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Selecting appropriate mapping units is vital for landslide susceptibility mapping as well as pattern investigation, given that various units-based analyses extensively control the prediction performances. This paper investigated the landslide development through the interpretation and field surveys along the Yunling–Yanjing segment of the Lancang River in southeastern Tibet, and fulfil LSM with the consideration of 15 conditioning factors. Two grid unit methods (single-point and multi-point patterns) and two slope unit methods were comparatively analyzed for model training and mapping of landslide susceptibility via machine learning algorithms. The results suggest that the landslides are preferentially distributed in an elevation range of 2000–4000 m, in a slope range of 20–40°, a local relief range of 1000–2500 m, and southwest-oriented slopes. The data extracted by the multi-point method denotes a higher representation of landslide development features. All models possessed positive prediction ability for landslide susceptibility, and the multi-point method based on grid unit performed the best with the AUC exceeding 0.9. The best-performing models indicated that zones of high and very high susceptibility mainly distributed adjacent to the mainstem and some tributaries of the Lancang River. Furthermore, the distribution of "safety islands" (the slopes less prone to landslides) along National Highway G214 was reasonably illustrated as well, which provides a hazard predictability for such an important transportation corridor along the deeply-incised valley in the Lancang River. This study demonstrates a theoretical basis for the regional disaster prevention and mitigation for human activity, and provides methodological references for landslide susceptibility evaluation in similar mountainous areas over the world. [ABSTRACT FROM AUTHOR]

Among natural hazards occurring offshore, submarine landslides pose a significant risk to offshore infrastructure installations attached to the seafloor. With the offshore being important for current and future energy production, there is a need to anticipate where future landslide events are likely to occur to support planning and development projects. Using the northern Gulf of Mexico (GoM) as a case study, this paper performs Landslide Susceptibility Mapping (LSM) using a gradient-boosted decision tree (GBDT) model to characterize the spatial patterns of submarine landslide probability over the United States Exclusive Economic Zone (EEZ) where water depths are greater than 120 m. With known spatial extents of historic submarine landslides and a Geographic Information System (GIS) database of known topographical, geomorphological, geological, and geochemical factors, the resulting model was capable of accurately forecasting potential locations of sediment instability. Results of a permutation modelling approach indicated that LSM accuracy is sensitive to the number of unique training locations with model accuracy becoming more stable as the number of training regions was increased. The influence that each input feature had on predicting landslide susceptibility was evaluated using the SHapely Additive exPlanations (SHAP) feature attribution method. Areas of high and very high susceptibility were associated with steep terrain including salt basins and escarpments. This case study serves as an initial assessment of the machine learning (ML) capabilities for producing accurate submarine landslide susceptibility maps given the current state of available natural hazard-related datasets and conveys both successes and limitations. [ABSTRACT FROM AUTHOR]

As one of the most destructive geological disasters, a myriad of landslides has revived and developed in the Three Gorges Reservoir area under the combined action of various detrimental factors. Therefore, the pertinently regional landslide susceptibility mapping (LSM) is of great significance for disaster prevention and mitigation. In this study, LSM is prepared by using a boosting-C5.0 decision tree model. Under the landslide verification of on-site investigations, the study area is divided into accumulation and rock areas, and a total of 12 impact factors are selected. TOL and VIF are employed to determine the multicollinearity among the impact factors. The independent training (80%) and validation (20%) datasets are constructed by random sampling for LSM. ANN, C5.0, and SVM are selected for comparative analysis. The results show that there is no rigorous multicollinearity among the impact factors proposed in this paper. The landslide susceptibility in the study area is divided into low, moderate, high, and very high. The highest susceptibility area distributes along the riverside where the landslide ratio is 37.05% in boosting-C5.0 model. Then the ROCs are expropriated to infer the accuracy of each model. The boosting-C5.0 performs the best with the largest area under the curve in both accumulation and rock areas, reaching at 0.991 and 0.990 in the validation sets, respectively. Finally, the composite modification of the 5 validation sets shows that the uncertainty of boosting-C5.0 is concentrated in the intermediate probability areas of susceptibility. This study reveals the feasibility of machine learning in landslide susceptibility assessment, which could provide a basis for the risk management and control of geological disasters. [ABSTRACT FROM AUTHOR]

Landslide monitoring is a mandatory step in landslide risk assessment. It requires collecting data on landslide conditions (e.g., areal extent, landslide kinematics, surface topography, hydrogeometeorological parameters, and failure surfaces) from different time periods and at different scales, from site-specific to local, regional, and national, to assess landslide activity. In this analysis, we collected information on landslide monitoring techniques from 17 members of the Earth Observation and Geohazards Expert Group (from EuroGeoSurveys) deployed between 2005 and 2021. We examined the types of the 75 recorded landslides, the landslide techniques, spatial resolution, temporal resolution, status of the technique (operational, non-operational), time of using (before the event, during the event, after the event), and the applicability of the technique in early warning systems. The research does not indicate the accuracy of each technique but, rather, the extent to which Geological Surveys conduct landslide monitoring and the predominant techniques used. Among the types of landslides, earth slides predominate and are mostly monitored by geological and engineering geological mapping. The results showed that Geological Surveys mostly utilized more traditional monitoring techniques since they have a broad mandate to collect geological data. In addition, this paper provides new insights into the role of the Geological Surveys on landslide monitoring in Europe and contributes to landslide risk reduction initiatives and commitments (e.g., the Kyoto Landslide Commitment 2020). [ABSTRACT FROM AUTHOR]

Most previous studies on the quantitative risk assessment (QRA) of landslides focused on the probability of slope failure at the pre-failure stage and adopted empirical models for consequence analysis. The conventional approaches simplify the relationship between the pre-failure state and the post-failure behavior and cannot reasonably account for the effects of uncertainty on the entire landslide process. In this paper, an efficient QRA method that involves the direct simulation of the entire landslide process is proposed. A QRA formula that considers the probability of only those landslides that can impact the element at risk is used. The coupled Eulerian–Lagrangian method is used to simulate the entire landslide process and to identify slopes that can impact the element at risk and determine the failure consequences. The subset simulation method is adopted to efficiently estimate the probability of landslide impact, and parameter uncertainty is considered. Two case histories of landslides are investigated. First, the 2011 Baqiao loess landslide in Xi'an, China, is investigated, and the results of the proposed method are compared with those of the conventional approaches. Second, the proposed method is applied to assess the risk of the 2015 Ganjingzi landslide in the Three Gorges Reservoir. The effects of the risk mitigation works are also discussed. [ABSTRACT FROM AUTHOR]

