circumstances. The slope modification analysis shows that the safety factor increases as bench widths and the number of benches increase. In the slope section D1S3, this factor reached 1.222 when two benches measuring 5 meters in width were used on slide 2D. This initially showed an unstable safety factor of 0.26. Three benches of the same width were used under slide 2D. This resulted in a safety factor of 1.219. At the slope section (D1S2), flattening of the slope angle from initial 45⁰ to 35⁰, 28⁰, 25⁰ and 18⁰ increases the factor of safety of the slope from initial 0.284 to 0.77, 0.89, 1.022, and 1.151 respectively under slide 2D analysis. At the slope section (D2S1), flattening the slope angle from initial 46⁰ to 35⁰, 25⁰, 23⁰, and 20⁰ increases the safety factor from initial 0.412 to 0.684, 0.920, 1.02, and 1.315 respectively. Based on the analysis of the study results, it can be concluded that the identified slope sections are susceptible to failure under actual field scenarios, depending on the conditions under which they are predicted to occur. According to this study, the Benching method is an economical method for mitigating soil slopes, as a result of which it was recommended to be used.

Investigating the dynamic nature of landslide susceptibility in the Indian Himalayan region

Landslide susceptibility zonation (LSZ) mapping is used to delineate areas prone to landslides and is critical for effective landslide hazard management. The existing methodologies for generating such maps tend to neglect the influence of dynamic environmental variables on landslide occurrences, which may lead to obsolete and erroneous estimates of landslide susceptibility (LS) for a concerned area. Although recent studies have started to report the effects of Land Use/ Land Cover (LULC) variation on LSZ mapping, variations in other dynamic variables like rainfall, soil moisture, and evapotranspiration apart from LULC may also influence slope stability in mountainous regions. The present study investigates the impact of variations in these four variables on the LS distribution, of a selected Indian Himalayan region between 2017 and 2021. Random Forest (RF) susceptibility models are utilized for evaluating the LS for the selected years and geospatial technologies are employed for LS change detection. The results indicate up to 19% variations in the spatial extent for some of the zones of the generated LSZ maps. The research findings of this study are crucial since they reveal the impact of dynamic behavior on LS, which has not been previously documented in the literature.

Analysis of the spatial distribution of the landslides triggered by the 1923 Great Kanto Earthquake, Japan

The Great Kanto Earthquake that occurred in the southern part of Kanto district, Japan, on September 1, 1923, was reported to have triggered numerous landslides (over 89,080 slope failures over an area of 86.32 km2). This study investigated the relationship between the landslide occurrence caused by this earthquake and geomorphology, geology, soil, seismic ground motion, and coseismic deformation. We found that a higher landslide density was mainly related to a larger absolute curvature and a higher slope angle, as well as to several geological units (Neogene plutonic rock, accretionary prism, and metamorphic rocks). Moreover, we performed decision tree analyses, which showed that slope angle, geology, and coseismic deformation were correlated to landslide density in that order. However, no clear correlation was found between landslide density and seismic ground motion. These results suggest that landslide density was greater in areas of large slope angle or fragile geology in the area with strong shaking enough to trigger landslides.

Exploring machine learning and statistical approach techniques for landslide susceptibility mapping in Siwalik Himalayan Region using geospatial technology

Landslides are a natural threat that poses a severe risk to human life and the environment. In the Kumaon mountains region in Uttarakhand (India), Nainital is among the most vulnerable areas prone to landslides inflicting harm to livelihood and civilization due to frequent landslides. Developing a landslide susceptibility map (LSM) in this Nainital area will help alleviate the probability of landslide occurrence. GIS and statistical-based approaches like the certainty factor (CF), information value (IV), frequency ratio (FR) and logistic regression (LR) are used for the assessment of LSM. The landslide inventories were prepared using topography, satellite imagery, lithology, slope, aspect, curvature, soil, land use and land cover, geomorphology, drainage density and lineament density to construct the geodatabase of the elements affecting landslides. Furthermore, the receiver operating characteristic (ROC) curve was used to check the accuracy of the predicting model. The results for the area under the curves (AUCs) were 87.8% for logistic regression, 87.6% for certainty factor, 87.4% for information value and 84.8% for frequency ratio, which indicates satisfactory accuracy in landslide susceptibility mapping. The present study perfectly combines GIS and statistical approaches for mapping landslide susceptibility zonation. Regional land use planners and natural disaster management will benefit from the proposed framework for landslide susceptibility maps.

CAS Landslide Dataset: A Large-Scale and Multisensor Dataset for Deep Learning-Based Landslide Detection

In this work, we present the CAS Landslide Dataset, a large-scale and multisensor dataset for deep learning-based landslide detection, developed by the Artificial Intelligence Group at the Institute of Mountain Hazards and Environment, Chinese Academy of Sciences (CAS). The dataset aims to address the challenges encountered in landslide recognition. With the increase in landslide occurrences due to climate change and earthquakes, there is a growing need for a precise and comprehensive dataset to support fast and efficient landslide recognition. In contrast to existing datasets with dataset size, coverage, sensor type and resolution limitations, the CAS Landslide Dataset comprises 20,865 images, integrating satellite and unmanned aerial vehicle data from nine regions. To ensure reliability and applicability, we establish a robust methodology to evaluate the dataset quality. We propose the use of the Landslide Dataset as a benchmark for the construction of landslide identification models and to facilitate the development of deep learning techniques. Researchers can leverage this dataset to obtain enhanced prediction, monitoring, and analysis capabilities, thereby advancing automated landslide detection.

Evaluating the influence of road construction on landslide susceptibility in Saudi Arabia's mountainous terrain: a Bayesian-optimised deep learning approach with attention mechanism and sensitivity analysis

In the mountainous region of Asir region of Saudi Arabia, road construction activities are closely associated with frequent landslides, posing significant risks to both human life and infrastructural development. This highlights an urgent need for a highly accurate landslide susceptibility map to guide future development and risk mitigation strategies. Therefore, this study aims to (1) develop robust well-optimised deep learning (DL) models for predicting landslide susceptibility and (2) conduct a comprehensive sensitivity analysis to quantify the impact of each parameter influencing landslides. To achieve these aims, three advanced DL models-Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Bayesian-optimised CNN with an attention mechanism-were rigorously trained and validated. Model validation included eight matrices, calibration curves, and Receiver Operating Characteristic (ROC) and Precision-Recall curves. Multicollinearity was examined using Variance Inflation Factor (VIF) to ensure variable independence. Additionally, sensitivity analysis was used to interpret the models and explore the influence of parameters on landslide. Results showed that road networks significantly influenced the areas identified as high-risk zones. Specifically, in the 1-km buffer around roadways, CNN_AM identified 10.42% of the area as 'Very High' susceptibility-more than double the 4.04% indicated by DNN. In the extended 2-km buffer zone around roadways, Bayesian CNN_AM continued to flag a larger area as Very High risk (7.46%), in contrast to DNN's 3.07%. In performance metrics, CNN_AM outshined DNN and regular CNN models, achieving near-perfect scores in Area Under the Curve (AUC), precision-recall, and overall accuracy. Sensitivity analysis highlighted 'Soil Texture', 'Geology', 'Distance to Road', and 'Slope' as crucial for landslide prediction. This research offers a robust, high-accuracy model that emphasises the role of road networks in landslide susceptibility, thereby providing valuable insights for planners and policymakers to proactively mitigate landslide risks in vulnerable zones near existing and future road infrastructure.

