Landslide Susceptibility Research Articles

Identification of Potential Landslides in the Gaizi Valley Section of the Karakorum Highway Coupled with TS-InSAR and Landslide Susceptibility Analysis

Landslides have become a common global concern because of their widespread nature and destructive power. The Gaizi Valley section of the Karakorum Highway is located in an alpine mountainous area with a high degree of geological structure development, steep terrain, and severe regional soil erosion, and landslide disasters occur frequently along this section, which severely affects the smooth flow of traffic through the China-Pakistan Economic Corridor (CPEC). In this study, 118 views of Sentinel-1 ascending- and descending-orbit data of this highway section are collected, and two time-series interferometric synthetic aperture radar (TS-InSAR) methods, distributed scatter InSAR (DS-InSAR) and small baseline subset InSAR (SBAS-InSAR), are used to jointly determine the surface deformation in this section and identify unstable slopes from 2021 to 2023. Combining these data with data on sites of historical landslide hazards in this section from 1970 to 2020, we constructed 13 disaster-inducing factors affecting the occurrence of landslides as evaluation indices of susceptibility, carried out an evaluation of regional landslide susceptibility, and identified high-susceptibility unstable slopes (i.e., potential landslides). The results show that DS-InSAR and SBAS-InSAR have good agreement in terms of deformation distribution and deformation magnitude and that compared with single-orbit data, double-track SAR data can better identify unstable slopes in steep mountainous areas, providing a spatial advantage. The landslide susceptibility results show that the area under the curve (AUC) value of the artificial neural network (ANN) model (0.987) is larger than that of the logistic regression (LR) model (0.883) and that the ANN model has a higher classification accuracy than the LR model. A total of 116 unstable slopes were identified in the study, 14 of which were determined to be potential landslides after the landslide susceptibility results were combined with optical images and field surveys. These 14 potential landslides were mapped in detail, and the effects of regional natural disturbances (e.g., snowmelt) and anthropogenic disturbances (e.g., mining projects) on the identification of potential landslides using only SAR data were assessed. The results of this research can be directly applied to landslide hazard mitigation and prevention in the Gaizi Valley section of the Karakorum Highway. In addition, our proposed method can also be used to map potential landslides in other areas with the same complex topography and harsh environment.

ПРИМЕНЕНИЕ КОЛИЧЕСТВЕННОГО МЕТОДА В ИССЛЕДОВАНИИ ОПОЛЗНЕВОЙ ВОСПРИИМЧИВОСТИ СЕЛЕОПАСНОГО БАССЕЙНА РЕКИ АГСУЧАЙ

The article uses a quantitative method to analyze dangerous landslide processes occurring in the mudflow-prone Agsuchay River basin, taking into account the active development of tourism and recreational activities in the Shamakhi-Ismayilli region. In order to identify landslide susceptibility and the potential manifestation of landslides, the “weight” of 9 factors associated with landslides was determined, including hypsometry, slope angles (slope steepness), slope exposure, geological structure (lithology), distance from faults, average annual precipitation, distance to the erosion networks, distance to roads and land use. By summing up all the landslide formation factors without exception and multiplying them by their “weight”, a map of landslide susceptibility of the mudflow-prone Agsuchay River basin was compiled. The reliability of the obtained models was assessed using the AUC ROC (area under the error curve) analysis, which showed a fairly high efficiency (up to 72 %) of the applied method.

Application of Bivariate Statistical Method for Landslide Susceptibility Assessment along Srinagar-Bandipora Highway, North Kashmir Himalaya

