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On June 17, 2020, Aniangzhai landslide, an ancient landslide located in Danba County, southwest China, was reactivated by Meilonggou debris flow. The front edge of the slope collapsed, mobilizing a soil mass of about 2.35 × 106 m3. Evaluating the stability of the whole slope is of great importance to avoid further landslides and mitigate the damage for Aniangzhai villagers living on this slope. This paper focuses on the inaccessible upper slope of Aniangzhai landslide (no attention paid before) that exhibits a relative elevation difference of more than 1000 m. Multi-source remote sensing, including unmanned aerial vehicle (UAV) photogrammetry, light detection and ranging (LiDAR), and satellite-based interferometric synthetic aperture radar (InSAR) techniques, was used in this research to identify and evaluate this high mountain upper slope potential hazard in Aniangzhai landslide. Considering the huge height difference and the steep slope of Aniangzhai landslide, an iterative route planning method was proposed and adopted to obtain a 3D model with 0.02 m resolution and a DEM with 0.25 m resolution by using UAV and LiDAR close-in flight method, respectively. Meter-level huge cracks were clearly identified by the high-resolution UAV 3D model and LiDAR data, which confirm that the location of these cracks is related to the morphological structure of this ancient landslide. Time series InSAR analysis reveals the activity of this high-altitude area, with a maximum LOS displacement rate of 15 cm/a. The combination of the above remote sensing technologies confirms and reveals the high potential risk and the reactivated condition of the upper slope of Aniangzhai landslide. Through this finding, we show that the evolution of Aniangzhai landslide happened through four stages with a cascading effect. This paper proves the usefulness of an integrated method to successfully identify and evaluate the high-altitude upper slope potential hazard and compares the technical features of them, providing a reference for future works that aimed at mitigating the potential damage of the upper slope. [ABSTRACT FROM AUTHOR]

This paper discusses the role of plants in the prevention of shallow landslides induced by rain (soil slips); these phenomena, related to "hydrogeological instability," are among the most feared because their evolutionary processes can cause huge damages and losses of human lives when interacting with anthropized areas and infrastructures. The paper first highlights how the plants interact with the soil; then introduces the G-SLIP (Green – Shallow Landslides Instability Prediction) model, i.e., the simplified physically-based SLIP model, modified to predict soil slips at punctual and large scale taking into account the vegetation effects. The G-SLIP model is thus applied to a case study of the Parma Apennines (Northern Italy) by using the G-XSLIP platform. In this area, during the intense events of rain between the 4th and 5th of April 2013, numerous landslides occurred, provoking huge damages to structures and infrastructures, and consequent economic losses. The stability analyses carried out with G-XSLIP demonstrate that the presence of vegetation in the study area led to a significant reduction in the triggering of shallow landslides. Finally, an attempt at soil slip mitigation through naturalistic techniques (planting of specific vegetation) is presented. [ABSTRACT FROM AUTHOR]

Machine learning models have been widely used in landslide susceptibility prediction. However, landslide multidimensional feature extraction, model generalization ability, and prediction quantification of deep learning are still challenging. This paper proposes a new approach, the landslide density-based bidirectional long short-term memory (LD-BiLSTM) model with multichannel input and an optimized sampling strategy to predict and map landslide susceptibility in active seismic mountainous areas of Sichuan Province, China. First, to ensure the generalization ability of the LD-BiLSTM model, other regions in Sichuan were selected as the model training area independent of the prediction area (Luding County). Multichannel landslide datasets were constructed to extract high-dimensional geospatial features of landslides. Subsequently, the landslide density of each grid cell was utilized as the label for the corresponding input sample. The LD-BiLSTM model was improved by using transfer learning and sampling optimization strategies, which makes our method attenuate the impact of historical landslide inventory deviation on the spatial susceptibility mode compared with the existing DL model, which usually uses landslide objects (LO) as input sample labels. Model performance evaluation results show that the LD-BiLSTM model (precision = 0.903, recall = 0.899, F1-score = 0.901, Area under receiver operating characteristic curve (AUC) = 0.940) outperformed the LO-BiLSTM model (precision = 0.812, recall = 0.815. F1-score = 0.813, AUC = 0.910) in the case areas. Meanwhile, the performance of the LD-BiLSTM model (AUC = 0.9407) significantly outperformed both the information value (IV) (AUC = 0.7207) model and the random forest (RF) (AUC = 0.8116) models in the landslide prediction area (Luding), which confirms that the proposed LD-based method is superior to traditional LO-based methods. Significantly, our approach can effectively extract the spatial distribution of landslides and predict potential landslides in complex high-mountain environments. [ABSTRACT FROM AUTHOR]

Worldwide, landslides are the most frequently occurring disaster that is very destructive and unpredictable in nature. A total of 850 landslide events were detected during 2005–2020 in the Tehri region of the Indian Himalayas. Many researchers have conducted landslide susceptibility mapping (LSM) studies for this region using different static landslide-causing factors. However, studies considering dynamic factors in predicting future landslide susceptibility scenarios are inadequate. Hence in this study, both dynamic and static factors were utilized in predicting future landslide susceptibility maps for the year 2050. The paper's main objective is the future prediction of LSM, considering future projections of land use land cover (LULC) and climate variables (precipitation and temperature). To achieve this objective, first, the geospatial database in three temporal categories, 2005–2010, 2010–2015, and 2015–2020, was prepared for the historical landslide events. Second, the landslide-causing factors were optimized and utilized in LSM for 2010, 2015, and 2020. Third, projected LULC map was generated for the year 2050 using the Artificial Neural Network-Cellular Automata (ANN-CA) model. Fourth, CMIP6 climate projection maps were prepared using the Indian Institute of Tropical Meteorology Earth system model (IITM ESM) under four shared socioeconomic pathway (SSP) scenarios. Finally, the projected maps were used as the driving parameter for the future prediction of LSM. The results reveal a high increase in the built-up area (5%) and agriculture land (4%) with a decrease in forest area (10%) in future LULC projections. The results of future LSM prediction under SSP 1–2.6, SSP 2–4.5, SSP 3–7.0, and SSP 5–8.5 climate scenarios show an increase in very high landslide susceptibility class by 2%, 4%, 7%, and 9% respectively. The predicted maps were validated utilizing the Kappa coefficient verifies the reliability of the simulated future results. [ABSTRACT FROM AUTHOR]

Slow-moving landslide-prone areas (SLAs) are unstable objects on the terrestrial surface that can collapse rapidly when provoked by earthquakes, leading to infrastructure damage. It is critical to identify SLAs prior to earthquake events and assess their hazard-causing probabilities when triggered. An assessment approach of earthquake-triggered geohazards is proposed in this paper by combining interferometric synthetic aperture radar (InSAR) derived SLAs and geological and geomorphological factors. Taking the Ms6.8 Luding earthquake, which occurred in the Sichuan Province of southwestern China on September 5, 2022, as an example, 1320 scenes of Sentinel-1 SAR data in western Sichuan were processed using the small baseline subset (SBAS) InSAR technique before the earthquake. After the earthquake, hazard probability assessment was performed in real-time by filtering the SLAs using a spatial analysis technique with geological and geomorphological factors, e.g., real-time peak ground acceleration (PGA), slope, distance to fault (DTF), and distance to the river (DTR) data. The results show that 11 SLAs were classified into high-risk zones. As verified by the Luding co-seismic landslide dataset from visual interpretation of optical remote sensing images, 142 coseismic landslides were triggered by the earthquake in these high-risk regions. In these areas, an ancient landslide, with high pre-earthquake displacement rates (−50 mm/year) on the scarp was reactivated under the Luding earthquake forces. This method can provide a scientific tool for disaster mitigation and rapid response emergency management. [ABSTRACT FROM AUTHOR]