Detection and analysis of potential landslides based on SBAS-InSAR technology in alpine canyon region

The Lancang River flows through the alpine canyon region of southwest China, an area that has experienced frequent geological disasters over the years. Early monitoring of geological hazards is essential for disaster prevention and mitigation. However, traditional ground monitoring techniques are limited by the complex terrain conditions in high-altitude valley regions. In contrast, interferometric synthetic aperture radar (InSAR) technology can provide a high-precision, wide-range monitoring of slow rock-slope deformation, making it an effective tool for studying geological hazards. Within the study area, multiple synthetic aperture radar (SAR) images from the Sentinel-1A satellite were collected, and surface deformation was obtained using the small baseline subset InSAR (SBAS-InSAR). The results demonstrate that combining ascending and descending orbit images can be successfully applied to landslide monitoring in complex mountainous areas. Over 30 potential landslides were identified by combining InSAR results with optical images. The Line-Of-Sight (LOS) direction deformation features and their relationship with precipitation were analyzed based on two typical landslides, and two-dimensional/three-dimensional (2D/3D) deformation decomposition was carried out to reveal its motion characteristics. It was found that the cumulative deformation fluctuation amplitude was higher during the rainy season, and the main movement direction of the landslide was east-west. In addition, based on the spatial distribution and statistical analysis of deformation points along with meteorological data, geological elements, human activities, and topographic conditions, it is inferred that factors such as low vegetation coverage, tectonic movements, human activities, and high-altitude glacier thawing may contribute to the occurrence of disasters. And it was found that areas with high vegetation cover, high rainfall, and snow cover exhibit lower coherence coefficients. This study offers valuable insights for investigating large-scale geological in alpine canyon regions.

Development of landslide susceptibility maps of Tripura, India using GIS and analytical hierarchy process (AHP)

Landslides are one of the most extensive and destructive geological hazards on the globe. Tripura, a northeastern hilly state of India experiences landslides almost every year during monsoon season causing casualties and huge economic losses. Hence, it is required to assess the landslide susceptibility of the area that would support short- and long-term planning and mitigation. The analytic hierarchy process (AHP) integrated with geospatial technology has been adopted for landslide susceptibility mapping in the state. Eight influencing factors such as slope, lithology, drainage density, rainfall, land use land cover, distance from rivers and roads, and soil type were selected to map the landslide susceptibility. Landslide susceptibility index (LSI) was found to vary from 6.205 during monsoon to 1.427 during post-monsoon season. The LSI values were classified into very high, high, moderate, low, and very low susceptibility. Landslide susceptibility maps for three different seasons, namely, pre-monsoon, monsoon, and post-monsoon, were prepared. The study showed that most of the areas of the state come under very low to moderate landslide susceptibility zones. Around 73.2% area of the state is found to be under low landslide-susceptible zones during the pre-monsoon season, around 62% area is prone to landslides with moderate susceptibility during the monsoon season, and 68.5% area comes under landslides with low susceptibility zones during the post-monsoon season. The results of this study may be referred to the engineers and planners for the assessment, control, and mitigation of landslides and the development of basic infrastructure in the state.

Research on landslide susceptibility prediction model based on LSTM-RF-MDBN

The occurrence of landslide disasters causes huge economic losses and casualties. Although many achievements have been made in predicting the probability of landslide disasters, various factors such as the scale and spatial location of landslide geological disasters should still be fully considered. Further research on how to quantitatively characterize the susceptibility of landslide geological disasters is necessarily important. To this end, taking the Wenchuan earthquake as the research area and extracting eight influencing factors, including terrain information entropy (Ht), lithology, distance from rivers, distance from faults, vegetation coverage (NDVI), distance from roads, peak ground motion acceleration (PGA), and annual rainfall, a landslide susceptibility prediction model was hereby established based on LSTM-RF-MDBN, a landslide susceptibility prediction map was drawn, and the spatial distribution characteristics of landslide disasters were analyzed. The results showed that (1) LSTM had good prediction results for the eight influencing factors, with an average prediction accuracy of 85%; (2) compared with models such as DNN and LR for predicting landslide disaster points, the AUC value of RF for predicting landslide point positions reached 0.88, presenting a higher accuracy compared to other models; (3) the AUC value of the landslide susceptibility prediction model based on LSTM-RF-MDBN reached 0.965, which had a high accuracy in predicting landslide susceptibility. Overall, the research results can provide a scientific basis for selecting the best strategy for landslide disaster warning, prevention, and mitigation.

An ensemble learning-based experimental framework for smart landslide detection, monitoring, prediction, and warning in IoT-cloud environment

Landslides occur every year during the monsoon season in hilly areas. This natural disaster annually leads to several fatalities, injuries, and property destruction. Monitoring landslides and promptly alerting people to looming disasters in light of these injuries and fatalities are crucial. To date, no efficient technique is in practice to predict landslides. The tools that are now available monitor landslides at a very high cost and do not offer early warning or forecasts of soil movement. An innovative, low-cost Internet of Things (IoT)-based system for landslip warning, monitoring, and prediction is the major objective of this research. Its assessment, implementation, and development are described in detail. This study proposes an IoT-based smart landslide detection, warning, prediction, and monitoring system. The pre and post-measures use sensors and other hardware to deal with landslide disasters. It uses real-time environment monitoring (landslide site) for any changes and provides appropriate output by comparing the threshold values. The proposed system is tested on a prototype model, which performed well in our tests. The database was updated 2.5 s after the landslide thanks to a steady Internet connection. In less than 5 s after the event, the Thingspeak channel can display a graphical depiction of the data and its position. Multiple readings showed an 80-85% system accuracy rate. Further, the proposed ensemble learning-based risk prediction model is applied to static and dynamic data to predict the landslide for future reference. The ensemble classifier model has 98.67% recall, 96.56% accuracy, 97.35% F1-value, and 96.07% precision. The alert SMS is also sent to concerned authorities for medical emergency/PWD department/district administration.

A novel evolutionary combination of artificial intelligence algorithm and machine learning for landslide susceptibility mapping in the west of Iran

Detecting and mapping landslides are crucial for effective risk management and planning. With the great progress achieved in applying optimized and hybrid methods, it is necessary to use them to increase the accuracy of landslide susceptibility maps. Therefore, this research aims to compare the accuracy of the novel evolutionary methods of landslide susceptibility mapping. To achieve this, a unique method that integrates two techniques from Machine Learning and Neural Networks with novel geomorphological indices is used to calculate the landslide susceptibility index (LSI). The study was conducted in western Azerbaijan, Iran, where landslides are frequent. Sixteen geology, environment, and geomorphology factors were evaluated, and 160 landslide events were analyzed, with a 30:70 ratio of testing to training data. Four Support Vector Machine (SVM) algorithms and Artificial Neural Network (ANN)-MLP were tested. The study outcomes reveal that utilizing the algorithms mentioned above results in over 80% of the study area being highly sensitive to large-scale movement events. Our analysis shows that the geological parameters, slope, elevation, and rainfall all play a significant role in the occurrence of landslides in this study area. These factors obtained 100%, 75.7%, 68%, and 66.3%, respectively. The predictive performance accuracy of the models, including SVM, ANN, and ROC algorithms, was evaluated using the test and train data. The AUC for ANN and each machine learning algorithm (Simple, Kernel, Kernel Gaussian, and Kernel Sigmoid) was 0.87% and 1, respectively. The Classification Matrix algorithm and Sensitivity, Accuracy, and Specificity variables were used to assess the models' efficacy for prediction purposes. Results indicate that machine learning algorithms are more effective than other methods for evaluating areas' sensitivity to landslide hazards. The Simple SVM and Kernel Sigmoid algorithms performed well, with a performance score of one, indicating high accuracy in predicting landslide-prone areas.