In this paper, the artificial bee colony (ABC) algorithm is used to generate landslide susceptibility maps. To the best of our knowledge, it is the first time the ABC algorithm is applied as a classifier on landslide susceptibility mapping. Twelve features are considered as inputs for the ABC model in this research work. These features contain redundant and correlated information. Therefore, normalization and dimension reduction are applied in the pre-processing stage. Three different normalization methods are investigated, i.e. minimum and maximum (Min-Max), mean standard deviation (Mean-SD), and median and interquartile range (Med-IQR). Two dimensionality reduction techniques, i.e., principal component analysis (PCA) and canonical variate analysis (CVA), are utilized to deal with high-dimensional information and eliminate correlated information in the data. In total, six different schemes of input data based on the three normalization methods and two techniques of dimension reduction are applied to train the ABC model. There are a total of 382 landslide incidents occurred at various locations on Penang Island, out of which 255 are selected for training and remaining 127 are used for the validation purpose of the model. The model classification accuracies and receiver of characteristics (ROC) curve are applied to evaluate the model's performance. The results show that the ABC model trained using Min-Max normalization and CVA scheme generates the best results among these six schemes. The landslide susceptibility maps generated in this research offer significant information for landslide risk management. [ABSTRACT FROM AUTHOR]

Long-runout landslides can be extremely hazardous and unpredictable. The prediction of long-runout landslides is challenging because it is unclear what factors control their mobility, and it is unclear how to best measure their runout given that they traverse changing topography. In this paper, we document the development and statistical evaluation of a Landslide Runout Score (LRS) system to predict short, medium, and long runout using a new mobility measure, the unitless Runout Number L/A1/2 (where L is landslide length and A is landslide area). The Runout Number has previously been correlated to three geomorphological factors (planimetric curvature, sand content, and upslope contributing area normalized to landslide area), so the LRS uses these factors as inputs. These factors are readily calculated using geographic information systems (GIS) and publicly available data sources. The LRS system predicts runout categories (short, medium, and long) with one-vs.-all accuracies (i.e., percentage of correct predictions within a given class) of 75, 58, and 72%, respectively, for a total weighted accuracy of approximately 65%. The results of this work are summarized in a worksheet that can be used by geologists and engineers to develop preliminary predictions of landslide runout behavior, which can be incorporated into or used alongside regional-scale landslide hazard assessments. [ABSTRACT FROM AUTHOR]

Rapid detection of landslides using remote sensing images plays a key role in hazard assessment and mitigation. Many deep convolutional neural network-based models have been proposed for this purpose; however, for small-scale landslide detection, excessive convolution and pooling process may cause potential texture information loss, which can lead to misclassification of landslide target. In this paper, we present a novel UNet model for the automatic detection of landslides, wherein the reversed image pyramid features (RIPFs) are adapted to mitigate the information loss caused by a succession of convolution and pooling. The proposed RIPF-Unet model is trained and validated using the open-source landslides dataset of the Bijie area, Guizhou Province, China, wherein the precision of the proposed model is observed to increase by 3.5% and 4.0%, compared to the conventional UNet and UNet + + model, respectively. The proposed RIPF-Unet model is further applied to the case of the Longtoushan region after the 2014 Ms.6.5 Ludian earthquake. Results show that the proposed model achieves a 96.63% accuracy for detecting landslides using remote sensing images. And the RIPF-Unet model is also advanced in its compact parameter size; notably, it is 31% lighter compared to the UNet + + model. [ABSTRACT FROM AUTHOR]

As a frequent natural disaster, landslides incur significant economic and human losses worldwide. The main idea of this paper is to propose novel integrative models for landslide susceptibility evaluation in a prone area of Chaharmahal va Bakhtiari Province in Iran. To do this, four metaheuristic techniques, namely chimp optimization algorithm (ChOA), crow search algorithm (CSA), satin bowerbird optimization (SBO), and water cycle algorithm (WCA) are used to supervise the training of an artificial neural network (ANN). A spatial database is created by 170 historical landslides and 14 most common conditioning factors. The models are optimized in terms of hyper-parameters and elite ones are then used to produce the susceptibility maps. During the training phase, all models could acquire a reliable understanding of the landslide pattern. The obtained accuracy index of area under the receiving operating characteristic curve (AUROC) showed that the maps produced by the WCA-ANN (AUROC = 0.925) and SBO-ANN (AUROC = 0.900) are more accurate than those of the ChOA-ANN (AUROC = 0.851) and CSA-ANN (AUROC = 0.855). Considering this assessment, the suggested hybrid models are reliable enough for landslide susceptibility modeling. Accordingly, the generated susceptibility map can be used for urban planning and generating alert systems over the studied area. [ABSTRACT FROM AUTHOR]

The main purpose of this study was to compare and evaluate the performance of two multicriteria models for landslide susceptibility assessment in Constantine, north-east of Algeria. The landslide susceptibility maps were produced using the analytic hierarchy process (AHP) and Fuzzy AHP (FAHP) via twelve landslides conditioning factors, including the slope gradient, lithology, land cover, distance from drainage network, distance from the roads, distance from faults, topographic wetness index, stream power index, slope curvature, Normalized Difference Vegetation Index, slope aspect and elevation. In this study, the mentioned models were used to derive the weighting value of the conditioning factors. For the validation process of these models, the receiver operating characteristic analysis, and the area under the curve (AUC) were applied by comparing the obtained results to The landslide inventory map which prepared using the archives of scientific publications, reports of local authorities, and field survey as well as analyzing satellite imagery. According to the AUC values, the FAHP model had the highest value (0.908) followed by the AHP model (0.777). As a result, the FAHP model is more consistent and accurate than the AHP in this case study. The outcome of this paper may be useful for landslide susceptibility assessment and land use management. [ABSTRACT FROM AUTHOR]