Landslides frequently disrupt roadway networks in mountainous regions worldwide. Because of the relatively long roadway extension and low roadway density in mountainous regions, the occurrence of a landslide hazard along a local road segment will cause traffic paralysis on the individual roadway and will further impact regional roadway network accessibility. This paper establishes an integrative risk assessment framework based on risk theory and complex network theory to combine the results of landslide susceptibility mapping along roadways and impact assessment on the roadway network. Through an analysis of the relationship between various geo-environmental conditioning factors and historical landslides along roadways, the support vector machine (SVM) model is used to assess landslide susceptibility across the regional roadway network. Both topological connectivity of the entire roadway network and transport accessibility between local residents are considered in the impact assessment on roadway networks. To illustrate the effectiveness of the proposed risk assessment framework, a case study of the roadway network in Fengjie County, China, which is prone to landslide occurrence, is conducted. The resulting landslide risk heatmap of Fengjie County's roadway network is generated using ArcGIS software. The most critical road segments are identified as being highly susceptible to landslides, and if they are disrupted, the entire roadway network will suffer significant performance loss. The results can support adaptive strategies for landslide mitigation, preparedness, and emergency response services, as well as improve roadway plans to reduce exposure and associated consequences by adding new links to the existing roadway network. [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]

On October 2017, due to continuous rainfall, there was a massive river blockage induced by a landslide in Guang'an Village, Chongqing, China. Long-term monitoring analysis has shown that there remain four strong deformation areas on the slope, which seriously threaten the life and property of nearby residents. In this paper, a granular flow model and an elasto-visco-plasticity model were applied to reproduce and predict the landslide event that hit Guang'an Village. The results showed that the landslide gradually moved along the sliding surface, pushing loose deposits and blocking the Xixi River. The numerical reproduction results of the 2017 event are consistent with the actual slope deformation and failure process and deposit morphology. The simulated maximum depth-averaged velocity of this landslide was approximately 1.89 m/s, and the height of the landslide dam was approximately 10 m. After the landslide occurred in 2017, several large deformation areas appeared in the vicinity of the sliding area, and the right rear side of the sliding mass in area III has the largest deformation volume, accompanied by the most developed surface crack and the most intense deformation. There is a risk that the Xixi river will be blocked again. Therefore, with the same parameter and numerical model, a sliding–pushing–blocking dynamic prediction analysis of the strong deformation area III was conducted. The pushing motion of the mass in this area will reactivate the landslide mass observed in 2017. The maximum depth-averaged velocity of deformation area III was 0.5 m/s, and the maximum depth-averaged velocity of landslide deposition was 0.45 m/s. The length of the blocking dam formed by the mass of deformation area III along river was approximately 780 m, 30 m longer than that in 2017. The predicted height of the landslide dam was 14.5 m, approximately 4.5 m higher than that in 2017. The length of the landslide dam reservoir was predicted to be 2.55 km along the Xixi River, which may submerge the Waping Village. This study supports the landslide hazard prevention, reveals the whole movement process of sliding-pushing-blocking, and provides a new research idea and method for the landslide movement prediction. Hence, this study can serve as a reference for the hazard prevention and mitigation of such chain disasters. [ABSTRACT FROM AUTHOR]

As the global climate warms, the frequency of soil slope failures induced by snowmelt is gradually increasing, especially loess landslide disasters in seasonally frozen areas. Landslide disasters in seasonally frozen areas pose a serious threat to human lives and engineering constructions and are gradually drawing greater public attention around the world. However, the mechanism that regulates deformation and failure processes, resulting in snowmelt-driven landslides, remains elusive. The Yili River Valley in Xinjiang, China, is an ideal location for exploring the mechanism of snowmelt-driven landslides. Based on detailed field surveys, remote sensing image identification, meteorological data analysis and loess characteristic tests, this paper takes a representative high-level loess landslide in north-west China as a case in order to explore its evolution history, movement process and resurrection mechanism. It was found that this loess slope had suffered two large-scale sliding failures and different degrees of inherited slope deformation had been found between the two slope failures. The 32 surface cracks identified have a total length of 3,505.10 m, and the total area of the landslide was found to be 135,462m2. The average thickness of the sliding body was about 30 m, and the volume was approximately 504,000 m3. The actual movement time lasted for 32 s with an average moving speed of about 15 m/s. Human grazing activities dominate the formation and development of slope surface cracks in the early stage. The strong water sensitivity of distinctive loess controls soil strength deterioration in the slip zone. Rapid snow removal and infiltration, driven by an abnormal temperature rise in Spring, is the most important triggering factor for slope deformation, evolution and failure. As regional grazing activities increase and global warming intensifies, the potential for resurrection landslides will increase. The results provide essential information for a comprehensive understanding of early warning systems and risk assessment for snowmelt-triggered landslides in cold areas. [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]

Geomorphological analysis of landslide processes in mountainous terrains with difficult access has benefited from virtual representation of topography through the use of high-resolution digital elevation models (DEMs) generated by use of light detecting and ranging. Generic models of overlay and interpolation take advantage of the high-resolution DEMs for volume calculation. However, the advantages and limitations of the models at regional scale have received scant attention. These challenges are magnified in landslide hazard zonation mapping projects at state or national level, where the models need to be implemented for large datasets. To address the above deficiency, this paper presents a means to estimate landslide volume production and distribution by taking full advantage of LiDAR and to standardize landslide volume calculation in a geographic information system (GIS). We implemented two generic landslide volume models by using Python scripts; this is a systematic methodology for modeling volume of shallow and deep-seated landslides. The models were tested in real and theoretical conditions to highlight advantages and limitations. At the same time, we explored how the interpolation model is affected by local altimetric variation. The results show that one of the models can be used to make first-order interpretations regarding volume of eroded debris for landslide deposits at a local or national scale, while the other can help to assess the sequence of landslide activity. Theoretical evaluations show that local altimetric variation of < 1 m could lead to errors of almost 17%. The approach is explored with examples from Sumas Mountain in Whatcom County, Washington, USA. [ABSTRACT FROM AUTHOR]