Study of substrata of a slope susceptible to landslide in hilly environment using a geophysical method in the Nilgiris, India

Landslides are one of the prevailing threats to life that cause huge loss to the environment. Around 3.7 million km2 of the area is exposed to landslides globally, and 820,000 km2 is at high risk for landslides in India. Rainfall and earthquakes are the two primary landslide-causing variables in India. The Nilgiris district which is in the south-western part of India is more prone to rainfall-induced landslides. This study intends to calculate the depth of the slip surface on a slope (Lovedale area, the Nilgiris) in the event of a future landslide using Multichannel Analysis of Surface Waves (MASW) and validate using bore log data. During November 2009 rainfall, a shallow landslide occurred at the toe of this slope. There is a greater chance that a landslide will occur again in the event of rainfall in the future. To comprehend how the sub-strata vary, and to forecast the depth of a prospective failure surface, the shear wave velocity (Vs) obtained from MASW proved beneficial. Slip surfaces, one at a shallow depth and another at a deeper depth, were found based on the shear wave velocity and bore log data. The importance of the MASW output in the engineering properties of soil was also studied. The compressional velocity (Vp) and shear wave velocity obtained from MASW were evaluated for their applicability in calculating the elastic moduli of soil. It was established that shear wave velocity was of greater significance than compressional velocity. The MASW results can be further used as a preliminary data for analysing the stability of the slope, reactivation of landslides, and landslide early warning system.

Determination of landslide susceptibility with Analytic Hierarchy Process (AHP) and the role of forest ecosystem services on landslide susceptibility

The analysis of landslide susceptibility is a crucial tool in the mitigation and management of ecological and economic hazards. The number of studies examining how the form and durability of forest areas affect landslide susceptibility is very limited. This study was conducted in the Marmara region of northwestern Türkiye, where forested areas and industrial zones are intertwined and dense. The landslide susceptibility map was produced by Analytic Hierarchy Process (AHP) method. In the context of AHP, a total of 12 different variables were employed, namely lithology, slope, curvatures, precipitations, aspect, distance to fault lines, distance to streams, distance to roads, land use, soil, elevation, and Normalized Difference Vegetation Index (NDVI). The performance analysis of the landslide susceptibility map was conducted using the Receiver Operating Characteristics (ROC) curve method. The AUC value was computed (0.809) for the landslide susceptibility map generated by using the AHP technique. Forest type maps were used to analyze the impact of forests on landslide susceptibility. In terms of forest structure, 4 main criteria were determined: stand structure, development stage, crown closure, and stand age. Each criterion was analyzed with Geographic Information Systems (GIS) by overlaying it with the landslide susceptibility map of the study area. The results showed that the risk of landslides was lowest in forests with more than one tree species, mature, development stage and of (e) > 52 cm, and crown closure of 41%-70% (2).

Integrating stratified best-worst method and GIS for landslide susceptibility assessment: a case study in Erzurum province (Turkey)

Landslides are among the most destructive geological disasters that seriously damage human life and infrastructures. Landslides mainly occur in mountainous regions around the world. One of the key processes to reduce these damages is to uncover landslide-exposed areas through different data-driven methods such as Geographical Information System (GIS) and multi-criteria decision-making (MCDM). In the literature, there are many studies developed with these fundamental tools. In this study, unlike the literature, a new landslide susceptibility assessment model is proposed by integrating GIS with the stratified best-worst method (S-BWM). This model has four main dimensions and 16 sub-dimensions under topography, environment-land, location, and hydrological factors, weighted with the S-BWM. A network was created considering the different states that may arise in the importance weights of these dimensions in the future. The transition probabilities of these states were predicted and injected into the classical BWM. Then, maps were created for these dimensions and classifications for each sub-dimension according to the map characteristics. Finally, the most susceptive landslide locations were determined with GIS-based calculations. To demonstrate the model's applicability, a case study was conducted for the Erzurum region, one of Turkey's landslide-prone regions. In addition, besides the landslide map, an analysis and discussion about the spatial distribution of susceptibility classes was presented, contributing to the study's robustness. In the results of landslide susceptibility analysis, landslides are higher in the range of about 1600-2500 m. Approximately 42% (35.59 sq. km) of the study area has high landslide susceptibility, while 58% (64.41 sq. km) has medium and low landslide susceptibility.

Study on the control effect of local basement replacement on the stability of dump

This study focuses on effectively controlling landslides at the boundary of a soft rock open-pit dump while ensuring safe increases in the dump's capacity and optimal utilization of external dump sites. To achieve this, the adoption of a local filling method for the dump base is proposed. By leveraging the concepts of limit equilibrium theory and equivalent shear strength parameters, the mathematical expression of the slope stability coefficient in the Morgenstern-Price method is derived and improved. This improved method is then applied to a real engineering example to determine the optimal basement replacement rate required to maintain slope stability. The findings reveal that the local filling of the base is well-suited for slopes susceptible to potential landslides associated with cutting layers, bedding layers, and swelling. For practical ease, it is advisable to choose the lowest step in the dump's slope for construction convenience. As the local replacement rate of the base increases, the slope's stability coefficient gradually improves, with the K-Fs ratio showing a prominent role in this process. Additionally, numerical simulation methods are employed to elucidate the mechanism of the dump's landslide following local basement replacement, thereby providing comprehensive evidence of the engineering applicability of this method. The research results demonstrate a promising practical application prospect for effectively controlling the stability of soft base dump slopes.

Geological challenges and stabilization strategies for phyllite rock slopes: a case study of Guang-Gansu expressway in Western China

The increased occurrence and severity of natural disasters, such as landslides, have impacted the stability of phyllite rock slopes in the complex geological regions of Western China. This situation presents significant challenges for infrastructure development in the area. This study investigates the upper span bridgehead slope of Guang-Gansu expressway K550 + 031 as a case study to analyze the sliding failure mechanism of thousand rock slopes in the seismic fault zone and the supporting structure failure through field investigation and exploration. The analysis shows that the slope's rock mass is extensively fractured, primarily influenced by the Qingchuan fault zone. This geological activity leads to slope instability, worsened by seasonal rainfall. The phyllite undergoes alternating dry and wet cycles, weakening its mechanical strength, forming cracks, and accelerating slope displacement, subsidence, and cracking. This results in front slope instability, followed by gradual backward and step-by-step traction sliding deformation on both sides. The geological structure and seasonal rainfall damage the original bolt-grid beam-supporting structure. To address this issue, an anti-slide pile combined with a grid beam treatment method is proposed, and its effectiveness is verified through deep displacement monitoring. This study emphasizes the significance of integrating geological structure and seasonal rainfall impacts into infrastructure design within complex geological areas, ensuring slope and supporting structure stability.

Risk assessment of chemical release accident triggered by landslide using Bayesian network

This study evaluated the risk of 461,260,800 scenarios of chemical release accidents triggered by landslides. Several industrial accidents triggered by landslides have recently occurred in Japan; however, only a few studies have analyzed the impact of landslide-triggered chemical release accidents on the surrounding areas. Bayesian networks (BNs) have recently been used in the risk assessment of natural hazardtriggered technological accidents (Natech) to quantify uncertainties and develop methods applicable to multiple scenarios. However, the scope of BN-based quantitative risk assessment is limited to the risk assessment of explosions triggered by earthquakes and lightning. We aimed to extend the BN-based risk analysis methodology and evaluate the risk and the effectiveness of the countermeasures for specific facility. A methodology was developed to assess human health risk in the surrounding areas when n-hexane was released and dispersed into the atmosphere due to a landslide. Risk assessment results showed that the societal risk (representing the relationship between frequency and number of people suffering from a particular harm) of the storage tank closest to the slope exceeded the Netherlands' criteria, which are the safest among the criteria in the United Kingdom, Hong Kong, Denmark, and the Netherlands. Limiting the storage rate reduced the probability of one or more fatalities by up to about 40% compared with the no countermeasure case and was a more effective countermeasure than using oil fences and absorbents. Diagnostic analyses quantitatively showed that the distance between the tank and slope was the main contributing factor. The catch basin parameter contributed to the reduction in the variance of the results compared to the storage rate. This finding indicated that physical measures, such as strengthening or deepening the catch basin, are essential for risk reduction. Our methods can be applied to other natural disasters for multiple scenarios by combining it with other models.