Landslides are common occurrences along Chinese rail routes. Accordingly, the accurate determination of sliding-surface locations and the underlying causes become critical from the landslide-disaster-management and loss-mitigation viewpoints. In this study, the landslide in the K66 section of the Jiheng Railway (installed in Jinggangshan City, Jiangxi, China) was investigated. The causative factors and characteristics of the landslide were analyzed using geophysical methods as well as drilling and monitoring techniques. The results reveal that a combination of the transient electromagnetic method and electrical resistivity tomography can be used to identify the location of water-rich landslide-prone regions. When combined with the drilling information, these results reveal the tendency of landslide occurrence in regions comprising strongly weathered carbonaceous shale. Moreover, an uneven slope deformation is observed along the railway line. The seepage of surface water into the slope is revealed as the primary cause of landslides. This is because the reduced strength of the strongly weathered carbonaceous shale facilitates the formation of sliding surfaces. This paper presents a comprehensive survey plan for landslides occurring along railway routes. Furthermore, an assessment of the risk of future landslide occurrences in the investigation area is presented, thereby providing an appropriate reference for landslide prevention and loss mitigation. [ABSTRACT FROM AUTHOR]

Landslide is a uniquely dynamic large-deformation process that can present serious threat to human lives and infrastructures. The natural soil properties often exhibit inherent spatial variability, which affects the landslide behavior significantly. This paper focuses on combined Monte Carlo simulation and three-dimensional (3D) dynamic large-deformation finite-element (LDFE) analysis using the coupled Eulerian-Lagrangian method to investigate the whole runout process of landslide induced by the earthquake in spatially varying soil. The results from LDFE analysis show that the mean value of runout distance in spatially varying soil is significantly higher than that of the deterministic value obtained from a homogeneous slope due to the slope failure developed along the weakest path in soils. The mean runout distance increases and converges with increasing slope length in 3D-LDFE stochastic analysis. The advantages and necessities of 3D-LDFE analysis were illustrated by comparing it with two-dimensional (2D) LDFE analysis of landslide in spatially varying soil. The results show that the calculated mean runout distance using 3D-LDFE method is at least 16.1% higher than that calculated using 2D-LDFE analysis. Finally, a linear regression formula was established to estimate the mean runout distance of landslide due to horizontal inertia acceleration. Such a formula may facilitate the risk assessment of landslide in practical engineering. [ABSTRACT FROM AUTHOR]

Calculating the failure probability of a landslide is important in engineering related to geological process and geomorphological evolution. Strength parameters of a soil (i.e., cohesion and internal friction angle) are regarded as uncertain-but-bounded parameters. In this study, a new method is proposed for computing the landslide failure probability based on a convex set model and artificial neural network (ANN). In the new method, ANN is used to determine the limit state function of landslide stability, and the failure probability is determined using a simple iterative algorithm. The new method was applied to calculate the failure probability of the Gufenping landslide in Nanjiang, Sichuan, China. The results calculated using a Monte Carlo simulation (MCS) method confirmed that the new method accurately and quickly obtains the failure probability of a landslide. Additionally, compared with the two-dimensional calculation method, the one-dimensional analysis method overestimates the failure probability of the landslide. The results of single factor and global sensitivity analysis indicate that the average of internal friction angle is the main factor affecting the stability of the landslide. It is easy to calculate failure probability of landslides using the novel method than using the conventional methods. [ABSTRACT FROM AUTHOR]

The Nepal Himalayas is one of the world's most active mountain belts and home to widespread natural hazards of various types, including landslides, which claim numerous lives and result in massive property damage in the country. Landslides occur due to the combined effects of seismic activity, monsoon rainfall, and improper land-use practices. The prevention and mitigation of landslides are challenging for countries such as Nepal. However, low-cost techniques such as bioengineering combined with affordable early warning systems have been implemented in recent decades. The Methum landslide in Lalitpur district, central Nepal, was selected as a case study to investigate the landslide geomorphology along with triggering factors and to evaluate the effectiveness of a landslide early warning system (LEWS). Multiple field visits were conducted to learn the patterns of landslide evolution, assess landslide risk, and identify potential triggers. This study analyzed aerial photos, satellite images, and precipitation records. Heavy rainfall and past earthquake events have been major landslide triggers, while sloped terrace farming has acted as a preparatory factor. LEWS, installed, measures rainfall, soil moisture, and displacement activity and generates an alarm to alert nearby inhabitants if any of these parameters exceed the threshold set. This monitoring system is a cost-effective technique and exemplifies the reduction of landslide risk at the community level in the landslide-prone mid-hills of Nepal. [ABSTRACT FROM AUTHOR]

The present work aimed to assess the feasibility of three methods, namely, analytical hierarchy process, frequency ratio and binary logistic regression, that are used for landslide susceptibility analysis. Twelve causal factors such as slope, relative relief, road buffer, fault/fold and thrust (FFT) buffer, topographic wetness index (TWI), aspect, stream power index (SPI), drainage buffer, profile curvature, land used/land cover (LULC), soil and geology as well as a landslide inventory were retrieved using multi-source spatial data. Correlation between landslides and causative factors was estimated using frequency ratio on a geospatial platform with 234 landslide locations, extracted using field visits and remote sensing imageries and found to be reasonably influencing landslides in the study region. These factors were then used in all three landslide susceptibility models to calculate and generate a landslide susceptibility map (LSM). LSM of the study region Mao-Maram (tehsil) of Manipur was generated with five classes: very low, low, medium, high, and very high. Cumulative percentage curve and receiver operating characteristic (ROC) approaches were used for validation of the model performance. Cumulative percentage curve area under curve (AUC) accuracy for frequency ratio, analytical hierarchy process and binary logistic regression were found to be 77.24%, 73.22%, and 80.32%, respectively. ROC curve under AUC for frequency ratio 88.7%, analytical hierarchy process 77.4%, and binary logistic regression 90%. A landslide susceptibility map derived from every three models can be helpful for city or village planners for planning, development, mitigation, and disaster prevention. [ABSTRACT FROM AUTHOR]