With the development and application of machine learning, significant advances have been made in landslide susceptibility mapping. However, due to challenges in actual field landslide investigations, current landslide susceptibility mapping is usually characterized by insufficient landslide samples (positive samples) and low reliability of non-landslide samples (negative samples). Considering Lianghe County in Yunnan Province, China, as an example, this paper aims to research the effectiveness of three oversampling models in generating positive samples for landslides: Conditional Tabular Generative Adversarial Networks (CTGAN), Generative Adversarial Networks (GAN), and the traditional Synthetic Minority Oversampling Technique (SMOTE) algorithms. Additionally, three machine learning methods, including 1D Convolutional Neural Network-Long Short-Term Memory Neural Network (CNN-LSTM), Random Forest (RF), and Gradient Boosting Decision Tree (GBDT) classifiers, are used for landslide susceptibility assessment. We also devise a non-landslide data (negative samples) screening method utilizing a self-trained support vector machine within a semi-supervised framework. The results show that by training on the dataset after negative sample screening, the AUC values for the 1D-CNN-LSTM, RF, and GBDT models have shown significant improvement, increasing from (0.778, 0.869, 0.849) to (0.837, 0.936, 0.877). Compared with the original training set, the prediction accuracy of the three machine learning models is improved after training on the augmented data by CTGAN, GAN, and SMOTE models. The RF model, augmented with 200 positive samples generated by CTGAN, achieves the highest prediction accuracy in the study (AUC = 0.962). The 1D CNN-LSTM model achieves its highest prediction accuracy (AUC = 0.953) when augmented with 200 positive samples from GAN. Similarly, the GBDT model reaches its highest prediction accuracy (AUC = 0.928) when augmented with 200 positive samples created by SMOTE. In addition, the spatial distribution of data indicates that the data generated by the generative adversarial model exhibits higher diversity, which can be used for landslide susceptibility assessment. [ABSTRACT FROM AUTHOR]

Size is a fundamental property of landslides, but it is described inconsistently within the scientific literature. There is currently no widely adopted size classification system applicable to all landslide types. A Scopus database search shows the most used landslide size descriptor is the term large, used to refer to landslides with volumes spanning ten orders of magnitude. Some size descriptors are unintuitive or potentially misleading (e.g. the term massive which describes a material property). We argue that a formal size classification scheme would encourage more consistent and logical usage of size descriptors and improve landslide science communication. To that end, we propose a size classification scheme suitable for all landslide types. The scheme provides a log scale of size classes for volume and area, with base units of cubic metre and square metre, respectively. In theory, there is no limit to the number of size classes possible. Six size descriptors are suggested, each spanning 3 orders of magnitude: very small (10−3–100 m3), small (10–103 m3), medium (103–106 m3), large (106–109 m3), giant (109–1012 m3), and monster (1012–1015 m3). Our system does not replace existing (or preclude future) classification systems for specific landslide types (e.g. snow avalanche) that use numerical size classes, and it maintains consistency with some commonly used descriptors. Whatever system is used, we encourage people to define the terms they use and to quantify size where possible, so that clearer meaning is given to the words used to describe landslide sizes. [ABSTRACT FROM AUTHOR]

Interferometric Synthetic Aperture Radar (InSAR) has been increasingly used in landslide detection over wide areas. However, atmospheric delays, phase unwrapping errors, and noise, which behave like deformation signals in InSAR deformation results, pose a challenge for semi-automatic and automatic landslide detection. C-index can assistant in landslide interpretation by assessing the rationality of InSAR deformation and filtering out non-real landslide deformation signals. Yet, few studies have analyzed its applicability in identifying landslide from InSAR results. In this study, we develop a method that uses C-index to assist in landslide detection using InSAR. We validate our method in a selected region of the Jinsha River basin, China. We first using multi-temporal InSAR (MT-InSAR) to obtain the deformation rate of the study area. Sixty-nine and 47 suspicious slope deformation areas are extracted from the ascending and descending deformation results, respectively. Next, C-index is used to automatically exclude 28 and 12 false deformation areas from the ascending and descending InSAR results, respectively. Remarkably, all the excluded false deformation areas do not correspond to landslides, suggesting the reliability of the proposed method. Finally, by visually interpreting the optical images of the remaining deformation results, we successfully detect 54 landslides. Among these, 9 landslides can be detected by both the ascending and descending Sentinel-1 data, and 15 landslides are located on the riverbanks, posing a potential risk of river blockage in the event of failure. The presented landslide detection method effectively filters out false deformation areas, ultimately enhancing the efficiency and accuracy of landslide detection over wide areas using InSAR. [ABSTRACT FROM AUTHOR]

Global navigation satellite system (GNSS) positioning technology is widely used in landslide monitoring due to its unique real-time and high-precision advantages. However, in complex landslide environments with severe occlusion, multipath error becomes one of the main error sources affecting GNSS real-time kinematic high-precision positioning, as it cannot be eliminated by differential correction. Traditional sidereal filtering methods cannot retain deformation information, which affects the accuracy of landslide early warning. To mitigate multipath error and preserve landslide deformation information, an improved sidereal filtering method based on mutation points and landslide evolution stage recognition is proposed. The method considers the landslide evolution stage, establishes a dataset for sidereal filtering, matches the multi-day correlation data with the sliding section-related data, and constructs a multipath model using averaging. Experimental results show that, compared with the traditional sidereal filtering and conventional segmentation methods, the proposed method reduces the coordinate deviation (QE) in the 3D direction by 25.02% and 22.13% and reduces the fitting deviation (QR) by 4.01% and 36.21% during the stabilization stage. During the critical sliding stage, QE decreases by 48.54% and 23.8%, and QR decreases by 60.12% and 42.54%, respectively. The proposed method has the best smoothness and power spectral density indicators in both stages. It has also been successfully applied to the Heifangtai landslide in Gansu Province, providing more stable and reliable data than traditional methods, avoiding delayed and false alarms, and issuing a red warning 3 h before the landslide failure, demonstrating its practical value. [ABSTRACT FROM AUTHOR]

Timely and systematic collection of landslide information after a triggering event is pivotal for the definition of landslide trends in response to climate change. On September 15, 2022, a large part of central Italy, particularly Marche and Umbria regions, was struck by an anomalous rainfall event that showed characteristics of a persistent convective system. An extraordinary cumulated rainfall of 419 mm was recorded by a rain gauge in the area in only 9 h. The rainfall triggered 1687 landslides in the area affected by the peak rainfall intensity and caused widespread flash floods and floods in the central and lower parts of the catchments. In this work, we describe the characteristics of the landslides identified during a field survey started immediately after the event. Most of the mass movements are shallow, and many are rapid (i.e., debris flows, earth flows) and widely affecting the road network. Landslide area spans from a few tens of square meters to 105 m2, with a median value of 87 m2. Field evidence revealed diffuse residual risk conditions, being a large proportion of landslides located in the immediate vicinity of infrastructures. Besides reporting the spatial distribution of landslides triggered by an extreme rainfall event, the data collected on landslides can be used to make comparisons with the distribution of landslides in the past, validation of landslide susceptibility models, and definition of the general interaction between landslides and structures/infrastructures. [ABSTRACT FROM AUTHOR]

The definition of landslide hazard is a step-like procedure that encompasses the quantification of its spatial and temporal attributes, i.e., a reliable definition of landslide susceptibility and a detailed analysis of landslide recurrence. However, available information is often incomplete, fragmented and unsuitable for reliable quantitative analysis. Nevertheless, landslide hazard evaluation has a key role in the implementation of risk mitigation policies and an effort should be done to retrieve information and make it useful for this purpose. In this research, we go through this topic of optimising the information available in catalogues, starting from landslide inventory review and constitution of a boosted training dataset, propaedeutic for susceptibility analysis based on machine learning methods. The temporal recurrence of landslide events has been approached here either through the definitions of large-scale quantitative hazard descriptors or by analysis of historical rainfall (i.e., the main triggering factor for the considered shallow earth slope failures) databases through the definition of rainfall probability curves. Spatial and temporal attributes were integrated, selecting potential landslide source areas ranked in terms of hazard. Data integration was also pursued through persistent scatterer interferometry analysis which pointed out areas of interest within potential landslide source areas featured by ongoing ground movement. The consequential approach led to the definition of the first hazard product of the city of Rome at a local scale functional for advisory purposes or the statutory level, representing a thematic layer able to orient the risk managers and infrastructure stakeholders. [ABSTRACT FROM AUTHOR]