Identifying the essential influencing factors of landslide susceptibility models based on hybrid-optimized machine learning with different grid resolutions: a case of Sino-Pakistani Karakorum Highway

This study attempts to explore the essential influencing factors of landslides and explores the effects of different datasets on landslide susceptibility mapping (LSM) at six grid resolutions (i.e., 10 m, 30 m, 300 m, 1000 m, 2000 m, and 3000 m). Firstly, the geospatial dataset of 21 influencing factors was extracted from 1847 historical landslide InSAR (Interferometric Synthetic Aperture Radar) points, which were taken as a sample for the Sino-Pakistani Karakorum Highway. Secondly, Spearman correlation coefficient (SCC), random forest feature selection (RFFS), and their combinations (SCC-RFFS) were selected at different grid resolutions to identify the essential influencing factors from the 21 original factors. A random division into training set (70%) and test set (30%) was performed. Then, the LSM models for the original influencing factors and the selected influencing factors were constructed separately using machine learning models. Finally, the reasonableness of the essential influencing factors was verified by comparing the accuracy of the models under different grid resolutions. The results show that (1) relief degree of land surface (RDLS), SPI, and rainfall have significant effects on landslide occurrence. (2) The primary elements (i.e., RDLS, slop, rainfall) are less affected by the grid resolution, while the secondary elements (TWI) are more affected by the grid resolution. (3) At 30 m, the SCC-RFFS-RF model can get the highest landslide susceptibility model accuracy. The prediction will also provide scientific guidance for the allocation of land resources on a regional and global scale, and minimize the human and economic costs along the highway, while ensuring safe highway operations.

Ensemble models based on radial basis function network for landslide susceptibility mapping

Ensemble learning techniques have shown promise in improving the accuracy of landslide models by combining multiple models to achieve better predictive performance. In this study, several ensemble methods (Dagging, Bagging, and Decorate) and a radial basis function classifier (RBFC) were combined to predict landslide susceptibility in the Trung Khanh district of the Cao Bang Province, Vietnam. The ensemble models were developed using a geospatial database containing 45 historical landslides (1074 points) and thirteen influencing variables characterizing the topography, geology, land use/cover, and human activities of the study area. The performance of the models was evaluated based on the area under the receiver operating characteristic curve (AUC) and several other performance metrics, including positive predictive value (PPV), negative predictive value (NPV), sensitivity (SST), specificity (SPF), accuracy (ACC), and root mean square error (RMSE). The Bagging-RBFC model with PPV = 86%, NPV = 95%, SST = 95%, SPF = 87%, ACC = 91%, RMSE = 0.297, and AUC = 98% was found to be the most accurate model for the prediction of landslide susceptibility, followed by the Dagging-RBFC, Decorate-RBFC, and single RBFC models. The study demonstrates the efficacy of ensemble learning techniques in developing reliable landslide predictive models, which can ultimately save lives and reduce infrastructure damage in landslide-prone regions worldwide.

Multiple reinforcement measures of flysch landslide

This work is mainly intended to investigate the flysch landslide reinforcement measures used in the Smokovac-Mateševo section of the North-South Expressway project in Montenegro. Bentley's Plaxis software is used for a numerical analysis of sliding surface parameters of flysch strata in the limit equilibrium state. This study analyzes the slope safety factor for rreinforcement measures such as rock bolts, retaining walls, anti-sliding piles, slope unloading and bolt anchoring and obtains an optimal combination of reinforcement application for the flysch landslide. The effects of seismic action on complex stress and the discontinuous stress boundary conditions arising from various reinforcement measures on landslide stability are also examined. The measures applied in this paper can be used as a reference for flysch landslide reinforcement or other similar slope engineering measures.

Insights into geospatial heterogeneity of landslide susceptibility based on the SHAP-XGBoost model

The spatial heterogeneity of landslide influencing factors is the main reason for the poor generalizability of the susceptibility evaluation model. This study aimed to construct a comprehensive explanatory framework for landslide susceptibility evaluation models based on the SHAP (SHapley Additive explanation)-XGBoost (eXtreme Gradient Boosting) algorithm, analyze the regional characteristics and spatial heterogeneity of landslide influencing factors, and discuss the heterogeneity of the generalizability of the models under different landscapes. Firstly, we selected different regions in typical mountainous hilly region and constructed a geospatial database containing 12 landslide influencing factors such as elevation, annual average rainfall, slope, lithology, and NDVI through field surveys, satellite images, and a literature review. Subsequently, the landslide susceptibility evaluation model was constructed based on the XGBoost algorithm and spatial database, and the prediction results of the landslide susceptibility evaluation model were explained based on regional topography, geology, and hydrology using the SHAP algorithm. Finally, the model was generalized and applied to regions with both similar and very different topography, geology, meteorology, and vegetation, to explore the spatial heterogeneity of the generalizability of the model. The following conclusions were drawn: the spatial distribution of landslides is heterogeneous and complex, and the contribution of each influencing factor on the occurrence of landslides has obvious regional characteristics and spatial heterogeneity. The generalizability of the landslide susceptibility evaluation model is spatially heterogeneous and has better generalizability to regions with similar regional characteristics. Further explanation of the XGBoost landslide susceptibility evaluation model using the SHAP method allows quantitative analysis of the differences in how much various factors contribute to disasters due to spatial heterogeneity, from the perspective of global and local evaluation units. In summary, the integrated explanatory framework based on the SHAP-XGBoost model can quantify the contribution of influencing factors on landslide occurrence at both global and local levels, which is conducive to the construction and improvement of the influencing factor system of landslide susceptibility in different regions. It can also provide a reference for predicting potential landslide hazard-prone areas and for Explainable Artificial Intelligence (XAI) research.

Anatomical growth response of Fagus sylvatica L. to landslide movements

Determining the age of landslide events is crucial for determining landslide risk, triggers, and also for predicting future landslide occurrence. Currently, the most accurate method for dating historical landslide events is dendrogeomorphic analysis. Unfortunately, the standard use of macroscopic growth responses of damaged trees for dating landslide activity suffers from many shortcomings. Thus, the aim of this study is to analyze in detail the growth response of trees to landslide movements at the anatomical level, a completely groundbreaking methodological approach. Ten specimens of European beech (Fagus sylvatica L.) were analyzed at two sampling heights, growing in two morphologically contrasting zones of the landslide area. Detailed anatomical analysis was focused on changes in morphometric parameters of the vessels and in the number of radial rays. The period (2008-2012) with the occurrence of the largest landslide movement (2010) recorded by long-term monitoring was analyzed. The results obtained revealed different anatomical responses in trees growing in different morphological zones of landslide. The tree responses on the ridge corresponded to the manifestations of tension wood formation, which corresponded to the stem tilting due to the landslide block movement. In the case of the trees in the trenches, root damage due to the subsidence of the landslide block blocked the flux of phytohormones, and their accumulation caused a significant reduction in the parameters of vessels and an increase in the number of rays. The study also includes recommendations in the future application of anatomical analyses in landslide research resulting from the obtained results. Thus, the obtained findings will improve the acquisition of chronological data for the purpose of landslide risk assessment.

Application of Bagging, Boosting and Stacking Ensemble and EasyEnsemble Methods for Landslide Susceptibility Mapping in the Three Gorges Reservoir Area of China

Since the impoundment of the Three Gorges Reservoir area in 2003, the potential risks of geological disasters in the reservoir area have increased significantly, among which the hidden dangers of landslides are particularly prominent. To reduce casualties and damage, efficient and precise landslide susceptibility evaluation methods are important. Multiple ensemble models have been used to evaluate the susceptibility of the upper part of Badong County to landslides. In this study, EasyEnsemble technology was used to solve the imbalance between landslide and nonlandslide sample data. The extracted evaluation factors were input into three bagging, boosting, and stacking ensemble models for training, and landslide susceptibility mapping (LSM) was drawn. According to the importance analysis, the important factors affecting the occurrence of landslides are altitude, terrain surface texture (TST), distance to residences, distance to rivers and land use. The influences of different grid sizes on the susceptibility results were compared, and a larger grid was found to lead to the overfitting of the prediction results. Therefore, a 30 m grid was selected as the evaluation unit. The accuracy, area under the curve (AUC), recall rate, test set precision, and kappa coefficient of a multi-grained cascade forest (gcForest) model with the stacking method were 0.958, 0.991, 0.965, 0.946, and 0.91, respectively, which a significantly better than the values produced by the other models.