There has been an increasing demand for detailed and accurate landslide maps and inventories in disaster-prone areas of subtropical and temperate zones, particularly in Asia as they can mitigate the impacts of landslides on social infrastructure and economic losses. Hence, in this study, models using automatically constructed high-performing convolutional neural network (CNN) architectures for landslide detection were applied and their outcomes were compared for landslide susceptibility mapping at the Kii peninsula, Japan. First, a total of 38 landslide and 63 non-landslide points were identified and divided into 70% and 30% of training and validation datasets, respectively. Eight landslide influence factors were used: slope angle, eigenvalue ratio, curvature, underground openness, overground openness, topographic witness index, wavelet, and elevation. These factors were selected using a 1-m DEM, which is easy to acquire and process data. Experimental results of model evaluation using receiver operating characteristics (ROC), area under the curve (AUC), and accuracy showed that the optimal models (ROC = 96.0%, accuracy = 88.7%) were more accurate than initial models (ROC = 91.1%, accuracy = 80.7%) in predicting landslides spatially. Furthermore, the landslide susceptibility mapping is consistent with the trends in the distribution of gentle slopes and knick lines unique to the study area and can be used as a powerful method for predicting landslides in future. [ABSTRACT FROM AUTHOR]

Some properties of landslide soils are generally recognized as inherently spatially variable because of the heterogeneity of natural geological deposits. Fluctuations in water levels of the Three Gorges Reservoir cause the depth of the groundwater table at landslide toes to change, resulting in fluctuations in the soil water content and significant soil degradation. The spatial variability and temporal weakening of soil properties should be incorporated into the landslide stability analysis. More importantly, rational deformation prediction and stability analysis of landslide numerical models require an advanced soil constitutive model. Herein, taking the Tangjiao landslide as a case study, the statistical characteristics of shear strength parameters were studied based on valuable soil test data. Then, the spatial variability of these parameters was modeled as a random field for the sliding mass. A hypoplastic constitutive model for clay was used to simulate the landslide deformation over 6 years caused by precipitation and changes in the reservoir water level. In addition, soil degradation induced by the fluctuating groundwater level was accounted for in key model parameters on the basis of experimental results. Eventually, the non-intrusive random finite element method was used to compute the landslide deformation and stability for the random field model. Landslide simulation of the deterministic model ignoring the spatial variation of soil parameters was also performed. Simulation results indicate that the difference in the landslide safety factor between the deterministic and random field models is up to 0.14 for the leading edge and up to 0.12 for the trailing edge. Random field models predict greater deformation and less stability than the deterministic model, suggesting that they are more conservative in this specific case. This research can serve as a useful reference for probabilistic stability analyses of riverside landslides considering soil spatial and temporal variability, which may be quantified more precisely in future research based on multi-source data inversion. [ABSTRACT FROM AUTHOR]

This effort is made to develop a landslide hazard map and perform the kinematic slope instability analysis of the critical sections at the Dasu dam site and its reservoir area. The study area is located at the Indus River in the Kohistan District within the highly elevated Himalayan terrain. The fragile geologic setup and high tectonic activity make this region favorable to produce frequent slope failures and landsliding events. First, a regional level landslide inventory map was created using GIS and remote-sensing tools. For this a hill shaded topographic map was created by overlapping a topographic map on to a hillshade image extracted from satellite based PALSAR Digital Elevation Model (DEM) with 12.5 m spatial resolution in GIS environment. Numerous past landsides were delineated using the expert driven knowledge of anomalous topographic expression like, crenulated contours, divergent contours, isolated topographic knobs, extended topographic ridges and arcuate headscarf evacuation areas. Total 120 landslides were marked, later a field visit was carried out for the validation of this exercise. The overall efficiency of this work was 85% which indicates that the GIS and remote sensing tools utilized for this study are reliable and effective in producing regional landslide inventory maps. Moreover, the scanline surveys were conducted at few selected locations to generate discontinuities data for the evaluation of slope instability issues by using kinematic analysis in DIPS 7.0 software. The study successfully highlights the usefulness of GIS and RS tools and in combination with kinematic analysis for landslides hazard assessment for mega projects. The outcomes of this research could serve as a guide in risk assessment and mitigation work in future to stabilize the critical slopes near Dasu dam site and along its reservoir area. [ABSTRACT FROM AUTHOR]

Landslide susceptibility maps are modelled either by knowledge-driven methods (qualitative) or data-driven methods (quantitative) depending on apriori expert knowledge or complete landslide inventory data, respectively, which hinders land regulatory authorities from taking quick decisions for land use planning at a regional scale. To overcome these limitations, a new modelling methodology is proposed which can perform equally with the existing established models and needs minimum knowledge of geo-environmental variables without any model testing data. In this method, common variances of the geo-environmental variables are extracted from initially equally weighted variables and are termed as equal weight method (EWM). The extracted common variances are used to weight the variables for the preparation of a landslide susceptibility map on a regional scale. The output of the model can be interpreted to understand the inter-relationship of different geo-environmental variables along with the identification and prioritization of variables on model performance by delineating the non-performing variables. The proposed modelling methodology is tested at the eastern Himalayan road corridor, the result of which is compared with the results of the analytical hierarchy process (qualitative) and frequency ratio (quantitative) modelling methods by area under curve (AUC) of receiver operating characteristic (ROC) curves and cost analysis. AUC of ROC curves for the analytical hierarchy process, frequency ratio method and equal weight method models are 0.755, 0.809 and 0.774, respectively, indicating little performance differences. Cost analysis of all models shows a maximum bounding cost of 0.4 indicating that all models are cost-effective. The study concludes that the equal weight method is quick, robust and cost-effective at par with other well tested knowledge-driven and data-driven methods. [ABSTRACT FROM AUTHOR]

Landslide risk analysis is a common geotechnical evaluation and it aims to protect life and infrastructure. In the case of sensitive clay zones, landslides can affect large areas and are difficult to predict. Here we propose a methodology to determine the landslide hazard across a large territory, and we apply our approach to the Saint-Jean-Vianney area, Quebec, Canada. The initial step consists of creating a 3D model of the surficial deposits of the target area. After creating a chart of the material electrical resistivity adapted for eastern Canada, we applied electric induction to interpret the regional soil. We transposed parameter values obtained from the laboratory to a larger scale, that is to a regional slope using the results of a back analysis undertaken earlier, on a smaller slide within the same area. The regional 3D model of deposits is then used to develop a zonation map of slopes that are at risk and their respective constraint areas with the study region. This approach allowed us to target specific areas where a more precise stability analysis would be required. Our methodology offers an effective tool for stability analysis in territories characterized by the presence of sensitive clays. [ABSTRACT FROM AUTHOR]