Soil moisture plays a vital role in slope stability. As water infiltrates into the soil, shear strength decreases eventually leading to failure. However, most of the existing regional-scale landslide early warning systems (LEWS) rely solely on rainfall information and use rainfall thresholds to determine if the landslide triggering conditions are met. The original version of the Catalonia region LEWS combines real-time rainfall observations and susceptibility to compute warnings. The LEWS applies a set of rainfall intensity-duration thresholds to determine if the rainfall conditions have the potential to trigger a landslide. This work explores the potential of using modelled soil moisture data in the Catalonia region LEWS. Volumetric water content (VWC) from the LISFLOOD hydrological simulations of the European Flood Awareness System and rainfall estimates have been analysed at the location of recent landslide events. Based on this data, a set of empirical hydrometeorological thresholds combining rainfall and soil moisture information has been obtained for their application into the Catalonia region LEWS. The LEWS has been run for nine months (April–December 2020) using two approaches: (i) combining susceptibility and rainfall intensity-duration (I-D) thresholds and (ii) combining susceptibility and the new hydrometeorological thresholds including soil moisture information. Generally, both LEWS approaches issued moderate or high warnings in the areas where significant rainfall accumulations were recorded. The outputs have been compared at specific locations where landslides were reported during the analysed period. Results show that at the analysed locations false positives are generally reduced when employing the hydrometeorological thresholds in the LEWS. Therefore, this approach is promising and could help improve regional scale LEWS in Catalonia. [ABSTRACT FROM AUTHOR]

In recent decades, developing countries have experienced an increase in the impact of natural disasters due to ongoing climate change and the sustained expansion of urban areas. The intrinsic vulnerability of settlements, due to poverty and poor governance, as well as the lack of tools for urban occupation planning and mitigation protocols, has made such impacts particularly severe. Cuenca (Ecuador) is a significant example of a city that in recent decades has experienced considerable population growth (i.e. exposure) and an associated increase in loss due to landslide occurrence. Despite such effects, updated urban planning tools are absent, so an evaluation of multitemporal exposure to landslides and related risks is required. In this perspective, a potential urban planning tool is presented based on updated data depicting the spatial distribution of landslides and their predisposing factors, as well as population change between 2010 and 2020. In addition, a multitemporal analysis accounting for changes in exposure between 2010 and 2020 and an estimation of relative landside risk was carried out. Due to the absence of spatially distributed population data, energy supply contract data have been used as a proxy of the population. The results show that the current higher exposure and related relative risk are estimated for parishes (parroquias) located in the southern sector of the study area (i.e. Turi, Santa Ana, Tarqui, Nulti, Baños and Paccha). Moreover, the exposure multitemporal analysis indicates that most parishes located in the hilly areas bounding the city centre (i.e. Sayausi, San Joaquin, Tarqui, Sidcay, Baños, Ricaurte, Paccha and Chiquintad) are experiencing sustained population growth and will be potentially exposed to an increased risk with a consistently growing trend. The obtained relative risk map can be considered a valuable tool for guiding land planning, land management, occupation restriction and early warning strategy adoption in the area. The methodological approach used, which accounts for landslide susceptibility and population variation through proxy data analysis, has the potential to be applied in a similar context of growing population cities in low- to mid-income countries, where data usually needed for a comprehensive landslide risk analysis are non-existing or only partially available. [ABSTRACT FROM AUTHOR]

Landslide risk assessment is fundamental in identifying risk areas, where mitigation measures must be introduced. Most of the existing methods are based on susceptibility assessment strongly site-specific and require information often unavailable for damage quantification. This study proposes a simplified methodology, specific for rainfall-induced shallow landslides, that tries to overcome both these limitations. Susceptibility assessed from a physically-based model SLIP (shallow landslides instability prediction) is combined with distance derived indices representing the interference probability with elements at risk in the anthropized environment. The methodology is applied to Gioiosa Marea municipality (Sicily, south Italy), where shallow landslides are often triggered by rainfall causing relevant social and economic damage because of their interference with roads. SLIP parameters are first calibrated to predict the spatial and temporal occurrence of past surveyed phenomena. Susceptibility is then assessed in the whole municipality and validated by comparison with areas affected by slide movements according to the regional databases of historical landslides. It is shown that all the detected areas are covered by points where the SLIP safety factor ranges between 0 and 2. Risk is finally assessed after computation of distances from elements at risk, selected from the land use map. In this case, results are not well validated because of lack of details in the available regional hydrogeological plan, both in terms of extension and information. Further validation of the proposed interference indices is required, e.g., with studies of landslide propagation, which can also allow considerations on the provoked damage. [ABSTRACT FROM AUTHOR]

On the 26th of November 2022, as a consequence of heavy rainfall, diffuse landslides were triggered in the Northern sector of Ischia. The present study investigated the main features of the debris flow originated in a small catchment basin falling within the Casamicciola Terme Municipality. The debris flow, triggered around 05:00 a.m. from the Northern slope of Mt. Epomeo, travelled about 850 m before impacting a populated area. In detail, it affected about 30 buildings causing 12 casualties and several injured, more than 200 people displaced, and severe damages to the road network, resulting one of the most destructive landslide events occurred on the island. This multidisciplinary work, aimed at a preliminary assessment of the event, considered geological and geomorphological evidence collected during a field investigation, as well as environmental data, to set up numerical models both for landslide triggering and propagation. Along the pre-existing channel, a marked erosional behaviour was observed with a maximum removed thickness of about 5.5 m, whilst the material deposition largely occurred after the impact on buildings. The total volume mobilised by the debris flow was estimated in approximately 40,000 m3. Considering the geological and geomorphological conditions, as well as the high vulnerability and the socio-economic importance of the area, a careful monitoring and risk management activities in the next months will be necessary. [ABSTRACT FROM AUTHOR]

The elevation dependence of climate change is unequivocal. Landslides (slides and debris flows) are the key mark of climate change acting on the land cryosphere, and they can pose a severe threat to the downslope or downstream community. However, whether there is an elevation dependence of landslide activity caused by climate change is unknown. In this study, Taxkorgan County in the eastern Pamirs was designated as the target area for studying the elevation dependence of shallow landslide activity. The volume and number of landslides have gradually increased over the last 20 years as the climate has become warmer and wetter. Small-sized (< 50 × 103 m3) slides induced by torrential rainfall are concentrated in areas below the 0 °C isotherm curve, while larger volume (> 50 × 103 m3) debris flows caused by the maximum temperature are distributed in areas above the 0 °C isotherm curve. Moreover, the landslides induced by climate change in the study area have a significant elevation dependence. In particular, the number and volume of debris flow affected by the maximum temperature in the areas above the 0 °C isotherm curve gradually increase with elevation. In the future (2020–2049), more small-sized slides will occur below the 0 °C isotherm curve as the climate continues to become warmer and wetter. The number and volume of the large-volume debris flow in the regions above the 0 °C isotherm curve will continuously increase with elevation. Our findings suggest that identifying and predicting the volume, number, and triggering factors of landslides within different elevation intervals should contribute to the more accurate assessment and management of landslide risks at the regional scale. [ABSTRACT FROM AUTHOR]