Identification and deformation analysis of potential landslides after the Jiuzhaigou earthquake by SBAS-InSAR

A World Natural Heritage Site, Jiuzhaigou, is the first nature reserve in China whose primary purpose is to protect natural scenery. On August 8, 2017, a M_s 7.0 earthquake caused many unstable slopes in Jiuzhaigou, Sichuan Province, China. In the extreme storm conditions that follow, the unstable slopes tend to develop into potential landslides, which can cause many casualties and property losses in scenic areas. Sentinel-1A ascending orbit data were obtained in this paper to establish a SAR database. The large-scale deformation rate map of the study area was obtained using a small baseline set InSAR technology. The potential landslides in the deformation area are preliminarily confirmed with remote sensing interpretation. The field verification is further carried out by studying the deformation information of the characteristic points on the potential landslides. The results show that 13 deformation zones were preliminarily identified, and three typical deformation zones were selected for coupling verification and identified as potential landslides. At the same time, further analysis shows that the four potential landslides have been in continuous linear deformation for a long time since the earthquake, posing a severe threat to the safety of local people's lives and property. The research results provide a reference for the early identification and warning of potential landslides in earthquake-prone regions.

Determining the suitable settlement areas in Alanya with GIS-based site selection analyses

Urbanization, which is defined as an irreversible global-scale problem nowadays, necessitates the foundation of new settlement areas. In general, no sufficient scientific assessment and analysis were performed during these processes, and thus, various natural disasters cost the loss of many lives and properties every year. Nevertheless, considering the areas that are risky in terms of natural disasters during the selection of settlement areas might prevent a large-scale loss of lives and properties because of natural disasters. Within the scope of this study, it was aimed to determine suitable settlement areas in the Alanya district, which is one of the significant points of interest for tourists in our country and has a large population and new settlement areas because of this increasing population. Within this scope, besides the risks of flood and landslide that are the most important natural disasters in the region, and a forest fire that is among the most significant risks for the region, also the biocomfort zones were included in the assessments. As a result of the study, it was determined that the most important natural disaster risk was flooding in a large portion of the region and that only 6.72% of the study area was suitable for settlement in terms of all the criteria examined in the present study.

A mechanistic approach to include climate change and unplanned urban sprawl in landslide susceptibility maps

Empirical evidence shows that climate, deforestation and informal housing (i.e. unregulated construction practices typical of fast-growing developing countries) can increase landslide occurrence. However, these environmental changes have not been considered jointly and in a dynamic way in regional or national landslide susceptibility assessments. This gap might be due to a lack of models that can represent large areas (>100km²) in a computationally efficient way, while simultaneously considering the effect of rainfall infiltration, vegetation and housing. We therefore suggest a new method that uses a hillslope-scale mechanistic model to generate regional susceptibility maps under changing climate and informal urbanisation, which also accounts for existing uncertainties. An application in the Caribbean shows that the landslide susceptibility estimated with the new method and associated with a past rainfall-intensive hurricane identifies ~67.5 % of the landslides observed after that event. We subsequently demonstrate that the hypothetical expansion of informal housing (including deforestation) increases landslide susceptibility more (+20 %) than intensified rainstorms due to climate change (+6 %). However, their combined effect leads to a much greater landslide occurrence (up to +40 %) than if the two drivers were considered independently. Results demonstrate the importance of including both land cover and climate change in landslide susceptibility assessments. Furthermore, by modelling mechanistically the overlooked dynamics between urban growth and climate change, our methodology can provide quantitative information of the main landslide drivers (e.g. quantifying the relative impact of deforestation vs informal urbanisation) and locations where these drivers are or might become most detrimental for slope stability. Such information is often missing in data-scarce developing countries but is key for supporting national long-term environmental planning, for targeting financial efforts, as well as for fostering national or international investments for landslide mitigation.

Modeling snowmelt influence on shallow landslides in Tartano valley, Italian Alps

Shallow landslides (SLs) are rapid soil mass movements, typically occurring in the mountain areas, involving the most superficial soil layers up to 5 to 10 m in depth. Damages, and casualties due to shallow landslides are recorded globally, and in literature a variety of models to study landslides have been implemented hitherto. Often times, shallow landslides occur in the wake of snowfall events, when sudden temperature increase triggers fast snow thaw, and soil moisture increases thereby. Several models studied the influence of intensity, and duration of rainfall upon shallow landslides, but the effect of snow melt in spring/summer was little considered so far. Thus, we developed a simple but robust, and parameter-wise parsimonious model, that mimics the triggering mechanism of SLs, accounting for the combined effect of precipitation duration and intensity, and snowmelt at thaw. The model is here applied to the case study of the high altitude Tartano basin, paradigmatic of SLs in the Alps of Lombardia. Our results showed that about 26 % of the Tartano basin slopes display unstable conditions. Using a traditional (i.e. rainfall-based) approach, the occurrence of shallow landslides was predicted in ca. 19 % of the basin, mainly during storms in October and November. In contrast, when snowmelt was included, the model was able to mimic potential SLs even during April and May, when snow melt rate is the highest, and may increase SLs triggering potential, to ca. 26 % of the treated area. With better spatial and temporal description of slope failure as achieved here, validated against observed failures, a public authority may be prepared to implement emergency plans, to prevent injuries, causalities, and damages to infrastructures even during springtime, when shallow landslides may occur in response to fast snowmelt, even during dry, clear sky days, and with scarce/null precipitation.

Landslide Displacement Prediction Based on Multivariate LSTM Model

There are many frequent landslide areas in China, which badly affect local people. Since the 1980s, there have been more than 200 landslides in China with a death toll of 30 or more people at a time, economic losses of more than CNY 10 million or significant social impact. Therefore, the study of landslide displacement prediction is very important. The traditional ARIMA and LSTM models are commonly used for forecasting time series data. In our study, a multivariable LSTM landslide displacement prediction model is proposed based on the traditional LSTM model, which integrates rainfall and reservoir water level data. Taking the Baijiabao landslide in the Three Gorges Reservoir area as an example, the data of displacement, rainfall and reservoir water level of monitoring point ZG323 from November 2006 to December 2012 were selected for this study. Our results show that the displacement prediction results of the multivariable LSTM model are more accurate than those of the ARIMA and the univariate LSTM models, and the mean square, root mean square and mean absolute errors are the smallest, which are 0.64223, 0.8014 and 0.50453 mm, respectively. Therefore, the multivariable LSTM model method has higher accuracy and better application prospects in the displacement prediction of the Baijiabao landslide, which can provide a certain reference for the displacement prediction of the same type of landslide.

Blind identification of active landslides in urban areas: a new set of comprehensive criteria

More than 70% of catastrophic landslides were previously unknown and brought tremendous losses to human life and property in urban regions; therefore, there is an urgent need for early identification of active landslides to eliminate landslide risk at the early stage. However, early identification of landslides has always been a worldwide challenge due to high concealment, steep topography, inaccessible location, and sudden onset. This work suggests a new set of comprehensive criteria for the early identification of landslides by integrating surface deformation, geological, topographic, geomorphological, and disaster-failure features. This set of criteria is universally applicable with no use of the prior knowledge of landslide locations (blind identification) and is successfully validated by a field survey. This work selects the Xuecheng region, a hard-hit area of landslides, as the study area and employs multisource data (seismic, geological, topographic, meteorological, SAR, and optical remote sensing data) and time-series InSAR technology to identify active landslides and reveal their deformation rules. Some new viewpoints are suggested. (1) The new comprehensive criteria synthesize the surface deformation, disaster-controlling, and disaster-inducing characteristics and achieve relatively high accuracy by field validation. (2) Forty-seven active landslides are identified in Xuecheng with no use of the prior knowledge of landslides. The soft rocks or soft-hard interbeddings, tectonic movement, fluvial undercutting and eroding, precipitation, earthquakes, and human engineering activity control or induce the development of these active landslides. (3) Two giant landslides that significantly threaten human lives and properties and exhibit different movement modes are selected to highlight the deformation rules of active landslides under the coupled action of poor lithologic condition, tectonic movement, river erosion, precipitation, and human engineering activity. The suggested new criteria can be applied to other landslide hard-hit urban regions and contribute to the timely and effective prevention and control of catastrophic landslides, reduction of enormous disaster losses, and rational management of the environment.