Landslides in the Himalayan region are primarily controlled by natural parameters, including rainfall, seismic activity, and anthropogenic parameters, such as the construction of large-scale projects like road development, tunneling and hydroelectric power projects and climate change. The parameters which are more crucial among these are a matter of scientific study and analysis. This research, taking Solan district, Himachal Pradesh, India, as the study area, aims to assess the impact of anthropogenic activities on landslide susceptibility at a regional scale. Landslide distribution was characterized into two groups, namely Rainfall-Induced Landslide (RIL) and Human-Induced Landslide (HIL) based on triggering factors. Multiple data such as slope angle, aspect, profile curvature, distance to drainage, distance to lineament, lithology, distance to road, normalized difference vegetation index (NDVI) and land use land cover (LULC) have been considered for delineating the landslide susceptibility zonation (LSZ) map. The effect of anthropogenic activities on landslide occurrences has been examined through the distribution of landslides along national highways and land use land cover changes (LULCC). Two sets of LSZ maps with a LULC of time interval covering five years (2017 & 2021) were prepared to compare the temporal progression of LULC and landslide susceptibility during the five years. The results indicated the significant impact of anthropogenic activities on the landslide susceptibility. LSZ map of the year 2021 shows that 23% area falls into high and very high susceptible classes and 48% area falls into very low and low susceptibility classes. Compared to LSZ map of 2017, high and very high susceptible classes have been increased by 15%, whereas very low and low susceptible classes have been reduced by 7%. The present case study will help to understand the potential driving parameters responsible for HIL and also suggest the inclusion of LULC in landslide susceptibility analysis. The study will demonstrate new opportunities for research that could help decision-makers prepare for future disasters, both in the Indian Himalayan region and other areas. [ABSTRACT FROM AUTHOR]

Landslide vulnerability prediction maps are among the most important tools for managing natural hazards associated with slope stability in river basins that affect ecosystems, properties, infrastructure and society. Landslide events are among the most hazardous patterns of slope instability in the coastal mountains of Syria. Thus, the main goals of this research are to evaluate the performance of three different statistical outputs: Frequency Ratio (FR), Statistical Index (SI) and Index of Entropy (IoE) and therefore map landslide susceptibility in the coastal region of Syria. To this end, we identified a total of 446 locations of landslide events, based on the preliminary inventory map derived from fieldwork and high-resolution imagery surveys. In this regard, 13 geo-environmental factors that have a high influence on landslides were selected for landslide susceptibility mapping. The results indicated that the FR method outperformed the SI and IoE models with a high AUC of 0.824 and better adaptability, followed by the SI with 0.791. According to the SCAI values, although the FR model achieved the best reliability, the other two models also showed good capability in determining landslide susceptibility. The result of FR-based modelling showed that 18.51 and 19.98% of the study area fall under the high and very high landslide susceptible categories, respectively. In the map generated by the SI method, about 36% of the study area is classified as having high or very high landslide sensitivity. In the IoE method, whereas 14.18 and 25.62% of the study area were classified as "very high susceptible" and "high susceptible," respectively. The relative importance analysis demonstrated that the slope aspects, lithology and proximity to roads effectively motivated the acceleration of slope material instability and were the most influential in both the FR and SI models. On the other hand, the IoE model indicated that the proximity to faults and roads, along with the lithology factor, were important influences in the formation of landslide events. As a result, the statistical bivariate models-based landslide mapping provided a reliable and systematic approach to guide the long-term strategic planning procedures in the study area. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Seismic slope stability analysis at regional scale (e.g., landslide hazard mapping, infrastructure slope management) is essential in areas that are susceptible to earthquake. Analytical infinite slope model is well suited and widely used for regional analyses which involve hundreds and thousands of slopes. Use of infinite slope model implicitly assumes shallow translational failure mechanism. However, earthquake can also cause deep rotational failure, which is more destructive and should also be considered. This study aims to develop closed-form solutions that can be efficiently used for seismic slope stability analysis at regional scale considering deep rotational failure mechanism. The existing research is critically reviewed first, and their shortcomings are identified. In contrast to the conventional "two-step" approach, a more efficient and versatile "one-step" approach is proposed in this study. The "one-step" approach can be used to calculate factor of safety for pseudo-static analysis and yield coefficient for displacement-based seismic analysis. To consider the effects of uncertainty from soil properties and seismic loading, the "one-step" approach is further extended for probabilistic analysis and application is demonstrated through a case study. The efficiency and versatility of the proposed "one-step" approach make it well suited for regional seismic slope stability analysis. [ABSTRACT FROM AUTHOR]

The upper flanks of stratovolcanoes usually exhibit steep slopes affected by fumarolic or hydrothermal activity. These conditions commonly promote rock instability, triggering rock avalanches and landslides. Quiescent and active stratovolcanoes transected by fractures and faults are further susceptible to these phenomenon, especially when they are located in tropical regions, where heavy rainfall regimes tend to saturate the volcanic material and promotes landslides. One example of such volcanoes is the Tacaná Volcanic Complex located at the Mexico-Guatemala border. It consists of three NE-SW aligned volcanoes (Chichuj, Tacaná, and San Antonio) and the Ardillas Dome. Just recently, the eastern portions of Ardillas Dome and San Antonio volcano have been affected by rockfalls and landslides toward the southern flank of the Complex, particularly to the Maxaum ravine. Field surveys showed tension fractures, landslide scars, fumaroles and volcanic vents along an important NE-SW fissure that has been growing between these two volcanic structures. To assess a future landslide within this zone, we delineate an area prone to collapse, according to the structural and morphometric analysis. We simulate a set of five gravitational flows of different volumes (1 × 105 to 2.5 × 107 m3) with the numerical code Titan2D varying the source area of the landslides. The simulated landslides showed that independently of the volume involved or source point of the landslide, it will be channeled within the Maxaum ravine and travel a maximum distance of up to 7 km from the source. The simulated flows moved at maximum velocities of ~ 41 m/s and were dispersed over a surface area of ~ 17 km2 with variable thicknesses (0.5–7 m at the affected villages). The most voluminous landslides (2.5 × 107 m3) will impact at least eight villages inhabited by ~ 2000 people on the southern flanks of the Complex. By analyzing the potential building damage caused by the largest simulated landslide (intensity index, IDF), two localities would have major structural damages in their dwellings and two would be completely destroyed; meanwhile, another three villages would suffer minor structural damages and one would experience minor sedimentation. Although the outcomes described here represent the first evaluation of small-volume landslide hazards at the Complex, specifically at the Ardillas Dome, our maps are useful tools for the preparedness for the Civil Protection authorities and the population at risk. Thus, a detailed landslide hazard assessment is needed, taking into account the present state of the Tacaná Volcanic Complex as well as the geologic and climatologic setting of the volcano, and a full stability analysis. [ABSTRACT FROM AUTHOR]