The number of large landslides in India has risen in the recent years, due to an increased occurrence of extreme monsoon rainfall events. There is an urgent need to improve our understanding of moisture-induced landslide dynamics, which vary both spatially and temporally. Geophysical methods provide integrated tools to monitor subsurface hydrological processes in unstable slopes at high spatial resolution. They are complementary to more conventional approaches using networks of point sensors, which can provide high temporal resolution information but are severely limited in terms of spatial resolution. Here, we present and discuss data from an electrical resistivity tomography monitoring system—called PRIME—deployed at the Amrita Landslide Early Warning System (Amrita-LEWS) site located in Munnar in the Western Ghats (Kerala, India). The system monitors changes in electrical resistivity in the subsurface of a landslide-prone slope that directly threatens a local community. The monitoring system provides a 4D resistivity model informing on the moisture dynamics in the subsurface of the slope. Results from a 10-month period spanning from pre-monsoon to the end of the monsoon season 2019 are presented and discussed with regard to the spatial variation of soil moisture. The temporal changes in resistivity within the slope are further investigated through the use of time-series clustering and compared to weather and subsurface pore water pressure data. This study sheds new light on the hydrological processes occurring in the shallow subsurface during the monsoon and potentially leading to slope failure. This geophysical approach aims at better understanding and forecasting slope failure to reduce the risk for the local community, thereby providing a powerful tool to be included in local landslide early warning systems. [ABSTRACT FROM AUTHOR]

In the present study, the Sataun landslide was investigated along the National Highway-72 (NH-72) between Sirmauri Tal and Sataun towns of the Sirmaur district of Himachal Pradesh, Northwestern Himalaya, India. Geologically, the Sataun landslide is composed of Middle Siwalik sandstones, which are highly fractured and faulted due to the interaction of the active Sirmauri Tal Fault zone and the Malgi Fault at the site of the Sataun landslide. Through the geological field investigations, three unstable zones were identified, which are prone to landslides. A major failure along one of the unstable zones (Sataun landslide, Zone 1) occurred on the 9th of October 2019. The investigation of the Sataun landslide was carried out using geological, geomorphological, geotechnical, and kinematic analysis. A pre-landslide topography was used to analyse the slope stability using FEM (finite element modelling) technique. Geomorphological studies and field investigations suggest the presence of an active fault zone (Sirmauri Tal Fault zone) passing through the Sataun landslide zone. Grain size analysis demonstrates that the soil is non-cohesive poorly graded sandy soil and geotechnical parameters indicate low cohesion (0.17 kPa) and low angle of internal friction (33.09°). Pre-landslide slope modelling suggests a low factor of safety (0.76). Toe cutting by the Giri River, the presence of radial, transverse cracks on the landslide body, and tension cracks on the crown propose the active and progressive nature of the landslide. Although the role of active fault is the most crucial factor for the Sataun landslide failure, rainfall played a critical triggering role in the Sataun landslide. This study shows that the unstable condition of the Sataun landslide may trigger another event of similar or greater intensity in the near future; therefore, proper monitoring is required to mitigate this landslide. [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]

Probabilistic modelling is gaining increased attention in the field of assessing the landslide hazard due to the ability to account for the spatial and temporal uncertainties related to the variability of geological, hydrological, geotechnical, seismological and geomorphological parameters. In this study, a seismic landslide hazard assessment was carried out for Uttarakhand state, located in the Indian Himalayan region. A methodology was developed to model the parametric uncertainties incorporated in the modified Newmark slope stability analysis model, which considers the rock joint shear strength properties in permanent displacement computation. The uncertainties related to input parameters were taken into account by utilizing statistical distributions to represent these parameters. On a pixel-by-pixel basis, several probability density functions were simulated using the Monte Carlo method, and the simulation results were retained throughout the computation process. As a result, there were no constraints on the mathematical complexity or symmetry of the underlying distributions when casting the derived quantities into probabilistic hazard maps. The hazard map showed the probability of exceedance of seismic slope displacement beyond a threshold value of 5 cm. High probability values were observed in the Middle and Greater Himalayas, emphasizing the likelihood of a large number of earthquake-induced landslides in this region. Finally, the results were validated using the landslide inventory of the 1999 Chamoli earthquake. The prepared seismic landslide hazard map will give infrastructural planners and local authorities a tool for evaluating the risk associated with a seismic landslide for land use planning and taking appropriate mitigation measures to reduce the losses. [ABSTRACT FROM AUTHOR]

Landslide occurrence has become increasingly influenced by human activities. Accordingly, changing land use and land cover (LULC) is an important conditioning factor in landslide susceptibility models. We present a bibliometric analysis and review of how LULC was explored in the context of landslide susceptibility in 536 scientific articles from 2001 to 2020. The pattern of publications and citations reveals that most articles hardly focus on the relationship between LULC and landslides despite a growing interest in this topic. Most research outputs came from Asian countries (some of which are frequently affected by landslides), and mostly with prominent international collaboration. We recognised three major research themes regarding the characteristics of LULC data, different simulated scenarios of LULC changes, and the role of future scenarios for both LULC and landslide susceptibility. The most frequently studied LULC classes included roads, soils (in the broadest sense), and forests, often to approximate the negative impacts of expanding infrastructure, deforestation, or major land use changes involving agricultural practice. We highlight several articles concerned primarily with current practice and future scenarios of changing land use in the context of landslides. The relevance of LULC in landslide susceptibility analysis is growing slowly, though with much potential to be explored for future LULC scenario analysis and to close gaps in many study areas. [ABSTRACT FROM AUTHOR]

Machine learning methods require a vast amount of data to train a model. The data necessary for landslide susceptibility mapping is a collection of landslide causative factors as predictors and landslide inventory as a response variable; however, landslides do not occur everywhere, and the occurrence of landslides is limited in an area. This geophysical phenomenon leads to severely skewed class distribution, wherein the number of landslide samples (minority class) is significantly less than non-landslide locations (majority class). The imbalance in landslide data hampers the predictive ability of learning algorithms, and hence, the final models show poor performance in the class with fewer samples. This work uses two undersampling techniques, namely, EasyEnsemble (EE) and BalanceCascade (BC), for reducing the effect of imbalance in data. The landslides that occurred between 2004 and 2013 are randomly divided into two groups, i.e., 70% of the samples for training and 30% for testing, whereas the landslides that occurred between 2014 and 2017 have been used for validation. The balanced data is used with the support vector machine (SVM) and artificial neural network (ANN), thereby making four new approaches, i.e., EESVM, EEANN, BCSVM, and BCANN, for susceptibility mapping. We used several metrics, such as recall, geometric mean, precision, accuracy, and Heidke skill score, to evaluate the performance of landslide susceptibility maps. The AUC for imbalanced data with SVM and ANN is 0.50, which shows that the model cannot discriminate between landslide and non-landslide locations. This misclassification is due to a small number of landslide samples and serious class biases. The balanced data using EE and BC methods gives promising results and shows significant improvements, wherein the AUC of EESVM, EEANN, BCSVM, and BCANN is 0.869, 0.918, 0.881, and 0.923, respectively. Among all the methods, the recall and G-mean values were highest for EEANN, which represents the best separation performance of EEANN on landslide samples. Furthermore, we have used the standard error (SE) of AUC and 95% confidence interval to test the significance of various combinations of classification and undersampling schemes. The SE is highest for EESVM and BCSVM among all methods. Based on several accuracy metrics, we conclude that EEANN performs better than all the other methods. The BC-based method does not perform well for landslide susceptibility mapping and provides the highest misclassification of landslide samples. The study shows that the susceptibility maps prepared over balanced data using SVM and ANN show remarkable improvements in accuracy over imbalanced data. [ABSTRACT FROM AUTHOR]