Analysis on the susceptibility of environmental geological disasters considering regional sustainable development

Environmental geological disasters seriously threaten human lives and property. A reasonable analysis of the susceptibility to environmental geological disasters is the basis for disaster prevention and mitigation and can promote the sustainable development of regional economy. This study analyzes the susceptibility of environmental geological disasters, such as collapses, landslides, and debris flows in Helong City, China. Through investigation and comprehensive analysis, ten environmental, geological disaster causing factors, including stratum lithology, distance from the fault, elevation, slope, aspect, rainfall, distance from the water system, NDVI, distance from the road, and profile curvature, were extracted. Combined with GIS, a vulnerability analysis database of environmental geological disasters was established, and vulnerability zoning prediction was performed by using two models of information amount and a generalized regression neural network (GRNN). Then, disaster-vulnerability factors such as population density, road density, GDP, and land use type were added. The results show that the predicted results of the two models are similar to the actual survey results.. The environmental geological disasters in the study area are mainly low and not prone to occur, and the northeast and central areas are highly prone to environmental geological disasters, which are the key prevention and control areas in the study area. The coverage rate of high-vulnerability areas with a high degree of economic development is 8.63%, and the prediction results of the GRNN model are mostly distributed in spots and strips, which is more conducive to accurate disaster prevention and mitigation and cost reduction, promotes regional sustainable development, and has guiding significance for disaster prevention and control and urban planning and construction.

Landslide Susceptibility Mapping by Fusing Convolutional Neural Networks and Vision Transformer

Landslide susceptibility mapping (LSM) is an important decision basis for regional landslide hazard risk management, territorial spatial planning and landslide decision making. The current convolutional neural network (CNN)-based landslide susceptibility mapping models do not adequately take into account the spatial nature of texture features, and vision transformer (ViT)-based LSM models have high requirements for the amount of training data. In this study, we overcome the shortcomings of CNN and ViT by fusing these two deep learning models (bottleneck transformer network (BoTNet) and convolutional vision transformer network (ConViT)), and the fused model was used to predict the probability of landslide occurrence. First, we integrated historical landslide data and landslide evaluation factors and analysed whether there was covariance in the landslide evaluation factors. Then, the testing accuracy and generalisation ability of the CNN, ViT, BoTNet and ConViT models were compared and analysed. Finally, four landslide susceptibility mapping models were used to predict the probability of landslide occurrence in Pingwu County, Sichuan Province, China. Among them, BoTNet and ConViT had the highest accuracy, both at 87.78%, an improvement of 1.11% compared to a single model, while ConViT had the highest F1-socre at 87.64%, an improvement of 1.28% compared to a single model. The results indicate that the fusion model of CNN and ViT has better LSM performance than the single model. Meanwhile, the evaluation results of this study can be used as one of the basic tools for landslide hazard risk quantification and disaster prevention in Pingwu County.

A bibliometric and content analysis of research trends on GIS-based landslide susceptibility from 2001 to 2020

To assess the status of hotspots and research trends on geographic information system (GIS)-based landslide susceptibility (LS), we analysed 1142 articles from the Thomas Reuters Web of Science Core Collection database published during 2001-2020 by combining bibliometric and content analysis. The paper number, authors, institutions, corporations, publication sources, citations, and keywords are noted as sub/categories for the bibliometric analysis. Thematic LS data, including the study site, landslide inventory, conditioning factors, mapping unit, susceptibility models, and mode fit/prediction performance evaluation, are presented in the content analysis. Then, we reveal the advantages and limitations of the common approaches used in thematic LS data and summarise the development trends. The results indicate that the distribution of articles shows clear clusters of authors, institutions, and countries with high academic activity. The application of remote sensing technology for interpreting landslides provides a more convenient and efficient landslide inventory. In the landslide inventory, most of the sample strategies representing the landslides are point and polygon, and the most frequently used sample subdividing strategy is random sampling. The scale effects, lack of geographic consistency, and no standard are key problems in landslide conditioning factors. Feature selection is used to choose the factors that can improve the model's accuracy. With advances in computing technology and artificial intelligence, LS models are changing from simple qualitative and statistical models to complex machine learning and hybrid models. Finally, five future research opportunities are revealed. This study will help investigators clarify the status of LS research and provide guidance for future research.

Evaluation of potential changes in landslide susceptibility and landslide occurrence frequency in China under climate change

Climate change can alter the frequency and intensity of extreme rainfall across the globe, leading to changes in hazards posed by rainfall-induced landslides. In recent decades, China suffered great human and economic losses due to rainfall-induced landslides. However, how the landslide hazard situation will evolve in the future is still unclear, also because of sparse comprehensive evaluations of potential changes in landslide susceptibility and landslide occurrence frequency under climate change. This study builds upon observed and modelled rainfall data from 24 bias-corrected Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models (GCMs), a statistical landslide susceptibility model, and empirical rainfall thresholds for landslide initiation, to evaluate changes in landslide susceptibility and landslide occurrence frequency at national-scale. Based on four Shared Socioeconomic Pathways (SSP) scenarios, changes in the rainfall regime are projected and used to evaluate subsequent alterations in landslide susceptibility and in the frequency of rainfall events exceeding empirical rainfall thresholds. In general, the results indicate that the extend of landslide susceptible terrain and the frequency of landslide-triggering rainfall will increase under climate change. Nevertheless, a closer inspection provides a spatially heterogeneous picture on how these landslide occurrence indicators may evolve across China. Until the late 21st century (2080-2099) and depending on the SSP scenarios, the mean annual precipitation is projected to increase by 13.4 % to 28.6 %, inducing an 1.3 % to 2.7 % increase in the modelled areal extent of moderately to very highly susceptible terrain. Different SSP scenarios were associated with an increase in the frequency of landslide-triggering rainfall events by 10.3 % to 19.8 % with respect to historical baseline. Spatially, the southeastern Tibetan Plateau and the Tianshan Mountains in Northwestern Basins are projected to experience the largest increase in landslide susceptibility and frequency of landslide-triggering rainfall, especially under the high emission scenarios. Adaptation and mitigation methods should be prioritized for these future landslide hotspots. This work provides a better understanding of potential impacts of climate change on landslide hazard across China and represents a first step towards national-scale quantitative landslide exposure and risk assessment under climate change.

A Digital Template for the Generic Multi-Risk (GenMR) Framework: A Virtual Natural Environment

Extreme disasters, defined as low-probability-high-consequences events, are often due to cascading effects combined to amplifying environmental factors. While such a risk complexity is commonly addressed by the modeling of site-specific multi-risk scenarios, there exists no harmonized approach that considers the full space of possibilities, based on the general relationships between the environment and the perils that populate it. In this article, I define the concept of a digital template for multi-risk R&D and prototyping in the Generic Multi-Risk (GenMR) framework. This digital template consists of a virtual natural environment where different perils may occur. They are geological (earthquakes, landslides, volcanic eruptions), hydrological (river floods, storm surges), meteorological (windstorms, heavy rains), and extraterrestrial (asteroid impacts). Both geological and hydrological perils depend on the characteristics of the natural environment, here defined by two environmental layers: topography and soil. Environmental objects, which alter the layers, are also defined. They are here geomorphic structures linked to some peril source characteristics. Hazard intensity footprints are then generated for primary, secondary, and tertiary perils. The role of the natural environment on intensity footprints and event cascading is emphasized, one example being the generation of a "quake lake". Future developments, à la SimCity, are finally discussed.