Assessing the hazard of potential landslides is crucial for developing mitigation strategies for landslide disasters. However, accurate assessment of landslide hazard is limited by the lack of landslide inventory maps and difficulty in determining landslide run-out distance. To address these issues, this study developed a novel method combining the InSAR technique with a depth-integrated model. Within this new framework, potential landslides are identified through InSAR and their potential impact areas are subsequently estimated using the depth-integrated model. To evaluate its capability, the proposed method was applied to a landslide event that occurred on November 3, 2018 in Baige village, Tibet, China. The simulated results show that the area with a probability of more than 50% to be affected by landslides matched the real trimlines of the landslide and that the accuracy of the proposed method reached 85.65%. Furthermore, the main deposit characteristics, such as the location of maximum deposit thickness and the main deposit area, could be captured by the proposed method. Potential landslides in the Baige region were also identified and evaluated. The results indicate that in the event of landslides, the collapsed mass has a high probability to block the Jinsha River. It is therefore necessary to implement field monitoring and prepare hazard mitigation strategies in advance. This study provides new insights for regional-scale landslide hazard management and further contributes to the implementation of landslide risk assessment and reduction activities. [ABSTRACT FROM AUTHOR]

The majority of landslides in the Indian Himalayas are triggered by rainfall and earthquake. The geology and geomorphology also play an important role and constitute the major predisposing factor controlling landslides. Additionally, active tectonics modify the landscape, impacting the occurrence and distribution of landslides. The objective of this study is to investigate the role of some of the major predisposing factors to assess landslide patterns in the Yamuna River Basin as it is prone to recurrent landslide hazards. The approach mainly deals with the application of geographical information system-based statistical analysis and quantifying frequency-area distribution of rainfall-triggered landslides for the years 2015 to 2017. The analysis shows that the frequency of landslides is highest in the slope range 40°–70° and the mean landslide size is higher in the slope range 0°–20° and 30°–40°, remaining constant on further increase in slope angle. The study shows that some critical slope range controls both the frequency and size of landslides. Landslides were mostly restricted to south facing slopes. About 91% of the landslide occur in the drainage density range of 1.2 to 2.1 km/km2 while landslides show very low occurrence in either lower (<1.2) or higher (>2.1) drainage density. Frequency-size distribution analysis of landslides depicts a distinct cutoff point but an indistinct rollover point. Under-sampling of small landslides can be a reasonable cause for this. Like other studies, the present data fits very well for medium and large landslides following an inverse power law with a scaling exponent of −2.16. Around 80% of landslides are smaller than the cutoff size and 20% larger than this value. The geology and tectonics of Lesser Himalayas and Sivaliks offer favorable conditions for frequent landslides. Areas such as Dehradun, Uttarkashi, Sirmaur, Shimla, and Solan are very prone to landslide activity owing to the presence of poorly consolidated sandstone, conglomerate, quartzites, phyllites, etc. Moreover, the majority of larger landslides are in the Dehradun district where precipitation, drainage density, and lithology are highly favorable to trigger large events. The present study has identified the critical range of predisposing factors responsible for landslides and presents the frequency-size distribution of landslides in the Yamuna River Basin. These are the key inputs in landslide susceptibility mapping and risk assessment. Therefore, the findings from the present analysis can be very helpful in landslide risk reduction and landslide hazard zonation and probably plan critical locations for installation of early warning signals. [ABSTRACT FROM AUTHOR]

The primary objective of the present research is to apply and compare the performance of evidential belief function (EBF)-based logistic model trees (LMTs) and multiclass alternate decision trees (LADTrees) in landslide susceptibility mapping in Xiaojin County, China. Firstly, 328 landslides were mapped in the study area. Then, 70% of landslide points were used as training samples randomly, and the remaining 30% were intended for validation samples. For the study area, 12 landslide-related conditioning factors were identified, for instance, plan curvature, profile curvature, elevation, slope angle, slope aspect, normalized difference vegetation index (NDVI), topographic wetness index (TWI), land use, lithology, distance to river soil, and distance to roads. The following procedure was to map landslide susceptible regions through EBF, LADTree and LMT models. Finally, the receiver operating characteristic (ROC) curve was utilized to contrast and test the capacity of the three models. The success rates with the training dataset were 0.880, 0.877 and 0.886 for the EBF, LADTree and LMT models, respectively. In addition, their prediction rates with the validation dataset were 0.846, 0.861 and 0.865, respectively. The results could provide references for disaster management and land-use planning. [ABSTRACT FROM AUTHOR]

Landslide hazard mapping is essential for disaster reduction and mitigation. The hazard map produced by the spatiotemporal probability analysis is usually static with false-negative and false-positive errors due to limited data resolution. Here we propose a new method to obtain dynamic landslide hazard maps over the Wushan section of the Three Gorges Reservoir Area by introducing the ground deformation measured by the spaceborne Copernicus Sentinel-1 synthetic aperture radar (SAR) imagery collected from 9/30/2016 to 9/13/2017. We first determine the spatial probability of landslide occurrence predicted by the support vector machine algorithm. We also conducted the statistical analysis on the temporal probability of landslide occurrence under various rainfall conditions (0, 0–50, 50–100, and > 100 mm for the antecedent 5-day total). We initialize a preliminary landslide hazard map by combining the spatial and temporal landslide probabilities. Meanwhile, the ground deformation velocities during the representative dry and wet seasons can be extracted from multi-temporal interferometric SAR (MT-InSAR). Thereafter, the landslide hazard map can be finalized by an empirical assessment matrix considering both the preliminary landslide hazard map and deformation velocities. Our results demonstrate that false-negative and false-positive errors in the landslide hazard map can be effectively reduced with the assistance of the deformation information. Our proposed method can be used to assess the dynamic landslide hazard at higher accuracy. [ABSTRACT FROM AUTHOR]