On February 26, 2021, the ancient Moli landslide in Guoye town, Zhouqu County, Gansu Province, China, was reactivated. The volume of the reactivated landslide was approximately 2120 × 104 m3, which was classified as a remote accumulation layer landslide in a deep superlarge fault fracture zone. No casualties occurred due to timely warnings. The deformation characteristics and failure mechanisms of the landslide were systematically studied by means of field investigations and engineering mapping, field exploration, deep displacement monitoring, and high-density electrical methods. We found that the geological structure and landform of Zhouqu County are extremely complex. The sliding mass of this landslide was mainly gravelly soil with poor soil mechanical properties. The gravelly clay and phyllite clastic layers were prone to sliding. The landslide as a whole presented the comprehensive characteristics of front edge erosion traction and rear load displacement and was associated with multiple secondary landslides. The Heisongping–Sanjiaoping fault zone of the Bailong River passes through the rear edge of the landslide, and a variety of inducing factors worked together to make the landslide failure mechanism particularly complex. The monitoring and comprehensive control of this kind of landslide should be strengthened. [ABSTRACT FROM AUTHOR]

The main purpose of the present study is to develop a debris flow susceptibility map of a mountain area (Susa Valley, Western Italian Alps) by using an upgraded version of the Bonetto et al. (Journal of Mountain Science 18, 2021) approach based on the Rock Engineering System (RES) method. In particular, the area under investigation was discretized in a 5 × 5-m grid on which GIS-based analyses were performed. Starting from available databases, several geological, geo-structural, morphological and hydrographical predisposing parameters were identified and codified into two interaction matrices (one for outcropping lithologies and one for Quaternary deposits), to evaluate their mutual interactions and their weight in the susceptibility estimation. The result for each grid point is the debris flow propensity index (DfPI), an index that estimates the susceptibility of the cell to be a potential debris flow source. The debris flow susceptibility map obtained was compared with those obtained from two expedited and universally recognized susceptibility methods, i.e. the Regional Qualitative Heuristic Susceptibility Mapping (RQHSM) and the Likelihood Ratio (LR). Each map was validated by using the Prediction Rate Curve method. The limitations and strong points of the approaches analysed are discussed, with a focus on the innovativeness and uniqueness of the RES. In fact, in the study site, the RES method was the most efficient for the detection of potential source areas. These results prove its robustness, cost-effectiveness and speed of application in the identification and mapping of sectors capable of triggering debris flow. [ABSTRACT FROM AUTHOR]

Earthquakes, extreme rainfall, or human activity can all cause landslides. Several landslides occur each year around the world, often resulting in casualties and economic consequences. Landslide susceptibility mapping is considered to be the main technique for predicting the likelihood of an event based on the characteristics of the physical environment. Digital Terrain Model (DTM) is one of the fundamental data of modeling and is used to derive important conditional factors for detailed scale landslide susceptibility analyses. With this in mind, this study aimed to compare landslide susceptibility maps generated by Random Forest (RF) machine learning algorithm with data from Light Detection and Range (LiDAR) and Unmanned Aerial Vehicle (UAV). To this end, the performance achieved in prediction was evaluated using statistical evaluation measures based on training and validation datasets. The obtained results showed that the accuracy of both models is greater than 0.70, the area under the curve (AUC) is greater than 0.80, and the model generated from the LiDAR data is more accurate. The results also showed that the data from UAV have potential to use in landslide susceptibility mapping on an intra-urban scale, contributing to studies in risk areas without available data. [ABSTRACT FROM AUTHOR]

The High City of Antananarivo is one of the most important cultural heritage sites of Madagascar, on the UNESCO Tentative List since 2016. Built on the hilltop of the Analamanga Hill, a granite ridge overlooking the Ikopa River valley, it is renowned for its baroque-style palaces, such as the Rova royal complex, and neo-Gothic cathedrals dating back to the nineteenth century. During the winter of 2015, the twin cyclones Bansi and Chedza hit the urban area of Antananarivo, triggering floods and shallow landslides, as well as causing thousands of evacuees and many casualties. Between 2018 and 2019 several rockfalls occurred from the rock cliffs of the Analamanga hills, destroying housings and killing over 30 people. Both events showed that landslides can pose a high risk to the safety of the inhabitants, the infrastructure, and the cultural heritage of the High City. To assess the landslide hazard in the Analamanga Hill area, an integrated approach was adopted by means of the following actions: (i) creation of a multitemporal detailed scale landslide map; (ii) geotechnical characterization of the involved materials; (iii) analysis of landslide susceptibility in soils/loose deposits; (iv) runout analysis of debris flows channeling within large creek gullies; (v) landslide kinematic analysis of the rockmass; (vi) simulation of rockfall trajectories; (vii) analysis of rainfall data. The results show that the main factors affecting landslides are slope, lithology, creek-gully erosion, and anthropization, while most of the landslide events are clearly triggered by heavy rainfall. The landslide-prone areas (the phenomena include shallow landslides, rock falls, and debris flows) are located primarily along the cliff bounding the western hill slope, the southeastern sector (where abandoned quarries form large slope cuts), and subordinately in the steep creek catchment just east of the Rova. The thematic maps produced represent fundamental land use management tools to be used in Geo Disaster Risk Reduction (GDRR) by scientists, practitioners and the decision-makers involved in the High City protection and conservation. The study conducted represents an important contribution for improving the knowledge on landslide processes in an area with limited data such as Madagascar, and may be reproduced in cultural heritage sites characterized by similar geomorphological and urban scenarios. [ABSTRACT FROM AUTHOR]

A new approach is proposed to evaluate the stability of a finite c-φ soil slope before the single row of piles based on the Bishop method, which considers the influence of the landslide's push force. The proposed model allows the analysis of the ultimate resistance of an isolate stabilizing pile below the slip surface via the displacement-based method. This approach takes into account the gradient of the slope, horizontal distance between the pile and the crest of the slope, and the pile spacing. The effectiveness of the proposed method is demonstrated by comparing the measured results and the calculated values by other methods. The comparison results show that the proposed approach is suitable for calculating the ultimate resistance of the pile below the slip surface under the finite slope condition. Moreover, through comparing the analysis results of the stability of the slope stabilization using the piles under the different conditions, it indicates that the gradient of the slope is the factor that has the greatest influence on the stability of the whole slope and the local stability of the finite soil slope in front of the pile. [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]