A novel landslide susceptibility optimization framework to assess landslide occurrence probability at the regional scale for environmental management

Landslide is a hazard that has drastic repercussions on population and the environment worldwide. Landslide susceptibility mapping (LSM) is vital for landslide disaster management and formulating mitigation strategies. In this study, with the support of geographic information system and remote sensing, a new LSM hybrid framework is developed based on random forest (RF) and cusp catastrophe model (CCM). Under the framework, 15 conditioning factors and 2082 historical landslides are selected to test and compare its performance in a landslide-prone area in Liangshan, Southwest China. The results depicted a better performance of the new LSM hybrid framework (RF-CCM) than those of RF or traditional application mode of catastrophe model (Catastrophe fuzzy membership functions, CFMFs) only. The RF-CCM achieved the highest accuracy (0.901), the narrowest confidence interval (0.895-0.907), and the smallest standard error (0.004) among all the models. Notably, RF-CCM successfully decreased the uncertainty of CFMFs in determining the relative importance of conditioning factors, overcame the dependence of the CFMFs on independence among the conditioning factors, and had a higher stability level than RF. Moreover, distance to human engineering activities and slope had the greatest impact on LSM in the modeling process. The study result can provide insights for developing reliable predictive models for other landslide-prone areas with similar geo-environmental conditions.

Landslide Susceptibility Evaluation Based on Potential Disaster Identification and Ensemble Learning

Catastrophic landslides have much more frequently occurred worldwide due to increasing extreme rainfall events and intensified human engineering activity. Landslide susceptibility evaluation (LSE) is a vital and effective technique for the prevention and control of disastrous landslides. Moreover, about 80% of disastrous landslides had not been discovered ahead and significantly impeded social and economic sustainability development. However, the present studies on LSE mainly focus on the known landslides, neglect the great threat posed by the potential landslides, and thus to some degree constrain the precision and rationality of LSE maps. Moreover, at present, potential landslides are generally identified by the characteristics of surface deformation, terrain, and/or geomorphology. The essential disaster-inducing mechanism is neglected, which has caused relatively low accuracies and relatively high false alarms. Therefore, this work suggests new synthetic criteria of potential landslide identification. The criteria involve surface deformation, disaster-controlling features, and disaster-triggering characteristics and improve the recognition accuracy and lower the false alarm. Furthermore, this work combines the known landslides and discovered potential landslides to improve the precision and rationality of LSE. This work selects Chaya County, a representative region significantly threatened by landslides, as the study area and employs multisource data (geological, topographical, geographical, hydrological, meteorological, seismic, and remote sensing data) to identify potential landslides and realize LSE based on the time-series InSAR technique and XGBoost algorithm. The LSE precision indices of AUC, Accuracy, TPR, F1-score, and Kappa coefficient reach 0.996, 97.98%, 98.77%, 0.98, and 0.96, respectively, and 16 potential landslides are newly discovered. Moreover, the development characteristics of potential landslides and the cause of high landslide susceptibility are illuminated. The proposed synthetic criteria of potential landslide identification and the LSE idea of combining known and potential landslides can be utilized to other disaster-serious regions in the world.

Principles for collaborative risk communication: Reducing landslide losses in Puerto Rico

Landslides are frequent and damaging natural hazards that threaten the people and the natural and built environments of Puerto Rico. In 2017, more than 70,000 landslides were triggered across the island by heavy rainfall from Hurricane María, prompting requests by local professionals for landslide education and outreach materials. This article describes a novel collaborative risk communication framework that was developed to meet those requests and shaped the creation of a Spanish- and English-language Landslide Guide for Residents of Puerto Rico. Collaborative risk communication is defined here as an iterative process guided by a set of principles for the interdisciplinary coproduction of hazards information and communication products by local and external stakeholders. The process that supports this form of risk communication involves mapping out the risk communication stakeholders in the at-risk or -disaster-affected location-in this case Puerto Rico-and collaborating over time to address a shared challenge, such as landslide hazards. The approach described in this article involved the formation of a core team of government and university partners that expanded in membership to conduct collaborative work with an informal network of hazards professionals from diverse sectors in Puerto Rico. The following principles guided this process: cultural competence, ethical engagement, listening, inclusive decision -making, empathy, convergence research, nested mentoring, adaptability, and reciprocity. This article contributes to the field of risk communication and emergency management by detailing these principles and the associated process in order to motivate collaborative risk communication efforts in different geographic and cultural contexts. While the work described here focuses on addressing landslides, the principles and process are transferable to other natural, technological, and willful human-caused hazards. They may also serve as a roadmap for future partnerships among government agencies and university researchers to inform the cocreation of science education and outreach tools.

Spatio-temporal landslide inventory and susceptibility assessment using Sentinel-2 in the Himalayan mountainous region of Pakistan

The 2005 Kashmir earthquake has triggered widespread landslides in the Himalayan mountains in northern Pakistan and surrounding areas, some of which are active and are still posing a significant risk. Landslides triggered by the 2005 Kashmir earthquake are extensively studied; nevertheless, spatio-temporal landslide susceptibility assessment is lacking. This can be partially attributed to the limited availability of high temporal resolution remote sensing data. We present a semi-automated technique to use the Sentinel-2 MSI data for co-seismic landslide detection, landslide activities monitoring, spatio-temporal change detection, and spatio-temporal susceptibility mapping. Time series Sentinel-2 MSI images for the period of 2016-2021 and ALOS PALSAR DEM are used for semi-automated landslide inventory map development and temporal change analysis. Spectral information combined with topographical, contextual, textural, and morphological characteristics of the landslide in Sentinel-2 images is applied for landslide detection. Subsequently, spatio-temporal landslide susceptibility maps are developed utilizing the weight of evidence statistical modeling with seven causative factors, i.e., elevation, slope, geology, aspect, distance to fault, distance to roads, and distance to streams. The results reveal that landslide occurrence increased from 2016 to 2021 and that the coverage of areas of relatively high susceptibility has increased in the study area.

Spatial-Temporal Characteristics of Multi-Hazard Resilience in Ecologically Fragile Areas of Southwest China: A Case Study in Aba

Aba's topography, weather, and climate make it prone to landslides, mudslides, and other natural disasters, which limit economic and social growth. Assessing and improving regional resilience is important to mitigate natural disasters and achieve sustainable development. In this paper, the entropy weight method is used to calculate the resilience of Aba under multi-hazard stress from 2010 to 2018 by combining the existing framework with the disaster resilience of the place (DROP) model. Then spatial-temporal characteristics are analyzed based on the coefficient of variation and exploratory spatial data analysis (ESDA). Finally, partial least squares (PLS) regression is used to identify the key influences on disaster resilience. The results show that (1) the disaster resilience in Aba increased from 2010 to 2018 but dropped in 2013 and 2017 due to large-scale disasters. (2) There are temporal and spatial differences in the level of development in each of the Aba counties. From 2010 to 2016, disaster resilience shows a significant positive spatial association and high-high (HH) aggregation in the east and low-low (LL) aggregation in the west. Then the spatial aggregation weakened after 2017. This paper proposes integrating regional development, strengthening the development level building, and emphasizing disaster management for Aba.