The present research was conducted in the town of Debre Werk, East Gojjam, North West Ethiopia. This study aimed to apply and validate Slope Stability Susceptibility Evaluation Parameter (SSEP) rating system and produce a landslide hazard zonation (LHZ) map of the area. This rating system was done by considering the parameters of intrinsic and external triggering factors that cause landslides. Systematic and detailed fieldwork had been undertaken as a justification. Secondary data, on the other hand, were required to define the general conditions of the area and to gain a thorough understanding of the field of study. Ratings for intrinsic parameters in the SSEP system include slope morphometry, relative relief, slope content, geological structures/discontinuities, land use land cover, groundwater, and external parameters include erosion, seismicity, and manmade activities. Individual facet-wise ratings for intrinsic causative factors and external triggering factors ratings are summarized to evaluate the landslide hazard zonation of an environment. The sum of all causative parameter ratings will give evaluated landslide hazards. Therefore, the research was carried out by dividing the study area into 70 facets. A landslide inventory including 85 landslide activities was prepared. Thus, 23, 39, and 23 landslide activities were identified as active landslide, past landslides, and signs of landslide, respectively. The delineated 70 facets were categorized into three landslide hazard zones. There are about 73.3 km2 (27.2%) of the study area within the low hazard zone, 140.8 km2 (52.1%) within the moderate hazard zone, and the remaining 55.9 km2 (20.7%) within the high hazard zone. Based on the findings of SSEP, it can be deduced that the present research area is highly susceptible to landslides and requires special attention during rainy seasons. Finally, the validity of the prepared LHZ map was checked by overlaying the inventory map over the produced LHZ map. The results were compared with the actual active landslide activity data in the area. The overlay analysis reveals that out of a total of 23 active landslide locations, 19 (82.6%) fall within the 'high hazard zone,' whereas the remaining 4 (17.4%) fall within the 'moderate hazard zone.' The validation of the prepared LHZ map suggests that the applications of the SSEP rating system provide a good basis to produce produced LHZ maps. [ABSTRACT FROM AUTHOR]

The evaluation of landslide runout behavior plays a vital role in the risk assessment of landslides. In this study, the runout behavior of a landslide that occurred on December 10, 2019, in the Three Gorges Reservoir region, China, known as the Kamenziwan landslide, is studied with the discrete element method. A field survey is first conducted to study the geological characteristics and failure mechanism of this landslide. The survey results indicate that this landslide is possibly caused by the long-term fluctuation of the reservoir water level. Then, a three-dimensional (3-D) numerical model of this landslide is constructed on the basis of the terrain data obtained before and after the landslide occurrence. The critical input parameters to the numerical model, in terms of the contact micro-parameters among particles, are calibrated with the machine learning method and uniaxial compression tests. To depict the effectiveness of the built numerical model, the simulation results are compared with the video snapshots taken by a local resident. Finally, the runout behavior and the energy transformation of the landslide are analyzed based on the validated numerical model. [ABSTRACT FROM AUTHOR]

Landslide and related mass movement activities are common and one of the most destructive natural hazards in the mountainous terrain including the Himalayas. Of the 11 administrative states in the Indian Himalayan region, the state of Uttarakhand has witnessed enhanced activities of these phenomena. It is therefore essential to understand the regional scale landslide susceptibility assessment of the state and in the present study, landslide susceptibility mapping for the entire state has been carried out using bivariate weight of evidence and information value methods which depict that around 51% of the area is located in the high and very high landslide susceptible zones, 22–23% in the moderate and ~26–27% in the low and very low landslide susceptible zones, and slopes ranging between 40° and 60°, located at an elevation of 2000–4000 m, facing towards southern sides and covered with limestone, gneiss, quartzite and phyllite, have higher propensity towards development of landslides in the region. [ABSTRACT FROM AUTHOR]

Landslides are one of the most important geohazards. In 2004–2016, more than 55,000 people lost their lives to landslides and this does not include deaths caused by seismically triggered landslides. Overall losses were estimated to be at USD 20 billion annually. The lives of many could be saved if more had been known regarding forecasting and mitigation. Studies have shown an increasing trend in landslides occurrence and fatalities. Over recent years, landslide risk assessment has been carried out extensively by geo-scientists worldwide. This review concentrates on the societal risks posed by landslides in various countries and the risk criteria used by various parts of the world in assessing landslide risks. The landslide risk tolerance criteria are strongly governed by utilitarian concerns i.e. financial power and the need for development. In developing countries, surprisingly high levels of tolerance are proposed for landslides. The risk criteria of Hong Kong and that of the Australian Geomechanics Society are widely employed in many countries. Although various risk tolerance levels have been proposed by various nations, many of them are still not being applied in their real-life scenarios. The procedures for setting risk criteria call for a wide agreement between geo-scientists, government decision makers, and the community. Risk criteria should be developed locally with historical landslide inventory, public perception, and engineering aspects being considered. [ABSTRACT FROM AUTHOR]

In the present study, landslide hazard, vulnerability and the risk assessment of the Himalayan township Mussoorie, located in the lower part of the Lesser Himalaya has been undertaken. The area famous for tourism constitutes > 7000 buildings with ~ 30,000 habitations. Four bivariate statistical approaches viz. Weight of evidence (WoE), Frequency ratio (FR), Yule Coefficient (YC), and Information Value (InV) were used for landslide hazard assessment, and all these approaches with 75%—80% success and predication rate are found to be acceptable for landslide hazard mapping. The analyses indicate that the Nagar Palika Parisad ward, Library ward, Happy valley ward and Bhadraj ward exhibit the maximum area falling under the high and very high landslide hazard zones, whereas Landaur ward, Jalkii ward and Indra Colony ward exhibit a greater part of the area in the low and very low hazard zones. On the basis of the six elements at risk present in the study area viz. settlement, crop land, water body, roads, barren and degraded land, and dense forest, the ~ 40% of the area is very high and high vulnerable, ~ 11% moderate, and ~ 49% is low and very low vulnerable. High and very high vulnerable zones are located in the west and central portion mainly because of higher habitation and more anthropogenic activities. Finally, risk map prepared intersecting hazard and vulnerability maps exhibits that Nagar Palika Parisad ward has the largest part (~ 41%) of the area with 37% buildings falling in the high and very high risk zones, whereas the Landour ward is the safest ward having ~ 83% area in the low and very low risk. Overall ~ 1604 buildings with habitation of ~ 8,000 persons in the township, are prone to high and very high landslide risk. The results of this study may be utilized for further development and planning in the area. [ABSTRACT FROM AUTHOR]