Coseismic landslides are common secondary earthquake geohazards in meizoseismal areas. Newmark sliding block permanent displacement method has been widely adopted to develop regional coseismic landslide hazard maps. However, uncertainties from the slope parameters (e.g., cohesion, pore water pressure, and block thickness) are not commonly considered in the ground displacement predictions. This study proposes a novel framework that consists of two uncertainty assessment methods of Monte Carlo and logic tree simulations (MCS and LTS) with seven different displacement regression functions to predict the regional coseismic landslides' permanent displacement. Compared with the existing methods, the proposed framework is argument-driven, avoiding huge number of repetitive simulations. The Jiuzhaigou earthquake, in China, is considered as an illustrative example to compare the performance of the framework with considered regression functions. The corresponding results show that using LTS, with a certain regression function, leads to better predictions compared to using MCS. It is demonstrated that the proposed framework can provide a meaningful measure for making informed decisions to diminish the potential risk of earthquake induced landslides, and/or generating emergency strategies to mitigate post-earthquake consequences. It should be noted that the application of the proposed method for deposits where the soil strength parameter values do not fit the normal distribution may be limited as only normal distribution for soil strengths is considered in this study. [ABSTRACT FROM AUTHOR]

The East Coast region of New Zealand has some of the highest erosion rates in the world, due to its proximity to an active plate boundary, susceptibility to high-intensity storms and steep terrain underlain by young soft sedimentary rock and soil. In the city of Gisborne, expansion of residential blocks into steeper terrain in peri-urban areas has required improved capacity for the characterisation and monitoring of slope stability. Landslides have affected several properties and have destroyed infrastructure. Slope failure commonly occurs during heavy rainfall events when slow-moving retrogressive slides transition into earthflows and mudflows. In this study, we used in situ sampling and testing methods combined with remote sensing techniques to provide an understanding of the pre-failure and post-failure behaviour of an urban landslide in Gisborne. High-resolution aerial imagery, unmanned aerial vehicle imagery and light detection and ranging data revealed slope morphology and contours of prehistoric failures in the area, and highlighted the more recent impacts of deforestation on slope stability. Furthermore, Sentinel-1 InSAR analysis determined that gradual deformation began in 2017, following two ex-tropical cyclone events. Deformation downslope continued until an initial failure in July 2020. Following that event, some parts of the slope proceeded to accelerate, leading to a further reactivation event in November 2021, following heavy rainfall. During this November 2021 event, average line of sight velocities ranged from −7.9 to −11.2 mm/year, and deformation rates in the vertical direction (related to rotational slumping) averaged −11.2 to −11.9 mm/year, consistent with field observations. [ABSTRACT FROM AUTHOR]

Landslide is regarded as one of the most prevalent and destroying geological hazards in natural terrain areas. Reliable landslide susceptibility analysis procedures are vital for policymakers to manage the regional-scale landslide risk. In the framework of physically based modelling analysis, the infinite slope model is commonly used to assess the surficial landslide susceptibility with deterministically defined geotechnical and geological parameters. This work aims to develop a user-friendly geographic information system (GIS) extension tool called the GIS-FORM landslide prediction toolbox using the Python programming language to consider the possible uncertainties in the physically based landslide susceptibility analysis in seismic areas. We implement the first-order reliability method (FORM) algorithm to calculate the probability of infinite slope failures. The proposed toolbox can produce some regional hazard distribution maps of different indexes, such as the factor of safety (FoS), reliability index (RI), and failure probability (Pf). Furthermore, the toolbox enables coseismic landslide displacement prediction using either the direct Newmark integration method and/or the empirical formula method. Outputs of the GIS-FORM landslide prediction analysis are verified using published data in the literature. Further, it is also successfully employed for landslide susceptibility analysis of the Ms 7.0 Jiuzhaigou earthquake in Sichuan Province, China. Without loss of generality, the GIS-FORM landslide prediction toolbox can serve for the rapid hazard mapping of earthquake-induced regional landslides where uncertainties in geological and geotechnical parameters should be considered. [ABSTRACT FROM AUTHOR]

It is well known that geological structure is one of the main predisposing factors that control landslide occurrence at regional scale. Along time, investigation of the role of structural and lithologic controls has raised the interest of specialists from various earth and planetary sciences who proposed different approaches that range from descriptive analysis to automatic detection. The aim of this study is to investigate the role of structural and lithological controls on landslide occurrence at regional scale. To achieve this, a collection of tools for calculating the TOBIA index and estimating the relationship between morphostructural and lithological conditions and landslide occurrences was developed using Python programming language. The relationship between geological structure and landslide occurrences was analyzed in three distinct landslide-prone sites in Romania, located in different geological and geomorphological contexts. The spatial distribution of the geological structure is modelled based on the TOBIA index (TOpographic/Bedding-plane Intersection Angle). The TOBIA index uses topographic slope, slope aspect, dip angle, and dip direction to divide the terrain into three categories named cataclinal, anaclinal, and orthoclinal, with each category having several subcategories. Additionally, by implementing the bivariate statistical analyses (Weight of Evidence), we investigated the relationship of landslide occurrences to the resulted morphostructural slopes, on the one hand, and to the lithological units, on the other hand. The output contrast values show an evident influence of the cataclinal slopes over landslide occurrences in homoclinal areas, with most of the scarps occurring at the contact between cataclinal and orthoclinal slopes. Fewer influences are found in areas where clay outcrops and where the geological structure has a much-diminished control over the presence of the landslides. These results contribute to a better understanding of the role of structural and lithologic controls on landslide occurrence at regional scale and furthermore could serve to a better assessment of landslide susceptibility. The results obtained on different geologic and geomorphic contexts in Romania suggest that this methodology can be applied successfully in other areas prone to landslides. For reproducibility purposes, the GIS set of tools was made available under the MIT license. [ABSTRACT FROM AUTHOR]

Earthquake-induced landslides represent a significant seismic hazard since they can largely increase the damage and losses due to a seismic event, an issue that must be considered in land-use and risk management purposes. However, it can be difficult to consider all the natural variables, such as geotechnical parameters, that predispose the occurrence of landslides under a specific dynamic triggering, especially for wide areas. Among these, the most important and critical ones to quantify at large scale, are represented by the hydraulic conditions in both unsaturated and saturated media. For this reason, in this work we present a newly developed GIS tool that was specifically designed for the automation of a pseudo-dynamic Newmark model to estimate the co-seismic displacements over wide areas. The tool takes into account reactivations of landslides under different rupture mechanisms and parametrically weighs the role of variable initial soil moisture or pressure head conditions, as well as the influence of ground shaking resulting from local amplification effects. The proposed tool was tested in the Molise region (central-southern Italy), where almost 23,000 existing landslides have been selected for evaluating potential reactivations. The obtained results point out the importance of local conditions on the displacement amount, even by considering a unique return period of the seismic action. Strengths and weaknesses of the proposed model have been also highlighted in view of potential future applications in the framework of co-seismic landslide risk assessment and mitigation measures. [ABSTRACT FROM AUTHOR]

Landslide run-out modeling is a powerful model-based decision support tool for landslide hazard assessment and mitigation. Most landslide run-out models contain parameters that cannot be directly measured but rely on back-analysis of past landslide events. As field data on past landslide events come with a certain measurement error, the community developed probabilistic calibration techniques. However, probabilistic parameter calibration of landslide run-out models is often hindered by high computational costs resulting from the long run time of a single simulation and the large number of required model runs. To address this computational challenge, this work proposes an efficient probabilistic parameter calibration method by integrating landslide run-out modeling, Bayesian inference, Gaussian process emulation, and active learning. Here, we present an extensive synthetic case study. The results show that our new method can reduce the number of necessary simulation runs from thousands to a few hundreds owing to Gaussian process emulation and active learning. It is therefore expected to advance the current practice of parameter calibration of landslide run-out models. [ABSTRACT FROM AUTHOR]