The present study aims to create and evaluate a map indicating the potential of landslides along the Srinagar-Bandipora highway in the North Kashmir Himalaya region. Therefore, the study utilized the Bivariate Statistical Relative effect (RE) method to establish the correlation between casual factors (CFs) contributing to landslides and the actual landslide incidences. Additionally, fifteen different CFs were analyzed within a Geographic Information System (GIS) framework to assess the impact of these factors on landslides. Further, a comprehensive GIS-based inventory map was compiled, incorporating both primary and secondary data sources, to identify landslide locations. A landslide susceptibility zonation (LSZ) map was developed by integrating all identified landslide CFs along with their respective effect function values. Moreover, the results revealed that 21.06% of the study area was classified as having a very high to high susceptibility to landslides. To validate the accuracy of the produced map, 30% of landslide validation data was employed and the receiver operating characteristics (ROC) curve method was utilized, yielding an excellent result with an area under curve (AUC) value of 0.919 for the study area. Consequently, the findings of this study hold significant potential for mitigating landslide-related hazards and guiding land use planning projects.

Landslide Susceptibility Assessment Based on Multisource Remote Sensing Considering Inventory Quality and Modeling

The completeness of landslide inventories and the selection of evaluation models significantly impact the accuracy of landslide susceptibility assessments. Conventional field geological survey methods and single remote-sensing technology struggle to reliably identify landslides under complex environmental conditions. Moreover, prevalent landslide susceptibility evaluation models are often plagued by issues such as subjectivity and overfitting. Therefore, we investigated the uncertainty in susceptibility modeling from the aspects of landslide inventory quality and model selection. The study focused on Luquan County in Yunnan Province, China. Leveraging multisource remote-sensing technologies, particularly emphasizing optical remote sensing and InSAR time-series deformation detection, the existing historical landslide inventory was refined and updated. This updated inventory was subsequently used to serve as samples. Nine evaluation indicators, encompassing factors such as distance to faults and tributaries, lithology, distance to roads, elevation, slope, terrain undulation, distance to the main streams, and average annual precipitation, were selected on the basis of the collation and organization of regional geological data. The information value and two coupled machine-learning models were formulated to evaluate landslide susceptibility. The evaluation results indicate that the two coupled models are more appropriate for susceptibility modeling than the single information value (IV) model, with the random forest model optimized by genetic algorithm in Group I2 exhibiting higher predictive accuracy (AUC = 0.796). Furthermore, comparative evaluation results reveal that, under equivalent model conditions, the incorporation of a remote-sensing landslide inventory significantly enhances the accuracy of landslide susceptibility assessment results. This study not only investigates the impact of landslide inventories and models on susceptibility outcomes but also validates the feasibility and scientific validity of employing multisource remote-sensing technologies in landslide susceptibility assessment.

Effects of Land Cover Changes on Shallow Landslide Susceptibility Using SlideforMAP Software (Mt. Nerone, Italy)

Land cover changes in mountainous areas due to silvo-pastoral abandonment can affect soil stability, especially on steep slopes. In addition, the increase in rainfall intensity in recent decades requires re-assessing landslide susceptibility and vegetation management for soil protection. This study was carried out using the software SlideforMAP in the Mt. Nerone massif (central Italy) to assess (i) the effects of land cover changes on slope stability over the past 70 years (1954–2021) and (ii) the role of actual vegetation cover during intense rainfall events. The study area has undergone a significant change in vegetation cover over the years, with a reduction in mainly pastures (−80%) and croplands (−22%) land cover classes in favor of broadleaf forests (+64%). We simulated twelve scenarios, combining land cover conditions and rainfall intensities, and analyzed the landslide failure probability results. Vegetation cover significantly increased the slope stability, up to three to four times compared to the unvegetated areas (29%, 68%, and 89%, respectively, in the no cover, 1954, and 2021 scenarios). The current land cover provided protection against landslide susceptibility, even during extreme rainfall events, for different return periods. The 30-year return period was a critical condition for a significant stability reduction. In addition, forest species provide different mitigation effects due to their root system features. The results showed that species with deep root systems, such as oaks, provide more effective slope stability than other species, such as pines. This study helps to quantify the mitigation effects of vegetation cover and suggests that physically based probabilistic models can be used at the regional scale to detect the areas prone to failure and the triggering of rainfall-induced shallow landslides. This approach can be important in land planning and management to mitigate risks in mountainous regions.