Analytical techniques for mapping multi-hazard with geo-environmental modeling approaches and UAV images

The quantitative spatial analysis is a strong tool for the study of natural hazards and their interactions. Over the last decades, a range of techniques have been exceedingly used in spatial analysis, especially applying GIS and R software. In the present paper, the multi-hazard susceptibility maps compared in 2020 and 2021 using an array of data mining techniques, GIS tools, and Unmanned aerial vehicles. The produced maps imply the most effective morphometric parameters on collapsed pipes, gully heads, and landslides using the linear regression model. The multi-hazard maps prepared using seven classifiers of Boosted regression tree (BRT), Flexible discriminant analysis (FDA), Multivariate adaptive regression spline (MARS), Mixture discriminant analysis (MDA), Random forest (RF), Generalized linear model (GLM), and Support vector machine (SVM). The results of each model revealed that the greatest percentage of the study region was low susceptible to collapsed pipes, landslides, and gully heads, respectively. The results of the multi-hazard models represented that 52.22% and 48.18% of the study region were not susceptible to any hazards in 2020 and 2021, while 6.19% (2020) and 7.39% (2021) of the region were at the risk of all compound events. The validation results indicate the area under the receiver operating characteristic curve of all applied models was more than 0.70 for the landform susceptibility maps in 2020 and 2021. It was found where multiple events co-exist, what their potential interrelated effects are or how they interact jointly. It is the direction to take in the future to determine the combined effect of multi-hazards so that policymakers can have a better attitude toward sustainable management of environmental landscapes and support socio-economic development.

Imminent threat of rock-ice avalanches in High Mountain Asia

Upsurge of glacier-related hazards in High Mountain Asia (HMA) has been evident in recent years due to global warming. While many glacial-related hazards are instantaneous, some large landslides were preceded by slow gravitational deformation, which can be predicted to evade catastrophes. Here, we present robust evidence of historical deformation in 2021 Chamoli rock-ice avalanche of Himalaya using space imaging techniques. Multi-temporal satellite data provide evidence of a precursor event in 2016 and expansion of a linear fracture along joint planes, indicating 2021 rock-ice avalanche is a retrogressive wedge failure. The deformation history shows that the fracture propagated at a velocity of ~0.07 m day^-1 until September 2020, and with an accelerated velocity (~0.14 m day^-1 on average) lately. Analysis of recent similar cases in HMA supported our inference on global warming-induced glacier retreat and thermomechanical effects in enhancing the weakening of fractured rock masses in tectonically active mountain belts. Recent advances in Earth observation and seismic monitoring system can offer clues to the location and timing of impending catastrophic failures in high mountain regions.

Identification of Landslides in Mountainous Area with the Combination of SBAS-InSAR and Yolo Model

Landslides have been frequently occurring in the high mountainous areas in China and poses serious threats to peoples' lives and property, economic development, and national security. Detecting and monitoring quiescent or active landslides is important for predicting risks and mitigating losses. However, traditional ground survey methods, such as field investigation, GNSS, and total stations, are only suitable for field investigation at a specific site rather than identifying landslides over a large area, as they are expensive, time-consuming, and laborious. In this study, the feasibility of using SBAS-InSAR to detect landslides in the high mountainous areas along the Yunnan Myanmar border was tested first, with fifty-four IW mode Sentinel-1A ascending scenes from 12 January 2019 to 8 December 2020. Next, the Yolo deep-learning model with Gaofen-2 images captured on 5 December 2020 was tested. Finally, the two techniques were combined to achieve better performance, given each of them has intrinsic limitations on landslide detection. The experiment indicated that the combination could improve the match rate between detection results and references, which implied that the performance of landslide detection can be improved with the fusion of time series SAR images and optical images.

Shallow Failure of Weak Slopes in Bayan Obo West Mine

The slope stability of large open-pit mines has always been a concern and the analysis of large-scale slope landslides is a focus. However, shallow failure in soft rock slopes also has a serious impact on safe production. The northern slope of Baiyunebo West Mine has many shallow landslides in the final slope, resulting in damage of the maintenance channel of the belt transportation system, which has a serious impact on the safety of production. In order to reduce the shallow failure in weak rock slope, it is necessary to analyze the behavior and characteristics of shallow failure in weak rock. Firstly, the mechanical parameters of the intact rock were obtained by using the exploration data; secondly, through the analysis of blasting-damage range, the distribution characteristics of fractures after the failure of weak rock were obtained. Finally, through theoretical analysis, numerical simulation, surface displacement monitoring and on-site shallow-failure case analysis, the deformation and characteristics of shallow failure of weak rock slope in West Mine were obtained. It was found that the mechanical parameters of rock mass strength on the surface of weak rock slope and the original rock were quite different after mining disturbance. The mode of failure of shallow weak rock slope in the West Mine was creep-cracking; the numerical modelling analysis was carried out by using the assignment method of shallow lithology weakening and gradual change, which is more in line with the deformation characteristics of weak rock slope in West Mine. The lower deformation of the soft rock slope in West Mine is 3-5 times that of the upper deformation. The research results are helpful to understand the influence of blasting on the stability of soft rock slope. At present, West Mine has started to adjust blasting parameters according to the research results, so as to reduce the excessive damage of blasting to rock mass, so the stability of the slope is improved.

Landslide susceptibility mapping by integrating analytical hierarchy process, frequency ratio, and fuzzy gamma operator models, case study: North of Lorestan Province, Iran

Identifying landslide-prone areas is an essential step in assessing landslide risk and reducing landslide damage. In this paper, GIS-based spatial analysis has been used to prepare the landslide susceptibility (LS) map in the north of Lorestan province in western Iran. For this purpose, three main criteria and their sub-criteria were identified as causative factors including geology and topography (i.e., distance from the fault, lithology, slope, aspect, and elevation), climate (i.e., rainfall and distance from the river), and environmental parameters (i.e., distance from the road, land-cover, NDVI). One hundred thirty-six known landslides were randomly divided into training ([Formula: see text] 70%) and validation ([Formula: see text] 30%) datasets. This study is based on the integration of popular analytic hierarchy process (AHP), frequency ratio (FR), and the fuzzy gamma operator (FGO) techniques. AHP was utilized to prioritize causal factors and fuzzy technique was applied in two stages of factor map fuzzification and calculation of sub-criteria maps and then overlap of fuzzified map layers. The fuzzy membership (FM) values were determined based on the FR method, which was normalized between the ranges of 0 and 1. Finally, LS zoning maps were estimated in five susceptibility classes (very low, low, moderate, high, and very high). Validation processes by comparing the three output maps with the layer of validation landslides in the study area and area under receiver operating characteristic curve confirm that the gamma value of 0.9 (AUC = 0.88) offers a more accurate LS map compared to other gamma values. The results of this study will be reliable for landslide risk reduction strategies.

Slide type landslide susceptibility assessment of the Büyük Menderes watershed using artificial neural network method

The Büyük Menderes watershed is the largest drainage watershed in Western Anatolia with an area of approximately 26,000 km². In the study area, almost 863 landslides occurred, extending over 222 km² with a mean landslide area of 0.21 km². In this study, landslide susceptibility assessments were carried out using artificial neural network method, which is one of the data-driven methods. In this study, that will contribute to the mitigation or control of the landslides caused by the reasons controlling the spatial and temporal distribution of landslides created in the GIS and MATLAB environment by using scientific and technological approaches within the framework. Since derivative activation function is also used in back-propagation artificial neural networks, its derivative is easily calculated in order not to slow down the calculation. Levenberg-Marquardt back-propagation (LM), resilient back propagation back-propagation (trainrp), scaled conjugate gradient back-propagation (trainscg), conjugate gradient with Powell/Beale restarts back-propagation (traincgb), and Fletcher-Powell conjugate gradient back-propagation (traincgf) algorithms are used, which constantly interrogate the link between the input parameter and the result output, and at least one cell's output is given as an input to any other cell. Geology, digital elevation model, slope, topographic wetness index, roughness index, plan, profile curvatures, and proximity to active faults and rivers were used as landslide conditioning factors. In susceptibility assessments, landslides were separated by 70% analysis, 15% test, and 15% validation datasets by random selection method. The performances of the landslide susceptibility maps were assessed by the area under the ROC curve (AUC), accuracy (ACC), precision, recall, F1 score, Kappa test error histogram, and confusion matrix, respectively. The area under the receiver operating characteristic curves, analysis, testing, validation, landslides, and study areas were found between 0.873 and 0.911. The susceptibility map had a high prediction rate in which high and very high susceptible zones corresponded to 26% of the study area including 82% of the recorded landslides.