Landslides represent a serious worldwide hazard, especially in Italy, where exposure to hydrogeological risk is very high; for this reason, a landslide quantitative risk assessment (QRA) is crucial for risk management and for planning mitigation measures. In this study, we present and describe a novel methodological approach of QRA for slow-moving landslides, aiming at national replicability. This procedure has been applied at the basin scale in the Arno River basin (9100 km2, Central Italy), where most landslides are slow-moving. QRA is based on the application of the equation risk = hazard (H) × vulnerability (V) × exposure (E) and on the use of open data with uniform characteristics at the national scale. The study area was divided into a grid with a 1 km2 cell size, and for each cell, the parameters necessary for the risk assessment were calculated. The obtained results show that the total risk of the study area amounts to approximately 7 billion €. The proposed methodology presents several novelties in the risk assessment for the regional/national scale of the analysis, mainly concerning the identification of the datasets and the development of new methodologies that could be applicable over such large areas. The present work demonstrates the feasibility of the methodology and discusses the obtained results. [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]

Event-based landslide inventories are essential sources to broaden our understanding of the causal relationship between triggering events and the occurring landslides. Moreover, detailed inventories are crucial for the succeeding phases of landslide risk studies like susceptibility and hazard assessment. The openly available inventories differ in the quality and completeness levels. Event-based landslide inventories are created based on manual interpretation, and there can be significant differences in the mapping preferences among interpreters. To address this issue, we used two different datasets to analyze the potential of U-Net and machine learning approaches for automated landslide detection in the Himalayas. Dataset-1 is composed of five optical bands from the RapidEye satellite imagery. Dataset-2 is composed of the RapidEye optical data, and ALOS-PALSAR derived topographical data. We used a small dataset consisting of 239 samples acquired from several training zones and one testing zone to evaluate our models' performance using the fully convolutional U-Net model, Support Vector Machines (SVM), K-Nearest Neighbor, and the Random Forest (RF). We created thirty-two different maps to evaluate and understand the implications of different sample patch sizes and their effect on the accuracy of landslide detection in the study area. The results were then compared against the manually interpreted inventory compiled using fieldwork and visual interpretation of the RapidEye satellite image. We used accuracy assessment metrics such as F1-score, Precision, Recall, and Mathews Correlation Coefficient (MCC). In the context of the Nepali Himalayas, employing RapidEye images and machine learning models, a viable patch size was investigated. The U-Net model trained with 128 × 128 pixel patch size yields the best MCC results (76.59%) with the dataset-1. The added information from the digital elevation model benefited the overall detection of landslides. However, it does not improve the model's overall accuracy but helps differentiate human settlement areas and river sand bars. In this study, the U-Net achieved slightly better results than other machine learning approaches. Although it can depend on architecture of the U-Net model and the complexity of the geographical features in the imagery, the U-Net model is still preliminary in the domain of landslide detection. There is very little literature available related to the use of U-Net for landslide detection. This study is one of the first efforts of using U-Net for landslide detection in the Himalayas. Nevertheless, U-Net has the potential to improve further automated landslide detection in the future for varied topographical and geomorphological scenes. [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]

In landslide susceptibility studies, the type of mapping unit adopted affects the obtained models and maps in terms of accuracy, robustness, spatial resolution and geomorphological adequacy. To evaluate the optimal selection of these units, a test has been carried out in an important catchment of northern Sicily (the Imera River basin), where the spatial relationships between a set of predictors and an inventory of 1608 rotational/translational landslides were analysed using the multivariate adaptive regression splines (MARS) method. In particular, landslide susceptibility models were prepared and compared by adopting four different types of mapping units: the largely adopted grid cells (PX), the typical contributing area–controlled slope units (5000_SLU), the recently optimized parameter-free multiscale slope units (PF_SLU) and a new type (LCL_SLU) of slope unit obtained by crossing classic hydrological partitioning with landform classification. At the same time, once a pixel-based model was prepared, four different SLU modelling strategies were applied to each of the obtained slope unit layers, including two different types of pixel score zoning, a pixel score re-modelling and a factor-based SLU re-modelling. According to the achieved results, LCL_SLUs produced the highest performance and reliability, offering an optimal compromise between the high-performing but scattered and the smoothed but lower-performing prediction images that were obtained from pixel-based and hydrologic SLU–based modelling, respectively. Additionally, among the four adopted SLU modelling strategies, the new proposed procedure, which uses the zoned pixel–based score deciles as the LCL_SLU predictors for a new regression, resulted in the best outstanding performance (ROC_AUC = 0.95). [ABSTRACT FROM AUTHOR]

The reliable landslide hazard assessment entails a robust understanding of frequency-magnitude analysis of the landslide inventory. Previous studies proposed that the landslide frequency-size distribution follows a power-law distribution even though 75–90% of data deviated from the power-law fit. This deviation from the power-law fit was ubiquitous in various landslide studies irrespective of their triggering factor, spatial and temporal resolution of the landslide inventory and geological and morphological settings of the area. This study conducted a detailed frequency-size distribution of the landslides at four different locations in India's Himalayan regions to check the validity of power law to represent the landslide data. The landslide inventory of Kathua, Shimla, Pithoragarh and Darjeeling regions are complied, consisting of 1942, 905, 2151 and 393 landslide events, respectively. The landslide frequency distribution is tested for the power-law, exponential and lognormal distribution. After a detailed statistical analysis, our finding suggests that medium-large landslide size events follow a power-law distribution, accounting for 8–25% of the total data; the rest deviates significantly from the power-law fit and follows a lognormal distribution. The cut-off between the lognormal and power-law distributions, or the minimum size for medium-large landslides, is 102.9–104.3 m2. The Kolmogorov–Smirnov (KS) and Lilliefors tests are used to validate the findings. This study provides insight into the landslide size probability distribution in different locations situated in India's Himalayan regions. [ABSTRACT FROM AUTHOR]

She worked as the leader of the World Centres on Excellence on Landslide Risk Reduction from 2017 to 2020 "Landslide integrated research for disaster risk reduction" as well as the World Centres of Excellence (WCoE) 2020-2023 "Integrated research on landslide disaster risk" in conjunction to IPL-208. In his career, he has held various roles: (i) contractor of National Agencies, (ii) representative of institutional entities and organizations at international level, (iii) active member of scientific commissions and associations, (iv) affiliate of scientific councils, (v) co-ordinator of research projects, (vi) Principal Investigator of the International Research Project in collaboration with the Italian and USA Space Agencies (ASI and NASA) and the Jet Propulsion Laboratory of Pasadena (JPL), (vii) co-ordinator of Scientific Committees for civil protection programs against geo-hydrological hazard, and (viii) Leader for Europe of the UNESCO International Collaboration Program IGCP-425 "Landslide hazard assessment and cultural heritage." At the occasion of each triennial World Landslide Forum, ICL-IPL Awards and Certificates are conferred to individuals and organizations that have contributed to the International Consortium on Landslides (ICL) and the International Programme on Landslides (IPL) since the last World Landslide Forum. I am nominating Paolo Canuti for the Varnes medal for his outstanding contribution to landslide research testified by the numerous projects and publications, for the constant and significant commitment in the education of new generations of scientists involved in landslide hazard research, and for his deep contribution to the birth of the International Consortium on landslides including the management of the consortium through his presidency from 2008 to 2014. [Extracted from the article]

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]