TRIGRS-FOSM: probabilistic slope stability tool for rainfall-induced landslide susceptibility assessment

TRIGRS-FOSM: probabilistic slope stability tool for rainfall-induced landslide susceptibility assessment

Landslide Susceptibility Assessment in Yulong County Using Contribution Degree Clustering Method and Stacking Ensemble Coupled Model Based on Certainty Factor

Landslide Susceptibility Assessment in Yulong County Using Contribution Degree Clustering Method and Stacking Ensemble Coupled Model Based on Certainty Factor

InSAR Integrated Machine Learning Approach for Landslide Susceptibility Mapping in California

InSAR Integrated Machine Learning Approach for Landslide Susceptibility Mapping in California

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

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

An updated landslide susceptibility model and a log-Gaussian Cox process extension for Scotland

AbstractAt the time of its development, GeoSure was created using expert knowledge based on a thorough understanding of the engineering geology of the rocks and soils of Great Britain. The ability to use a data-driven methodology to develop a national-scale landslide susceptibility was not possible due to the relatively small size of the landslide inventory at the time. In the intervening 20 years, the National Landslide Database has grown from around 6000 points to over 18,000 records today and continues to be added to. With the availability of this additional inventory, new data-driven solutions could be utilised. Here, we tested a Bernoulli likelihood model to estimate the probability of debris flow occurrence and a log-Gaussian Cox process model to estimate the rate of debris flow occurrence per slope unit. Scotland was selected as the test site for a preliminary experiment, which could potentially be extended to the whole British landscape in the future. Inference techniques for both of these models are applied within a Bayesian framework. The Bayesian framework can work with the two models as additive structures, which allows for the incorporation of spatial and covariate information in a flexible way. The framework also provides uncertainty estimates with model outcomes. We also explored consideration on how to communicate uncertainty estimates together with model predictions in a way that would ensure an integrated framework for master planners to use with ease, even if administrators do not have a specific statistical background. Interestingly, the spatial predictive patterns obtained do not stray away from those of the previous GeoSure methodology, but rigorous numerical modelling now offers objectivity and a much richer predictive description.

GIS-based landslide susceptibility assessment using random forest and support vector machine models: A case study for Chin State, Myanmar

GIS-based landslide susceptibility assessment using random forest and support vector machine models: A case study for Chin State, Myanmar

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

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

Improving Landslide Susceptibility Prediction in Uttarakhand through Hyper-Tuned Artificial Intelligence and Global Sensitivity Analysis

Improving Landslide Susceptibility Prediction in Uttarakhand through Hyper-Tuned Artificial Intelligence and Global Sensitivity Analysis

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

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

Assessment of landslide susceptibility in the Himalayan state of Tripura, India, using a Multi-Model Approach

Landslides are the down slope mass movement of soil, rocks, and debris due to a natural or human activities resulting in widespread hazard events in India. The most affected areas comprise 15 percent of its landmass which includes Tripura and eleven Himalayan states and parts of the Western and Eastern Ghats in India. In Tripura, landslides cause road blockage and destruction of settlements, bringing economic and life losses in every year. Thus, this research is focused on identifying landslide susceptible zones and the significant causative factors behind landslides. Assessment of Landslide Susceptibility (LS) identifies fifteen major causative factors under five broad groups; topographic, geotechnical, hydrological, environmental, and anthropogenic. With application of Analytical Hierarchical Process (AHP), Frequency Ratio (FR), and Random Forest (RF)-based models were performed to extract landslide susceptible zonation map for Tripura. This study reveals that the successive hill ranges formed by young sedimentary lithologic formations associated with deforestation, heavy rainfall during monsoon, and anthropogenic activities (road constructions and jhumming) are the responsible geo-conditions for triggering landslides. In this study, while the AHP and FR model show only 1.95% and 11.46% confined along the hilltop of Jampui, Sakhan, and Longtarai, the RF model designated Tripura’s 30% land area as high and very high landslide susceptible zones (LSZ), predominantly over hills, foothills, and low laying undulating land (tillas). For the accuracy assessment, the ROC curve is used, which shows that RF model appears to be the maximum accurate (0.810) one, followed by FR (0.806) and AHP (0.744).