Landslides can cause extensive damage, particularly those triggered by earthquakes. The current study used back propagation of an artificial neural network (ANN) to conduct risk studies on landslides in the area affected by the Manjil-Rudbar earthquake in Iran in 1990 (M = 7.7). Newmark displacement analysis was used to develop an earthquake-induced landslide hazard map for the blocks representing Chahar-Mahal and Chalkasar near the earthquake epicenter, an area of 309 km2. The input data included soil cohesion, soil friction angle, unit weight of soil, unit weight of water, distance from hypocenter, slope, and earthquake magnitude as effective parameters for landslide occurrence. The hazard map was compared with an inventory map and other research findings. The results indicated that the landslides predicted by ANN covered 50% of the inventory map of the study area (2088 of 4097 slide cells). The results of the current study suggest that the ANN method is relatively efficient for accurate prediction of landslides. [ABSTRACT FROM AUTHOR]

Uncertainties in parameters of landslide susceptibility models often hinder them from providing accurate spatial and temporal predictions of landslide occurrences. Substantial contribution to the uncertainties in landslide assessment originates from spatially variable geotechnical and hydrological parameters. These input parameters may often vary significantly through space, even within the same geological deposit, and there is a need to quantify the effects of the uncertainties in these parameters. This study addresses this issue with a new three-dimensional probabilistic landslide susceptibility model. The spatial variability of the model parameters is modeled with the random field approach and coupled with the Monte Carlo method to propagate uncertainties from the model parameters to landslide predictions (i.e., factor of safety). The resulting uncertainties in landslide predictions allow the effects of spatial variability in the input parameters to be quantified. The performance of the proposed model in capturing the effect of spatial variability and predicting landslide occurrence has been compared with a conventional physical-based landslide susceptibility model that does not account for three-dimensional effects on slope stability. The results indicate that the proposed model has better performance in landslide prediction with higher accuracy and precision than the conventional model. The novelty of this study is illustrating the effects of the soil heterogeneity on the susceptibility of shallow landslides, which was made possible by the development of a three-dimensional slope stability model that was coupled with random field model and the Monte Carlo method. [ABSTRACT FROM AUTHOR]

Landslides are natural hazards that represent a huge economic burden and cause the loss of human life around the world. In countries such as Colombia, the mass movement events that cause the highest number of deaths and economic losses are often related to river or stream flooding caused by landslides in basins. Therefore, it is necessary to develop tools that estimate and assess landslide risk in such areas. This study presents a methodology to assess the risk associated with landslides in streams or river basins. The hazard posed by landslides is evaluated considering probabilistic methods that include the effects of rainfall and earthquakes. In addition, this study assesses the probability of a sliding mass reaching riverbeds and the probability of riverbed obstruction. Vulnerability is then estimated using impact curves based on the obstruction height. Finally, risk is estimated as the probability that economic losses occur along the riverbed. This methodology is based on probability methods, such as the first-order second-moment (FOSM) method, and the punctual estimates method (PEM). The methodology was applied in the La Liboriana River basin, in the municipality of Salgar in the northwestern Colombian Andes. On May 18, 2015, this mountainous and tropical area suffered a flash flood caused by landslides in the basin, which killed more than 100 inhabitants and caused infrastructure damage and significant economic losses. The results suggest that the proposed method coherently assesses the hazard posed by landslides and that the expected losses are comparable with the records from previous events. [ABSTRACT FROM AUTHOR]

Landslides are downslope movements of rock and soil materials under the influence of gravity, and often these are triggered by various causes, and the factors that govern the landslide occurrence in Brunei Darussalam, Borneo, have not been investigated in any detail, although landslide hazards are the second most damaging hazards in the country. The equatorial tropical monsoon conditions contribute to the cause of landslides in the sultanate, and our mapping of landslide sites in the Brunei-Muara region has revealed a number of key factors that initiate and influence the formation and nucleation of landslides. And these are mainly related to extent of urbanisation, amount and intensity of rainfall, loss of vegetation cover, slope and geological factors. These factors may or may not work together for a particular landslide event, but the occurrence of landslides has been linked to these factors. Gravitational active faults were also mapped at three landslide sites and related to creep. Google, and 30 m shuttle radar topography satellite images, followed by detailed fieldwork with the aid of unmanned aerial vehicle tool, were employed to map the topographic, structural, bedrock, and landslide details and to differentiate the types of landslides. The landslide and rainfall data were obtained from the National Disaster Management Centre and Geotechnical and Geological Section of the Public Works Department and used in combination with the bedrock and slope maps. Our work should be used by local administrative departments to plan any future anthropogenic activity in the region. [ABSTRACT FROM AUTHOR]

Understanding the effect of acid rain to landslides is crucial for a better landslide risk assessment. This work aims to reveal the unsuspected but key role of acid rain in Panzhihua airport landslide, China. Firstly, we propose a hypothesis that acid rain may aggravate the slaking behavior of mudstone at weak interlayer and make it more fragmented, eventually further reducing its shear strength and predisposing the Panzhihua airport landslide. Subsequently, mudstone samples are subjected to slaking durability test, respectively, using water with a pH of 7 and two dilute hydrochloric acid solution with pH of 5 and 3. Slaking durability index (Idn) is adopted aiming to quantitatively evaluate the impact of acid rain on the slaking. Moreover, the mechanisms of acid rain affecting the slaking behavior of mudstone are revealed by (1) analyzing cation compositions changes in different pH slaking fluid and (2) observing micro-structure change of mudstone-chip before and after acid rain treatment. Finally, three works are conducted as evidences to prove that acid rain indeed plays a key role in the occurrence of Panzhihua airport landslide, including (1) analysis of the link between the slaking behavior of mudstone and its shear strength, (2) comparison of cations between spring water at the edge of the toe of landslide and acid rain, and (3) comparison of mineral contents of mudstone samples collected from different locations. These findings have implications for comprehensively analyzing the formation mechanism of landslide in acid rain area (such as Europe, North America, and China). [ABSTRACT FROM AUTHOR]