A semi-automatic interpretation method for utilizing InSAR results to recognize active landslides considering causative factors

Synthetic Aperture Radar Interferometry (InSAR), which can map subtle ground displacement over large areas, has been widely utilized to recognize active landslides. Nevertheless, due to various origins of subtle ground displacement, their presence on slopes may not always reflect the occurrence of active landslides. Therefore, interpretation of exact landslide-correlated deformation from InSAR results can be very challenging, especially in mountainous areas, where natural phenomenon like soil creep, anthropogenic activities and erroneous deformational signals accumulated during InSAR processing can easily lead to misinterpretation. In this paper, a two-phase interpretation method applicable to regional-scale active landslide recognition utilizing InSAR results is presented. The first phase utilizes statistical threshold and clustering analysis to detect unstable regions mapped by InSAR. The second phase introduces landslide susceptibility combined with empirical rainfall threshold, which are considered as causative factors for active landslides triggered by rainfall, to screen unstable regions indicative of active landslides. A case study validated by field survey indicates that the proposed interpretation method, when compared to a baseline model reported in the literature, can achieve better interpretation accuracy and miss rate.

Hybrid method for rainfall-induced regional landslide susceptibility mapping

Hybrid method for rainfall-induced regional landslide susceptibility mapping

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

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

A Comparison of Alternative GIS Data Model Methods for Landslide Susceptibility Mapping with XGBoost and SHAP

Geographic Information Systems and machine learning algorithms suggest good alternatives for producing landslide susceptibility maps. In the process of producing these maps with machine learning, alternative data model options exist. Success rate of analyses may change according to the preferred data method. In this study, 6 different machine learning models were created by passing different data models with the XGBoost algorithm. Study area is located in the cities of Ordu and Giresun, Turkiye. 14 different factors and related geographic data layers were used. As a result of the study, the most successful model performance was achieved by taking the average values of all pixels of the combined landslide record polygons (Accuracy=0,88, Precision=0,86, F1 score=0,87). SHAP method was applied for better interpretation of machine learning results The susceptibility map produced with the ideal model, overlapped with 57.556 buildings in the region. The buildings were classified in 4 groups (low, moderate, high, and very high) and mapped, indicating their risk level.

Sample size effects on landslide susceptibility models: A comparative study of heuristic, statistical, machine learning, deep learning and ensemble learning models with SHAP analysis

In landslide susceptibility assessment (LSA), inventory incompleteness impacts the accuracy of different models to varying degrees. However, this area remains under-researched. This study investigated six LSA models from heuristic, statistical, machine learning and ensemble learning models (analytical hierarchy process (AHP), frequency ratio (FR), logistic regression (LR), Keras based deep learning (KBDL), XGBoost, and LightGBM) across six different sample sizes (100%, 90%, 75%, 50%, 25%, and 10%). Results revealed that XGBoost and LightGBM consistently outperformed other models across all sample sizes. The LR and KBDL models followed, while FR model was the most affected by sample size variations. AHP, an empirical model, remained unaffected by sample size. Through SHapley Additive exPlanations (SHAP) analysis, elevation, NDVI, slope, land use, and distance to roads and rivers emerged as pivotal indicators for landslide occurrences in the study area, suggesting that human activities significantly influence these events. Five time-varying indicators regarding human activity and climate validated this inference, which provides a new method to identify landslide triggering factors, especially in areas of intense human activity. Based on the findings, a comprehensive framework for LSA is proposed to assist landslide managers in making informed decisions. Future research should focus on expanding model diversity to address the effects of sample size, enhancing the adaptability of the LSA framework, deepening the analysis of human activity impacts on landslides using explainable machine learning techniques, addressing temporal inventory incompleteness in LSA, and critically evaluating model sensitivity to sample size variations across multiple disciplines.